From c492a2c839541d1ebbb583837ba2298a99ffc7d0 Mon Sep 17 00:00:00 2001 From: kargaranamir Date: Thu, 25 Aug 2022 18:09:35 +0300 Subject: [PATCH 01/23] feat(metric, segmentation): star: add segmentation and grid metrics --- backend/aim/metrics/interfaces.py | 4 +- backend/aim/metrics/m1/m1_png_file_size.py | 4 +- backend/aim/metrics/m2/m2_jpeg_file_size.py | 4 +- backend/aim/metrics/m21/__init__.py | 0 .../aim/metrics/m21/m21_num_visual_blocks.py | 126 +++ backend/aim/metrics/m22/__init__.py | 0 .../metrics/m22/m22_num_alignment_points.py | 191 ++++ backend/aim/metrics/m23/__init__.py | 0 .../aim/metrics/m23/m23_num_block_sizes.py | 245 +++++ backend/aim/metrics/m24/__init__.py | 0 backend/aim/metrics/m24/m24_gui_coverage.py | 190 ++++ backend/aim/metrics/m25/__init__.py | 0 .../m25/m25_num_vertical_block_sizes.py | 151 +++ backend/aim/metrics/m26/__init__.py | 0 backend/aim/metrics/m26/m26_white_space.py | 120 +++ .../aim/metrics/m3/m3_distinct_rgb_values.py | 4 +- backend/aim/metrics/m4/m4_contour_density.py | 4 +- .../metrics/m5/m5_figure_ground_contrast.py | 4 +- .../aim/metrics/m6/m6_contour_congestion.py | 4 +- backend/aim/metrics/m7/m7_subband_entropy.py | 4 +- .../aim/metrics/m8/m8_feature_congestion.py | 11 +- backend/aim/metrics/m9/m9_umsi.py | 4 +- backend/aim/segmentation/LICENSE | 201 ++++ backend/aim/segmentation/Readme.md | 0 backend/aim/segmentation/__init__.py | 0 backend/aim/segmentation/image_seg_utils.py | 985 ++++++++++++++++++ backend/aim/segmentation/merge_seg_utils.py | 303 ++++++ backend/aim/segmentation/model.py | 444 ++++++++ backend/aim/segmentation/text_seg_utils.py | 106 ++ .../seg_0_interfacemetrics_aalto.png | Bin 0 -> 187685 bytes .../expected_results/seg_0_uied_mobile.png | Bin 0 -> 181695 bytes .../tests/expected_results/seg_0_white.png | Bin 0 -> 4219 bytes .../input_values/interfacemetrics_aalto.png | Bin 0 -> 214911 bytes .../data/tests/input_values/uied_mobile.png | Bin 0 -> 187355 bytes backend/tests/metrics/test_m21.py | 82 ++ backend/tests/metrics/test_m22.py | 79 ++ backend/tests/metrics/test_m23.py | 82 ++ backend/tests/metrics/test_m24.py | 82 ++ backend/tests/metrics/test_m25.py | 82 ++ backend/tests/metrics/test_m26.py | 84 ++ backend/tests/segmentation/__init__.py | 0 .../tests/segmentation/test_segmentation.py | 113 ++ metrics.json | 457 ++++++++ 43 files changed, 4156 insertions(+), 14 deletions(-) create mode 100644 backend/aim/metrics/m21/__init__.py create mode 100644 backend/aim/metrics/m21/m21_num_visual_blocks.py create mode 100644 backend/aim/metrics/m22/__init__.py create mode 100644 backend/aim/metrics/m22/m22_num_alignment_points.py create mode 100644 backend/aim/metrics/m23/__init__.py create mode 100644 backend/aim/metrics/m23/m23_num_block_sizes.py create mode 100644 backend/aim/metrics/m24/__init__.py create mode 100644 backend/aim/metrics/m24/m24_gui_coverage.py create mode 100644 backend/aim/metrics/m25/__init__.py create mode 100644 backend/aim/metrics/m25/m25_num_vertical_block_sizes.py create mode 100644 backend/aim/metrics/m26/__init__.py create mode 100644 backend/aim/metrics/m26/m26_white_space.py create mode 100644 backend/aim/segmentation/LICENSE create mode 100644 backend/aim/segmentation/Readme.md create mode 100644 backend/aim/segmentation/__init__.py create mode 100644 backend/aim/segmentation/image_seg_utils.py create mode 100644 backend/aim/segmentation/merge_seg_utils.py create mode 100644 backend/aim/segmentation/model.py create mode 100644 backend/aim/segmentation/text_seg_utils.py create mode 100644 backend/data/tests/expected_results/seg_0_interfacemetrics_aalto.png create mode 100644 backend/data/tests/expected_results/seg_0_uied_mobile.png create mode 100644 backend/data/tests/expected_results/seg_0_white.png create mode 100644 backend/data/tests/input_values/interfacemetrics_aalto.png create mode 100644 backend/data/tests/input_values/uied_mobile.png create mode 100644 backend/tests/metrics/test_m21.py create mode 100644 backend/tests/metrics/test_m22.py create mode 100644 backend/tests/metrics/test_m23.py create mode 100644 backend/tests/metrics/test_m24.py create mode 100644 backend/tests/metrics/test_m25.py create mode 100644 backend/tests/metrics/test_m26.py create mode 100644 backend/tests/segmentation/__init__.py create mode 100644 backend/tests/segmentation/test_segmentation.py diff --git a/backend/aim/metrics/interfaces.py b/backend/aim/metrics/interfaces.py index 36b5245..cbd7e7e 100644 --- a/backend/aim/metrics/interfaces.py +++ b/backend/aim/metrics/interfaces.py @@ -12,7 +12,7 @@ # Standard library modules import abc -from typing import Any, List, Optional, Union +from typing import Any, Dict, List, Optional, Union # Third-party modules from pydantic import HttpUrl @@ -51,6 +51,7 @@ def execute_metric( self, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -61,6 +62,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m1/m1_png_file_size.py b/backend/aim/metrics/m1/m1_png_file_size.py index 09ff7d4..7d17508 100644 --- a/backend/aim/metrics/m1/m1_png_file_size.py +++ b/backend/aim/metrics/m1/m1_png_file_size.py @@ -51,7 +51,7 @@ # ---------------------------------------------------------------------------- # Standard library modules -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union # Third-party modules from pydantic import HttpUrl @@ -85,6 +85,7 @@ class Metric(AIMMetricInterface): def execute_metric( gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -95,6 +96,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m2/m2_jpeg_file_size.py b/backend/aim/metrics/m2/m2_jpeg_file_size.py index ef7ef02..226bfe3 100644 --- a/backend/aim/metrics/m2/m2_jpeg_file_size.py +++ b/backend/aim/metrics/m2/m2_jpeg_file_size.py @@ -61,7 +61,7 @@ # ---------------------------------------------------------------------------- # Standard library modules -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union # Third-party modules from pydantic import HttpUrl @@ -100,6 +100,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -110,6 +111,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m21/__init__.py b/backend/aim/metrics/m21/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m21/m21_num_visual_blocks.py b/backend/aim/metrics/m21/m21_num_visual_blocks.py new file mode 100644 index 0000000..b9d43f2 --- /dev/null +++ b/backend/aim/metrics/m21/m21_num_visual_blocks.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + Number of Visual Blocks + + +Description: + The number of visual GUI blocks. + The code considers each GUI element as a visual block. This metric is + calculated in two ways, by considering all blocks or only parent blocks. + Parent blocks are those whose area is on top of another block area (child). + + Category: Grid Quality > Number of Visual Blocks. + For details, see G1 [1]. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface + Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human + Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. + doi: https://doi.org/10.1145/2702123.2702575 + + 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. + In Digital publishing (Vol. 6076, pp. 52-63). SPIE. + doi: https://doi.org/10.1117/12.642120 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Any, Dict, List, Optional, Union + +# Third-party modules +from pydantic import HttpUrl + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: Number of Visual Blocks. + """ + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - Number of Visual Blocks (int, [0, +inf)) + """ + + # Based on [1] this metric should not apply for mobile GUIs. + if gui_type == GUI_TYPE_MOBILE: + raise ValueError( + "This Metric requires gui_type to be non mobile (e.g, desktop)" + ) + + # Get all elements + if gui_segments is not None: + segments: List = gui_segments["segments"] + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + # Get elements without considering children: Filter elements that do not have 'parent' as a key + segments_without_children: List = list( + filter(lambda element: not element.get("parent", None), segments) + ) + + # Count number of all elements + num_vis_blocks_with_children: int = int(len(segments)) + # Count number of elements without considering children + num_vis_blocks_without_children: int = int( + len(segments_without_children) + ) + + return [num_vis_blocks_with_children, num_vis_blocks_without_children] diff --git a/backend/aim/metrics/m22/__init__.py b/backend/aim/metrics/m22/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m22/m22_num_alignment_points.py b/backend/aim/metrics/m22/m22_num_alignment_points.py new file mode 100644 index 0000000..b02b889 --- /dev/null +++ b/backend/aim/metrics/m22/m22_num_alignment_points.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + Number of Alignment Points of Blocks + + +Description: + The number of alignment points of GUI blocks. This metric is + calculated in two ways, by considering all blocks or only parent blocks. + Parent blocks are those whose area is on top of another block area (child). + Moreover, due to segmentation errors, this code considers only points with + tolerance distances from each other. + + Category: Grid Quality > Number of Alignment Points of Blocks. + For details, see G2 [1]. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface + Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human + Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. + doi: https://doi.org/10.1145/2702123.2702575 + + 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. + In Digital publishing (Vol. 6076, pp. 52-63). SPIE. + doi: https://doi.org/10.1117/12.642120 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +from pydantic import HttpUrl + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: Number of Alignment Points of Blocks. + """ + + _PIXEL_TOLERANCE_ROW: int = 0 # 0 or positive + _PIXEL_TOLERANCE_COLUMN: int = 0 # 0 or positive + + @classmethod + def count_points(cls, points: List[int], axis: int = 0) -> int: + """ + Count number of points with tolerance distance from each other. + + Args: + points: List of Alignment points + axis: 0 for rows and 1 for columns, default 0 + + Returns: + number of points with tolerance + """ + + if axis == 1: + tolerance = cls._PIXEL_TOLERANCE_COLUMN + else: + tolerance = cls._PIXEL_TOLERANCE_ROW + + # Sort alignment points + points = sorted(points) + # Alignment points with at least tolerance + 1 distance are added + points_tol: List[int] = [points[0]] + for p in points: + if p - points_tol[-1] > tolerance: + points_tol.append(p) + + # Count number of points + n_points: int = len(points_tol) + return n_points + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - Number of Alignment Points of Blocks (int, [0, +inf)) + """ + + # Based on [1] this metric should not apply for mobile GUIs. + if gui_type == GUI_TYPE_MOBILE: + raise ValueError( + "This Metric requires gui_type to be non mobile (e.g, desktop)" + ) + + # Get all elements + if gui_segments is not None: + segments: List = gui_segments["segments"] + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + # Compute alignment points based on column and rows + columns: List = [] + rows: List = [] + columns_wo_children: List = [] + rows_wo_children: List = [] + for element in segments: + position = element["position"] + columns = columns + [ + position["column_min"], + position["column_max"], + ] + rows = rows + [position["row_min"], position["row_max"]] + # W/O considering children + if not element.get("parent", None): + columns_wo_children = columns_wo_children + [ + position["column_min"], + position["column_max"], + ] + rows_wo_children = rows_wo_children + [ + position["row_min"], + position["row_max"], + ] + + # Compute number of alignment point for row W and W/O children + num_align_points_row: int = cls.count_points(points=rows, axis=0) + num_align_points_row_wo_children = cls.count_points( + points=rows_wo_children, axis=0 + ) + + # Compute number of alignment point for column W and W/O children + num_align_points_column = cls.count_points(points=columns, axis=1) + num_align_points_column_wo_children = cls.count_points( + points=columns_wo_children, axis=1 + ) + + # Compute number of unique alignment points + num_align_points: int = num_align_points_row + num_align_points_column + # Compute number of unique alignment points W/O considering children + num_align_points_wo_children = ( + num_align_points_row_wo_children + + num_align_points_column_wo_children + ) + + return [num_align_points, num_align_points_wo_children] diff --git a/backend/aim/metrics/m23/__init__.py b/backend/aim/metrics/m23/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m23/m23_num_block_sizes.py b/backend/aim/metrics/m23/m23_num_block_sizes.py new file mode 100644 index 0000000..725564e --- /dev/null +++ b/backend/aim/metrics/m23/m23_num_block_sizes.py @@ -0,0 +1,245 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + Number of Block Sizes + + +Description: + The number of block sizes, grid proportionality. This metric is + calculated in two ways, by considering all blocks or only parent blocks. + Parent blocks are those whose area is on top of another block area (child). + This code also supports tolerance because of segmentation errors. + + Category: Grid Quality > Number of Block Sizes. + For details, see G3 [1]. + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface + Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human + Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. + doi: https://doi.org/10.1145/2702123.2702575 + + 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. + In Digital publishing (Vol. 6076, pp. 52-63). SPIE. + doi: https://doi.org/10.1117/12.642120 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import numpy as np +from pydantic import HttpUrl +from scipy.sparse.csgraph import connected_components + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: Number of Block Sizes. + """ + + _PIXEL_TOLERANCE_WIDTH: int = 0 # 0 or positive + _PIXEL_TOLERANCE_HEIGHT: int = 0 # 0 or positive + + @staticmethod + def block_size_relation( + segments: List, pixel_tolerance_width, pixel_tolerance_height + ) -> Tuple[np.ndarray, np.ndarray, List[int], List[int]]: + """ + Execute the metric. + + Args: + segments: List of GUI elements + pixel_tolerance_width: width pixel tolerance threshold + pixel_tolerance_height: height pixel tolerance threshold + + Returns: + Relation of elements (blocks) sizes W children (np.ndarray) + Relation of elements (blocks) sizes W/O children (np.ndarray) + List of ids of elements in relation matrix (blocks) W children (List) + List of ids of elements in relation matrix W/O children (List) + """ + + # Compute block sizes + block_size_total: List[List[Tuple[int, int]]] = [] + block_size_total_without_children: List[List[Tuple[int, int]]] = [] + block_ids: List[int] = [] + block_ids_without_children: List[int] = [] + + # For each of element we also add the shape with tolerances. + for element in segments: + + # Store different shapes with tolerances of each shape + block_shapes: List[Tuple[int, int]] = [] + block_shapes_without_children: List[Tuple[int, int]] = [] + + # Save order of id of elements + block_ids.append(element["id"]) + # W/O considering children + if not element.get("parent", None): + block_ids_without_children.append(element["id"]) + + for w_tol in range( + pixel_tolerance_width, + -1 * pixel_tolerance_width - 1, + -1, + ): + for h_tol in range( + pixel_tolerance_height, + -1 * pixel_tolerance_height - 1, + -1, + ): + shape = ( + element["width"] + w_tol, + element["height"] + h_tol, + ) + block_shapes.append(shape) + # W/O considering children + if not element.get("parent", None): + block_shapes_without_children.append(shape) + + block_size_total.append(block_shapes) + # if element is not a child + if len(block_shapes_without_children) > 0: + block_size_total_without_children.append( + block_shapes_without_children + ) + + # Find relation between shapes, if they are the same. + blocks_total_len: int = len(block_size_total) + blocks_relation: np.ndarray = np.zeros( + (blocks_total_len, blocks_total_len), dtype=int + ) + for i_block in range(0, blocks_total_len): + for j_block in range(0, blocks_total_len): + if j_block > i_block: + if bool( + set(block_size_total[i_block]) + & set(block_size_total[j_block]) + ): + blocks_relation[i_block, j_block] = 1 + blocks_relation[j_block, i_block] = 1 + + # Find relation between shapes, if they are the same (without children) + blocks_total_without_children_len: int = len( + block_size_total_without_children + ) + blocks_relation_without_children: np.ndarray = np.zeros( + ( + blocks_total_without_children_len, + blocks_total_without_children_len, + ), + dtype=int, + ) + for i_block in range(0, blocks_total_without_children_len): + for j_block in range(0, blocks_total_without_children_len): + if j_block > i_block: + if bool( + set(block_size_total_without_children[i_block]) + & set(block_size_total_without_children[j_block]) + ): + blocks_relation_without_children[i_block, j_block] = 1 + blocks_relation_without_children[j_block, i_block] = 1 + + return ( + blocks_relation, + blocks_relation_without_children, + block_ids, + block_ids_without_children, + ) + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - Number of Block Sizes (int, [0, +inf)) + """ + + # Get all elements + if gui_segments is not None: + segments: List = gui_segments["segments"] + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + # Number of Unique block sizes + num_block_size: int = 0 + num_block_size_without_children: int = 0 + + if len(segments) != 0: + + # Compute block size relation + blocks_relation: np.ndarray + blocks_relation_without_children: np.ndarray + ( + blocks_relation, + blocks_relation_without_children, + _, + _, + ) = cls.block_size_relation( + segments, + pixel_tolerance_width=cls._PIXEL_TOLERANCE_WIDTH, + pixel_tolerance_height=cls._PIXEL_TOLERANCE_HEIGHT, + ) + + # Compute number of different block sizes the GUI have + num_block_size = connected_components(blocks_relation)[0] + num_block_size_without_children = connected_components( + blocks_relation_without_children + )[0] + + return [num_block_size, num_block_size_without_children] diff --git a/backend/aim/metrics/m24/__init__.py b/backend/aim/metrics/m24/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m24/m24_gui_coverage.py b/backend/aim/metrics/m24/m24_gui_coverage.py new file mode 100644 index 0000000..a74b0f1 --- /dev/null +++ b/backend/aim/metrics/m24/m24_gui_coverage.py @@ -0,0 +1,190 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + GUI Coverage + + +Description: + The proportion of GUI covered by same-size blocks (the cell coverage computation from [2]). + This code computes the number of gui blocks with same size. It does not consider blocks with + just one size appearance. + + Category: Grid Quality > GUI Coverage. + For details, see G4 [1]. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface + Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human + Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. + doi: https://doi.org/10.1145/2702123.2702575 + + 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. + In Digital publishing (Vol. 6076, pp. 52-63). SPIE. + doi: https://doi.org/10.1117/12.642120 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import numpy as np +from pydantic import HttpUrl +from scipy.sparse.csgraph import connected_components + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface +from aim.metrics.m23.m23_num_block_sizes import Metric as m23 + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: GUI Coverage. + """ + + _PIXEL_TOLERANCE_WIDTH: int = 2 # 0 or positive + _PIXEL_TOLERANCE_HEIGHT: int = 2 # 0 or positive + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - GUI Coverage (float, [0, 1]) + """ + # Get all elements + if gui_segments is not None: + segments: List = gui_segments["segments"] + height, width, _ = gui_segments["img_shape"] + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + # GUI coverage percent with the same size blocks + gui_coverage: float = 0.0 + gui_coverage_without_children: float = 0.0 + + if len(segments) != 0: + # Compute block size relation + blocks_relation: np.ndarray + blocks_relation_without_children: np.ndarray + ( + blocks_relation, + blocks_relation_without_children, + block_ids, + block_ids_without_children, + ) = m23.block_size_relation( + segments, + pixel_tolerance_width=cls._PIXEL_TOLERANCE_WIDTH, + pixel_tolerance_height=cls._PIXEL_TOLERANCE_HEIGHT, + ) + + # Coverage array + coverage_array = np.zeros((height, width), dtype=int) + coverage_array_without_children = np.zeros( + (height, width), dtype=int + ) + + # Find blocks with no same size pair (reverse: find block with at least one same size pair) + mask_blocks_zeroes = np.all(blocks_relation == 0, axis=1) + mask_block_without_children_zeroes = np.all( + blocks_relation_without_children == 0, axis=1 + ) + + # Compute coverage array + for mask_index in range(0, len(mask_blocks_zeroes)): + if not mask_blocks_zeroes[mask_index]: + ele = list( + filter( + ( + lambda element: block_ids[mask_index] + == element["id"] + ), + segments, + ) + )[0] + position = ele["position"] + coverage_array[ + position["row_min"] : position["row_max"], + position["column_min"] : position["column_max"], + ] = 1 + + # Compute coverage array (without children) + for mask_index in range( + 0, len(mask_block_without_children_zeroes) + ): + if not mask_block_without_children_zeroes[mask_index]: + ele = list( + filter( + ( + lambda element: block_ids_without_children[ + mask_index + ] + == element["id"] + ), + segments, + ) + )[0] + position = ele["position"] + coverage_array_without_children[ + position["row_min"] : position["row_max"], + position["column_min"] : position["column_max"], + ] = 1 + + # Compute percentage + gui_coverage = float(np.mean(coverage_array)) + gui_coverage_without_children = float( + np.mean(coverage_array_without_children) + ) + + return [gui_coverage, gui_coverage_without_children] diff --git a/backend/aim/metrics/m25/__init__.py b/backend/aim/metrics/m25/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m25/m25_num_vertical_block_sizes.py b/backend/aim/metrics/m25/m25_num_vertical_block_sizes.py new file mode 100644 index 0000000..484df1d --- /dev/null +++ b/backend/aim/metrics/m25/m25_num_vertical_block_sizes.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + Number of Vertical Block Sizes + + +Description: + The number of vertical block sizes, i.e., vertical grid + proportionality. This code computes the number of different + heights for blocks in the GUI. + + Category: Grid Quality > Number of Vertical Block Sizes. + For details, see G5 [1]. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface + Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human + Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. + doi: https://doi.org/10.1145/2702123.2702575 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Any, Dict, List, Optional, Union + +# Third-party modules +import numpy as np +from pydantic import HttpUrl + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: Number of Vertical Block Sizes. + """ + + _PIXEL_TOLERANCE_VERTICAL: int = 0 # 0 or positive + + @classmethod + def count_vertical_sizes(cls, vertical_sizes: List[int]) -> int: + """ + Count number of vertical sizes with tolerance distance from each other. + + Args: + vertical_sizes: List of Alignment points + + Returns: + number of vertical sizes with tolerance + """ + + # Sort vertical sizes + vertical_sizes = sorted(vertical_sizes) + # Vertical sizes with at least tolerance + 1 distance are added + vertical_sizes_tol: List[int] = [vertical_sizes[0]] + for p in vertical_sizes: + if p - vertical_sizes_tol[-1] > cls._PIXEL_TOLERANCE_VERTICAL: + vertical_sizes_tol.append(p) + + # Count number of vertical sizes + n_vertical_sizes: int = len(vertical_sizes_tol) + return n_vertical_sizes + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - Number of Vertical Block Sizes (int, [0, +inf)) + """ + + # Get all elements + if gui_segments is not None: + segments: List = gui_segments["segments"] + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + # Number of Unique vertical sizes + num_vertical_sizes: int = 0 + num_vertical_sizes_without_children: int = 0 + + # Compute vertical sizes (height of elements) + if len(segments) != 0: + vertical_sizes: List[int] = [] + vertical_sizes_without_children: List[int] = [] + for element in segments: + height = element["height"] + vertical_sizes.append(height) + # W/O considering children + if not element.get("parent", None): + vertical_sizes_without_children.append(height) + + # Compute unique vertical sizes + num_vertical_sizes = cls.count_vertical_sizes(vertical_sizes) + num_vertical_sizes_without_children = cls.count_vertical_sizes( + vertical_sizes_without_children + ) + + return [num_vertical_sizes, num_vertical_sizes_without_children] diff --git a/backend/aim/metrics/m26/__init__.py b/backend/aim/metrics/m26/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m26/m26_white_space.py b/backend/aim/metrics/m26/m26_white_space.py new file mode 100644 index 0000000..b43aec7 --- /dev/null +++ b/backend/aim/metrics/m26/m26_white_space.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + White Space + + +Description: + White Space. Non covered portion of GUI with visual blocks. + + Category: White Space. + For details, see [1]. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface + Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human + Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. + doi: https://doi.org/10.1145/2702123.2702575 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Any, Dict, List, Optional, Union + +# Third-party modules +import numpy as np +from pydantic import HttpUrl + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: White Space. + """ + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - White Space (int, [0, +inf)) + """ + + # Based on [1] this metric should not apply for mobile GUIs. + if gui_type == GUI_TYPE_MOBILE: + raise ValueError( + "This Metric requires gui_type to be non mobile (e.g, desktop)" + ) + + if gui_segments is not None: + height, width, _ = gui_segments["img_shape"] + segments: List = gui_segments["segments"] + img_binary = np.ones((height, width), dtype=int) + + for element in segments: + position = element["position"] + img_binary[ + position["row_min"] : position["row_max"], + position["column_min"] : position["column_max"], + ] = 0 + + white_percentage: float = float(np.mean(img_binary)) + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + return [ + white_percentage, + ] diff --git a/backend/aim/metrics/m3/m3_distinct_rgb_values.py b/backend/aim/metrics/m3/m3_distinct_rgb_values.py index 62ce334..f5a9a98 100644 --- a/backend/aim/metrics/m3/m3_distinct_rgb_values.py +++ b/backend/aim/metrics/m3/m3_distinct_rgb_values.py @@ -54,7 +54,7 @@ # Standard library modules import base64 from io import BytesIO -from typing import List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union # Third-party modules from PIL import Image @@ -94,6 +94,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -104,6 +105,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m4/m4_contour_density.py b/backend/aim/metrics/m4/m4_contour_density.py index f928e91..10d4bc6 100644 --- a/backend/aim/metrics/m4/m4_contour_density.py +++ b/backend/aim/metrics/m4/m4_contour_density.py @@ -56,7 +56,7 @@ # Standard library modules import base64 from io import BytesIO -from typing import List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union # Third-party modules import cv2 @@ -104,6 +104,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -114,6 +115,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m5/m5_figure_ground_contrast.py b/backend/aim/metrics/m5/m5_figure_ground_contrast.py index 5422678..cb2a95e 100644 --- a/backend/aim/metrics/m5/m5_figure_ground_contrast.py +++ b/backend/aim/metrics/m5/m5_figure_ground_contrast.py @@ -67,7 +67,7 @@ # Standard library modules import base64 from io import BytesIO -from typing import List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union # Third-party modules import cv2 @@ -111,6 +111,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -121,6 +122,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m6/m6_contour_congestion.py b/backend/aim/metrics/m6/m6_contour_congestion.py index 729f74a..8cb9b0f 100644 --- a/backend/aim/metrics/m6/m6_contour_congestion.py +++ b/backend/aim/metrics/m6/m6_contour_congestion.py @@ -66,7 +66,7 @@ # Standard library modules import base64 from io import BytesIO -from typing import Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union # Third-party modules import numpy as np @@ -441,6 +441,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -451,6 +452,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m7/m7_subband_entropy.py b/backend/aim/metrics/m7/m7_subband_entropy.py index 3039b21..fe56fe9 100644 --- a/backend/aim/metrics/m7/m7_subband_entropy.py +++ b/backend/aim/metrics/m7/m7_subband_entropy.py @@ -57,7 +57,7 @@ # Standard library modules import base64 from io import BytesIO -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union # Third-party modules import numpy as np @@ -133,6 +133,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -143,6 +144,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/metrics/m8/m8_feature_congestion.py b/backend/aim/metrics/m8/m8_feature_congestion.py index 4e189c4..b65434c 100644 --- a/backend/aim/metrics/m8/m8_feature_congestion.py +++ b/backend/aim/metrics/m8/m8_feature_congestion.py @@ -53,7 +53,6 @@ * Initial implementation """ - # ---------------------------------------------------------------------------- # Imports # ---------------------------------------------------------------------------- @@ -61,7 +60,7 @@ # Standard library modules import base64 from io import BytesIO -from typing import Dict, List, Optional, Union +from typing import Any, Dict, List, Optional, Union # Third-party modules import numpy as np @@ -123,7 +122,7 @@ class Metric(AIMMetricInterface): ) _ORIENT_NOISE: float = 0.001 # Was eps, but that gave too much orientation noise in the saliency maps. Then changed to 0.000001 and then changed to 0.001. - ## OPP_ENERGY constants: + # OPP_ENERGY constants: # These probably seem like arbitrary numbers, but it's just trying to get # three very different feature extraction methods to operate at basically # the same scales. @@ -131,7 +130,7 @@ class Metric(AIMMetricInterface): _OPP_ENERGY_FILTER_SCALE: float = 16 / 14 * 1.75 _OPP_ENERGY_POOL_SCALE: float = 1.75 - # Clutter coeficients to compute final feature congestion map + # Clutter coefficients to compute final feature congestion map _COLOR_COEF: float = 0.2088 _CONTRAST_COEF: float = 0.0660 _ORIENT_COEF: float = 0.0269 @@ -496,6 +495,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -506,6 +506,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: @@ -557,7 +558,7 @@ def execute_metric( ** (1 / cls._MINKOWSKI_ORDER) ) # element wise - # Normlize output image + # Normalize output image clutter_array_fc: np.ndarray = normlize(clutter_map_fc) clutter_im_fc: Image.Image = Image.fromarray(clutter_array_fc) clutter_b64_fc: str = image_utils.to_png_image_base64(clutter_im_fc) diff --git a/backend/aim/metrics/m9/m9_umsi.py b/backend/aim/metrics/m9/m9_umsi.py index df6030d..9ab9d52 100644 --- a/backend/aim/metrics/m9/m9_umsi.py +++ b/backend/aim/metrics/m9/m9_umsi.py @@ -43,7 +43,7 @@ import sys import warnings from io import BytesIO -from typing import Any, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union # Third-party modules import cv2 @@ -381,6 +381,7 @@ def execute_metric( cls, gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, gui_url: Optional[HttpUrl] = None, ) -> Optional[List[Union[int, float, str]]]: """ @@ -391,6 +392,7 @@ def execute_metric( Kwargs: gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) gui_url: GUI URL (defaults to None) Returns: diff --git a/backend/aim/segmentation/LICENSE b/backend/aim/segmentation/LICENSE new file mode 100644 index 0000000..638aa1c --- /dev/null +++ b/backend/aim/segmentation/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] [UIED mulong.xie@anu.edu.au] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/backend/aim/segmentation/Readme.md b/backend/aim/segmentation/Readme.md new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/segmentation/__init__.py b/backend/aim/segmentation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/segmentation/image_seg_utils.py b/backend/aim/segmentation/image_seg_utils.py new file mode 100644 index 0000000..6981757 --- /dev/null +++ b/backend/aim/segmentation/image_seg_utils.py @@ -0,0 +1,985 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Image Segmentation utility functions. +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Dict, List, Optional, Tuple, Union + +# Third-party modules +import cv2 +import numpy as np + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2021-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Image Segmentation utility functions. +# ---------------------------------------------------------------------------- + + +def resize_by_longest_edge(org_im, resize_length): + height, width = org_im.shape[:2] + w_h_ratio = width / height + + if height > width: + resize_w = resize_length * w_h_ratio + resize_img = cv2.resize(org_im, (int(resize_w), int(resize_length))) + return resize_img + else: + resize_h = resize_length / w_h_ratio + resize_img = cv2.resize(org_im, (int(resize_length), int(resize_h))) + return resize_img + + +def gray_to_gradient(img): + if len(img.shape) == 3: + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + img_f = np.copy(img) + img_f = img_f.astype("float") + + kernel_h = np.array([[0, 0, 0], [0, -1.0, 1.0], [0, 0, 0]]) + kernel_v = np.array([[0, 0, 0], [0, -1.0, 0], [0, 1.0, 0]]) + dst1 = abs(cv2.filter2D(img_f, -1, kernel_h)) + dst2 = abs(cv2.filter2D(img_f, -1, kernel_v)) + gradient = (dst1 + dst2).astype("uint8") + return gradient + + +def binarization(org, grad_min, show=False, wait_key=0): + grey = cv2.cvtColor(org, cv2.COLOR_BGR2GRAY) + grad = gray_to_gradient(grey) # get RoI with high gradient + rec, binary = cv2.threshold( + grad, grad_min, 255, cv2.THRESH_BINARY + ) # enhance the RoI + morph = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, (3, 3)) # remove noises + if show: + cv2.imshow("Binary", morph) + if wait_key is not None: + cv2.waitKey(wait_key) + return morph + + +# Draw +def draw_bounding_box( + org, + components, + color=(0, 255, 0), + line=2, + show=False, + write_path=None, + name="board", + is_return=False, + wait_key=0, +): + """ + Draw bounding box of components on the original image + :param wait_key: + :param is_return: + :param name: + :param write_path: + :param org: original image + :param components: bbox [(column_min, row_min, column_max, row_max)] + -> top_left: (column_min, row_min) + -> bottom_right: (column_max, row_max) + :param color: line color + :param line: line thickness + :param show: show or not + :return: labeled image + """ + if not show and write_path is None and not is_return: + return + board = org.copy() + for compo in components: + bbox = compo.put_bbox() + board = cv2.rectangle( + board, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, line + ) + if show: + cv2.imshow(name, board) + if wait_key is not None: + cv2.waitKey(wait_key) + if wait_key == 0: + cv2.destroyWindow(name) + if write_path is not None: + cv2.imwrite(write_path, board) + return board + + +def draw_boundary(components, shape, show=False): + """ + Draw boundary of objects on the black withe + :param components: boundary: [top, bottom, left, right] + -> up, bottom: (column_index, min/max row border) + -> left, right: (row_index, min/max column border) detect range of each row + :param shape: shape or original image + :param show: show or not + :return: drawn board + """ + board = np.zeros(shape[:2], dtype=np.uint8) # binary board + for component in components: + # up and bottom: (column_index, min/max row border) + for point in component.boundary[0] + component.boundary[1]: + board[point[1], point[0]] = 255 + # left, right: (row_index, min/max column border) + for point in component.boundary[2] + component.boundary[3]: + board[point[0], point[1]] = 255 + if show: + cv2.imshow("Rec", board) + cv2.waitKey(0) + return board + + +# File utils +def corners2json(compos, img_shape): + output = {"img_shape": img_shape, "segments": []} + + for compo in compos: + c = { + "id": compo.id, + "class": compo.category, + "subclass": compo.subcategory, + } + ( + c["column_min"], + c["row_min"], + c["column_max"], + c["row_max"], + ) = compo.put_bbox() + c["width"] = compo.width + c["height"] = compo.height + output["segments"].append(c) + return output + + +# Bbox +class Bbox: + def __init__(self, col_min, row_min, col_max, row_max): + self.col_min = col_min + self.row_min = row_min + self.col_max = col_max + self.row_max = row_max + + self.width = self.col_max - self.col_min + self.height = self.row_max - self.row_min + self.box_area = self.width * self.height + + def init_bound(self): + self.width = self.col_max - self.col_min + self.height = self.row_max - self.row_min + self.box_area = self.width * self.height + + def put_bbox(self): + return self.col_min, self.row_min, self.col_max, self.row_max + + def bbox_resize(self, resize_ratio): + self.col_min = int(self.col_min * resize_ratio) + self.row_min = int(self.row_min * resize_ratio) + self.col_max = int(self.col_max * resize_ratio) + self.row_max = int(self.row_max * resize_ratio) + self.init_bound() + return self + + def bbox_cal_area(self): + self.box_area = self.width * self.height + return self.box_area + + def bbox_relation(self, bbox_b): + """ + :return: -1 : a in b + 0 : a, b are not intersected + 1 : b in a + 2 : a, b are identical or intersected + """ + col_min_a, row_min_a, col_max_a, row_max_a = self.put_bbox() + col_min_b, row_min_b, col_max_b, row_max_b = bbox_b.put_bbox() + + # if a is in b + if ( + col_min_a > col_min_b + and row_min_a > row_min_b + and col_max_a < col_max_b + and row_max_a < row_max_b + ): + return -1 + # if b is in a + elif ( + col_min_a < col_min_b + and row_min_a < row_min_b + and col_max_a > col_max_b + and row_max_a > row_max_b + ): + return 1 + # a and b are non-intersect + elif (col_min_a > col_max_b or row_min_a > row_max_b) or ( + col_min_b > col_max_a or row_min_b > row_max_a + ): + return 0 + # intersection + else: + return 2 + + def bbox_relation_nms(self, bbox_b, bias=(0, 0)): + """ + Calculate the relation between two rectangles by nms + :return: -1 : a in b + 0 : a, b are not intersected + 1 : b in a + 2 : a, b are intersected + """ + col_min_a, row_min_a, col_max_a, row_max_a = self.put_bbox() + col_min_b, row_min_b, col_max_b, row_max_b = bbox_b.put_bbox() + + bias_col, bias_row = bias + # get the intersected area + col_min_s = max(col_min_a - bias_col, col_min_b - bias_col) + row_min_s = max(row_min_a - bias_row, row_min_b - bias_row) + col_max_s = min(col_max_a + bias_col, col_max_b + bias_col) + row_max_s = min(row_max_a + bias_row, row_max_b + bias_row) + w = np.maximum(0, col_max_s - col_min_s) + h = np.maximum(0, row_max_s - row_min_s) + inter = w * h + area_a = (col_max_a - col_min_a) * (row_max_a - row_min_a) + area_b = (col_max_b - col_min_b) * (row_max_b - row_min_b) + iou = inter / (area_a + area_b - inter) + ioa = inter / self.box_area + iob = inter / bbox_b.box_area + + if iou == 0 and ioa == 0 and iob == 0: + return 0 + + # contained by b + if ioa >= 1: + return -1 + # contains b + if iob >= 1: + return 1 + # not intersected with each other + # intersected + if iou >= 0.02 or iob > 0.2 or ioa > 0.2: + return 2 + # if iou == 0: + # print('ioa:%.5f; iob:%.5f; iou:%.5f' % (ioa, iob, iou)) + return 0 + + def bbox_cvt_relative_position(self, col_min_base, row_min_base): + """ + Convert to relative position based on base coordinator + """ + self.col_min += col_min_base + self.col_max += col_min_base + self.row_min += row_min_base + self.row_max += row_min_base + + def bbox_merge(self, bbox_b): + """ + Merge two intersected bboxes + """ + col_min_a, row_min_a, col_max_a, row_max_a = self.put_bbox() + col_min_b, row_min_b, col_max_b, row_max_b = bbox_b.put_bbox() + col_min = min(col_min_a, col_min_b) + col_max = max(col_max_a, col_max_b) + row_min = min(row_min_a, row_min_b) + row_max = max(row_max_a, row_max_b) + new_bbox = Bbox(col_min, row_min, col_max, row_max) + return new_bbox + + def bbox_padding(self, image_shape, pad): + row, col = image_shape[:2] + self.col_min = max(self.col_min - pad, 0) + self.col_max = min(self.col_max + pad, col) + self.row_min = max(self.row_min - pad, 0) + self.row_max = min(self.row_max + pad, row) + + +# Component +def cvt_compos_relative_pos(compos, col_min_base, row_min_base): + for compo in compos: + compo.compo_relative_position(col_min_base, row_min_base) + + +def compos_containment(compos): + for i in range(len(compos) - 1): + for j in range(i + 1, len(compos)): + relation = compos[i].compo_relation(compos[j]) + if relation == -1: + compos[j].contain.append(i) + if relation == 1: + compos[i].contain.append(j) + return compos + + +def compos_update(compos, org_shape): + for i, compo in enumerate(compos): + # start from 1, id 0 is background + compo.compo_update(i + 1, org_shape) + + return compos + + +class Component: + def __init__(self, region, image_shape): + self.id = None + self.region = region + self.boundary = self.compo_get_boundary() + self.bbox = self.compo_get_bbox() + self.bbox_area = self.bbox.box_area + + self.region_area = len(region) + self.width = len(self.boundary[0]) + self.height = len(self.boundary[2]) + self.image_shape = image_shape + self.area = self.width * self.height + + self.category = "Component" + self.subcategory = None + self.contain = [] + + self.rect_ = None + self.line_ = None + self.redundant = False + + def compo_update(self, id, org_shape): + self.id = id + self.image_shape = org_shape + self.width = self.bbox.width + self.height = self.bbox.height + self.bbox_area = self.bbox.box_area + self.area = self.width * self.height + + def put_bbox(self): + return self.bbox.put_bbox() + + def resize(self, resize_ratio): + self.bbox.bbox_resize(resize_ratio) + self.compo_update(self.id, self.image_shape) + return self + + def compo_update_bbox_area(self): + self.bbox_area = self.bbox.bbox_cal_area() + + def compo_get_boundary(self): + """ + get the bounding boundary of an object(region) + boundary: [top, bottom, left, right] + -> up, bottom: (column_index, min/max row border) + -> left, right: (row_index, min/max column border) detect range of each row + """ + border_up, border_bottom, border_left, border_right = {}, {}, {}, {} + for point in self.region: + # point: (row_index, column_index) + # up, bottom: (column_index, min/max row border) detect range of each column + if point[1] not in border_up or border_up[point[1]] > point[0]: + border_up[point[1]] = point[0] + if ( + point[1] not in border_bottom + or border_bottom[point[1]] < point[0] + ): + border_bottom[point[1]] = point[0] + # left, right: (row_index, min/max column border) detect range of each row + if point[0] not in border_left or border_left[point[0]] > point[1]: + border_left[point[0]] = point[1] + if ( + point[0] not in border_right + or border_right[point[0]] < point[1] + ): + border_right[point[0]] = point[1] + + boundary = [border_up, border_bottom, border_left, border_right] + # descending sort + for i in range(len(boundary)): + boundary[i] = [[k, boundary[i][k]] for k in boundary[i].keys()] + boundary[i] = sorted(boundary[i], key=lambda x: x[0]) + return boundary + + def compo_get_bbox(self): + """ + Get the top left and bottom right points of boundary + :return: corners: [(top_left, bottom_right)] + -> top_left: (column_min, row_min) + -> bottom_right: (column_max, row_max) + """ + + col_min, row_min = ( + int(min(self.boundary[0][0][0], self.boundary[1][-1][0])), + int(min(self.boundary[2][0][0], self.boundary[3][-1][0])), + ) + col_max, row_max = ( + int(max(self.boundary[0][0][0], self.boundary[1][-1][0])), + int(max(self.boundary[2][0][0], self.boundary[3][-1][0])), + ) + bbox = Bbox(col_min, row_min, col_max, row_max) + return bbox + + def compo_is_rectangle(self, min_rec_evenness, max_dent_ratio, test=False): + """ + detect if an object is rectangle by evenness and dent of each border + """ + dent_direction = [1, -1, 1, -1] # direction for convex + + flat = 0 + parameter = 0 + for n, border in enumerate(self.boundary): + parameter += len(border) + # dent detection + pit = 0 # length of pit + depth = 0 # the degree of surface changing + if n <= 1: + adj_side = max( + len(self.boundary[2]), len(self.boundary[3]) + ) # get maximum length of adjacent side + else: + adj_side = max(len(self.boundary[0]), len(self.boundary[1])) + + # -> up, bottom: (column_index, min/max row border) + # -> left, right: (row_index, min/max column border) detect range of each row + abnm = 0 + for i in range(int(3 + len(border) * 0.02), len(border) - 1): + # calculate gradient + difference = border[i][1] - border[i + 1][1] + # the degree of surface changing + depth += difference + # ignore noise at the start of each direction + if ( + i / len(border) < 0.08 + and (dent_direction[n] * difference) / adj_side > 0.5 + ): + depth = 0 # reset + + # print(border[i][1], i / len(border), depth, (dent_direction[n] * difference) / adj_side) + # if the change of the surface is too large, count it as part of abnormal change + if abs(depth) / adj_side > 0.3: + abnm += 1 # count the size of the abnm + # if the abnm is too big, the shape should not be a rectangle + if abnm / len(border) > 0.1: + if test: + print("abnms", abnm, abnm / len(border)) + draw_boundary([self], self.image_shape, show=True) + self.rect_ = False + return False + continue + else: + # reset the abnm if the depth back to normal + abnm = 0 + + # if sunken and the surface changing is large, then counted as pit + if ( + dent_direction[n] * depth < 0 + and abs(depth) / adj_side > 0.15 + ): + pit += 1 + continue + + # if the surface is not changing to a pit and the gradient is zero, then count it as flat + if abs(depth) < 1 + adj_side * 0.015: + flat += 1 + if test: + print(depth, adj_side, flat) + # if the pit is too big, the shape should not be a rectangle + if pit / len(border) > max_dent_ratio: + if test: + print("pit", pit, pit / len(border)) + draw_boundary([self], self.image_shape, show=True) + self.rect_ = False + return False + if test: + print(flat / parameter, "\n") + draw_boundary([self], self.image_shape, show=True) + # ignore text and irregular shape + if self.height / self.image_shape[0] > 0.3: + min_rec_evenness = 0.85 + if (flat / parameter) < min_rec_evenness: + self.rect_ = False + return False + self.rect_ = True + return True + + def compo_is_line(self, min_line_thickness): + """ + Check this object is line by checking its boundary + :param min_line_thickness: + :return: Boolean + """ + # horizontally + slim = 0 + for i in range(self.width): + if ( + abs(self.boundary[1][i][1] - self.boundary[0][i][1]) + <= min_line_thickness + ): + slim += 1 + if slim / len(self.boundary[0]) > 0.93: + self.line_ = True + return True + # vertically + slim = 0 + for i in range(self.height): + if ( + abs(self.boundary[2][i][1] - self.boundary[3][i][1]) + <= min_line_thickness + ): + slim += 1 + if slim / len(self.boundary[2]) > 0.93: + self.line_ = True + return True + self.line_ = False + return False + + def compo_relation(self, compo_b, bias=(0, 0)): + """ + :return: -1 : a in b + 0 : a, b are not intersected + 1 : b in a + 2 : a, b are identical or intersected + """ + return self.bbox.bbox_relation_nms(compo_b.bbox, bias) + + def compo_relative_position(self, col_min_base, row_min_base): + """ + Convert to relative position based on base coordinator + """ + self.bbox.bbox_cvt_relative_position(col_min_base, row_min_base) + + def compo_merge(self, compo_b): + self.bbox = self.bbox.bbox_merge(compo_b.bbox) + self.compo_update(self.id, self.image_shape) + + def compo_clipping(self, img, pad=0, show=False): + (column_min, row_min, column_max, row_max) = self.put_bbox() + column_min = max(column_min - pad, 0) + column_max = min(column_max + pad, img.shape[1]) + row_min = max(row_min - pad, 0) + row_max = min(row_max + pad, img.shape[0]) + clip = img[row_min:row_max, column_min:column_max] + if show: + cv2.imshow("Clipping", clip) + cv2.waitKey() + return clip + + +# Detection + + +def merge_intersected_compos(compos): + changed = True + while changed: + changed = False + temp_set = [] + for compo_a in compos: + merged = False + for compo_b in temp_set: + if compo_a.compo_relation(compo_b) == 2: + compo_b.compo_merge(compo_a) + merged = True + changed = True + break + if not merged: + temp_set.append(compo_a) + compos = temp_set.copy() + return compos + + +def rm_contained_compos_not_in_block(compos): + """ + remove all components contained by others that are not Block + """ + marked = np.full(len(compos), False) + for i in range(len(compos) - 1): + for j in range(i + 1, len(compos)): + relation = compos[i].compo_relation(compos[j]) + if relation == -1 and compos[j].category != "Block": + marked[i] = True + if relation == 1 and compos[i].category != "Block": + marked[j] = True + new_compos = [] + for i in range(len(marked)): + if not marked[i]: + new_compos.append(compos[i]) + return new_compos + + +def rm_line( + binary, max_line_thickness, min_line_length_ratio, show=False, wait_key=0 +): + def is_valid_line(line): + line_length = 0 + line_gap = 0 + for j in line: + if j > 0: + if line_gap > 5: + return False + line_length += 1 + line_gap = 0 + elif line_length > 0: + line_gap += 1 + if line_length / width > min_line_length_ratio: + return True + return False + + height, width = binary.shape[:2] + + start_row, end_row = -1, -1 + check_line = False + check_gap = False + for i, row in enumerate(binary): + # line_ratio = (sum(row) / 255) / width + # if line_ratio > 0.9: + if is_valid_line(row): + # new start: if it is checking a new line, mark this row as start + if not check_line: + start_row = i + check_line = True + else: + # end the line + if check_line: + # thin enough to be a line, then start checking gap + if i - start_row < max_line_thickness: + end_row = i + check_gap = True + else: + start_row, end_row = -1, -1 + check_line = False + # check gap + if check_gap and i - end_row > max_line_thickness: + binary[start_row:end_row] = 0 + start_row, end_row = -1, -1 + check_line = False + check_gap = False + + if (check_line and (height - start_row) < max_line_thickness) or check_gap: + binary[start_row:end_row] = 0 + + if show: + cv2.imshow("No-line binary", binary) + if wait_key is not None: + cv2.waitKey(wait_key) + cv2.destroyWindow("No-line binary") + + return binary + + +def compo_filter(compos, min_area, img_shape): + max_height = img_shape[0] * 0.8 + compos_new = [] + for compo in compos: + if compo.area < min_area: + continue + if compo.height > max_height: + continue + ratio_h = compo.width / compo.height + ratio_w = compo.height / compo.width + if ( + ratio_h > 50 + or ratio_w > 40 + or ( + min(compo.height, compo.width) < 8 + and max(ratio_h, ratio_w) > 10 + ) + ): + continue + compos_new.append(compo) + return compos_new + + +def is_block(clip, thread): + """ + Block is a rectangle border enclosing a group of compos (consider it as a wireframe) + Check if a compo is block by checking if the inner side of its border is blank + """ + side = 4 # scan 4 lines inner forward each border + # top border - scan top down + blank_count = 0 + for i in range(1, 5): + if sum(clip[side + i]) / 255 > thread * clip.shape[1]: + blank_count += 1 + if blank_count > 2: + return False + # left border - scan left to right + blank_count = 0 + for i in range(1, 5): + if sum(clip[:, side + i]) / 255 > thread * clip.shape[0]: + blank_count += 1 + if blank_count > 2: + return False + + side = -4 + # bottom border - scan bottom up + blank_count = 0 + for i in range(-1, -5, -1): + if sum(clip[side + i]) / 255 > thread * clip.shape[1]: + blank_count += 1 + if blank_count > 2: + return False + # right border - scan right to left + blank_count = 0 + for i in range(-1, -5, -1): + if sum(clip[:, side + i]) / 255 > thread * clip.shape[0]: + blank_count += 1 + if blank_count > 2: + return False + return True + + +def compo_block_recognition(binary, compos, block_side_length=0.15): + height, width = binary.shape + for compo in compos: + if ( + compo.height / height > block_side_length + and compo.width / width > block_side_length + ): + clip = compo.compo_clipping(binary) + if is_block(clip, thread=0.15): + compo.category = "Block" + return compos + + +def component_detection( + binary, + min_obj_area, + min_rec_evenness, + max_dent_ratio, + step_h=5, + step_v=2, + rec_detect=False, + show=False, + test=False, +): + """ + take the binary image as input. + calculate the connected regions -> get the bounding boundaries of them -> check if those regions are rectangles + return all boundaries and boundaries of rectangles + :param test: + :param show: + :param rec_detect: + :param step_v: + :param step_h: + :param binary: Binary image from pre-processing + :param min_obj_area: If not pass then ignore the small object + :param min_rec_evenness: If not pass then this object cannot be rectangular + :param max_dent_ratio: If not pass then this object cannot be rectangular + :return: boundary: [top, bottom, left, right] + -> up, bottom: list of (column_index, min/max row border) + -> left, right: list of (row_index, min/max column border) detect range of each row + """ + mask = np.zeros((binary.shape[0] + 2, binary.shape[1] + 2), dtype=np.uint8) + compos_all = [] + compos_rec = [] + compos_nonrec = [] + row, column = binary.shape[0], binary.shape[1] + for i in range(0, row, step_h): + for j in range(i % 2, column, step_v): + if binary[i, j] == 255 and mask[i, j] == 0: + # get connected area + # region = util.boundary_bfs_connected_area(binary, i, j, mask) + + mask_copy = mask.copy() + ff = cv2.floodFill( + binary, mask, (j, i), None, 0, 0, cv2.FLOODFILL_MASK_ONLY + ) + if ff[0] < min_obj_area: + continue + mask_copy = mask - mask_copy + region = np.reshape( + cv2.findNonZero(mask_copy[1:-1, 1:-1]), (-1, 2) + ) + region = [(p[1], p[0]) for p in region] + + # filter out some compos + component = Component(region, binary.shape) + # calculate the boundary of the connected area + # ignore small area + if component.width <= 3 or component.height <= 3: + continue + # check if it is line by checking the length of edges + # if component.compo_is_line(line_thickness): + # continue + + if test: + print("Area:%d" % (len(region))) + draw_boundary([component], binary.shape, show=True) + + compos_all.append(component) + + if rec_detect: + # rectangle check + if component.compo_is_rectangle( + min_rec_evenness, max_dent_ratio + ): + component.rect_ = True + compos_rec.append(component) + else: + component.rect_ = False + compos_nonrec.append(component) + + if show: + print("Area:%d" % (len(region))) + draw_boundary(compos_all, binary.shape, show=True) + + # draw_boundary(compos_all, binary.shape, show=True) + if rec_detect: + return compos_rec, compos_nonrec + else: + return compos_all + + +def nested_components_detection( + grey, + grad_thresh, + line_thickness, + min_rec_evenness, + max_dent_ratio, + step_h=10, + step_v=10, + show=False, +): + """ + :param grad_thresh: + :param show: + :param step_h: + :param step_v: + :param line_thickness: + :param max_dent_ratio: + :param min_rec_evenness: + :param grey: grey-scale of original image + :return: corners: list of [(top_left, bottom_right)] + -> top_left: (column_min, row_min) + -> bottom_right: (column_max, row_max) + """ + compos = [] + mask = np.zeros((grey.shape[0] + 2, grey.shape[1] + 2), dtype=np.uint8) + broad = np.zeros((grey.shape[0], grey.shape[1], 3), dtype=np.uint8) + broad_all = broad.copy() + + row, column = grey.shape[0], grey.shape[1] + for x in range(0, row, step_h): + for y in range(0, column, step_v): + if mask[x, y] == 0: + # region = flood_fill_bfs(grey, x, y, mask) + + # flood fill algorithm to get background (layout block) + mask_copy = mask.copy() + ff = cv2.floodFill( + grey, + mask, + (y, x), + None, + grad_thresh, + grad_thresh, + cv2.FLOODFILL_MASK_ONLY, + ) + # ignore small regions + if ff[0] < 500: + continue + mask_copy = mask - mask_copy + region = np.reshape( + cv2.findNonZero(mask_copy[1:-1, 1:-1]), (-1, 2) + ) + region = [(p[1], p[0]) for p in region] + + compo = Component(region, grey.shape) + + if compo.height < 30: + continue + + if compo.area / (row * column) > 0.9: + continue + elif compo.area / (row * column) > 0.7: + compo.redundant = True + + # get the boundary of this region + # ignore lines + if compo.compo_is_line(line_thickness): + continue + # ignore non-rectangle as blocks must be rectangular + if not compo.compo_is_rectangle( + min_rec_evenness, max_dent_ratio + ): + continue + # if block.height/row < min_block_height_ratio: + # continue + compos.append(compo) + if show: + cv2.imshow("Flood-fill all", broad_all) + cv2.imshow("Block", broad) + cv2.waitKey() + return compos + + +def nesting_inspection( + grey, + compos, + ffl_block, + line_thickness, + min_rec_evenness, + max_dent_ratio, + show=False, +): + """ + Inspect all big compos through block division by flood-fill + :param show: + :param max_dent_ratio: + :param min_rec_evenness: + :param line_thickness: + :param grey: + :param compos: + :param ffl_block: gradient threshold for flood-fill + :return: nesting compos + """ + nesting_compos = [] + for i, compo in enumerate(compos): + if compo.height > 50: + replace = False + clip_grey = compo.compo_clipping(grey) + n_compos = nested_components_detection( + clip_grey, + grad_thresh=ffl_block, + line_thickness=line_thickness, + min_rec_evenness=min_rec_evenness, + max_dent_ratio=max_dent_ratio, + show=show, + ) + cvt_compos_relative_pos( + n_compos, compo.bbox.col_min, compo.bbox.row_min + ) + + for n_compo in n_compos: + if n_compo.redundant: + compos[i] = n_compo + replace = True + break + if not replace: + nesting_compos += n_compos + return nesting_compos + + +def transform_img(image: np.ndarray, image_shape) -> np.ndarray: + """ + Transform function, resizes input images to fit in the network input size + + Args: + image: input image + image_shape: shape of resize + + Returns: + resized and normalized array of image + """ + image_resized: np.ndarray = cv2.resize(image, image_shape[:2]) + image_resized_normalized: np.ndarray = (image_resized / 255).astype( + "float32" + ) + image_resized_normalized = np.array([image_resized_normalized]) + return image_resized_normalized diff --git a/backend/aim/segmentation/merge_seg_utils.py b/backend/aim/segmentation/merge_seg_utils.py new file mode 100644 index 0000000..598bf0f --- /dev/null +++ b/backend/aim/segmentation/merge_seg_utils.py @@ -0,0 +1,303 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Merge (Image and Text) Segmentation utility functions. +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Dict, List, Optional, Tuple, Union + +# Third-party modules +import cv2 +import numpy as np + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2021-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Merge (Image and Text) Segmentation utility functions. +# ---------------------------------------------------------------------------- + + +class Element: + def __init__( + self, id, corner, category, subcategory=None, text_content=None + ): + self.id = id + self.category = category + self.subcategory = subcategory + self.col_min, self.row_min, self.col_max, self.row_max = corner + self.width = self.col_max - self.col_min + self.height = self.row_max - self.row_min + self.area = self.width * self.height + + self.text_content = text_content + self.parent_id = None + self.children = [] # list of elements + + def init_bound(self): + self.width = self.col_max - self.col_min + self.height = self.row_max - self.row_min + self.area = self.width * self.height + + def put_bbox(self): + return self.col_min, self.row_min, self.col_max, self.row_max + + def wrap_info(self): + info = { + "id": self.id, + "class": self.category, + "subclass": self.subcategory, + "height": self.height, + "width": self.width, + "position": { + "column_min": self.col_min, + "row_min": self.row_min, + "column_max": self.col_max, + "row_max": self.row_max, + }, + } + if self.text_content is not None: + info["text_content"] = self.text_content + if len(self.children) > 0: + info["children"] = [] + for child in self.children: + info["children"].append(child.id) + if self.parent_id is not None: + info["parent"] = self.parent_id + return info + + def resize(self, resize_ratio): + self.col_min = int(self.col_min * resize_ratio) + self.row_min = int(self.row_min * resize_ratio) + self.col_max = int(self.col_max * resize_ratio) + self.row_max = int(self.row_max * resize_ratio) + self.init_bound() + return self + + def element_merge( + self, element_b, new_element=False, new_category=None, new_id=None + ): + col_min_a, row_min_a, col_max_a, row_max_a = self.put_bbox() + col_min_b, row_min_b, col_max_b, row_max_b = element_b.put_bbox() + new_corner = ( + min(col_min_a, col_min_b), + min(row_min_a, row_min_b), + max(col_max_a, col_max_b), + max(row_max_a, row_max_b), + ) + if element_b.text_content is not None: + self.text_content = ( + element_b.text_content + if self.text_content is None + else self.text_content + "\n" + element_b.text_content + ) + if new_element: + return Element(new_id, new_corner, new_category) + else: + self.col_min, self.row_min, self.col_max, self.row_max = new_corner + self.init_bound() + + def calc_intersection_area(self, element_b, bias=(0, 0)): + a = self.put_bbox() + b = element_b.put_bbox() + col_min_s = max(a[0], b[0]) - bias[0] + row_min_s = max(a[1], b[1]) - bias[1] + col_max_s = min(a[2], b[2]) + row_max_s = min(a[3], b[3]) + w = np.maximum(0, col_max_s - col_min_s) + h = np.maximum(0, row_max_s - row_min_s) + inter = w * h + + iou = inter / (self.area + element_b.area - inter) + ioa = inter / self.area + iob = inter / element_b.area + + return inter, iou, ioa, iob + + def element_relation(self, element_b, bias=(0, 0)): + """ + @bias: (horizontal bias, vertical bias) + :return: -1 : a in b + 0 : a, b are not intersected + 1 : b in a + 2 : a, b are identical or intersected + """ + inter, iou, ioa, iob = self.calc_intersection_area(element_b, bias) + + # area of intersection is 0 + if ioa == 0: + return 0 + # a in b + if ioa >= 1: + return -1 + # b in a + if iob >= 1: + return 1 + return 2 + + def visualize_element(self, img, color, line, show=False): + loc = self.put_bbox() + cv2.rectangle(img, loc[:2], loc[2:], color, line) + if show: + cv2.imshow("Element", img) + cv2.waitKey(0) + cv2.destroyWindow("Element") + + +def show_elements( + org_img, eles, show=False, win_name="Element", line=2, write_path=None +): + color_map = { + "Text": (0, 0, 255), + "Component": (0, 255, 0), + "Block": (0, 255, 0), + "Text Content": (255, 0, 255), + } + img = org_img.copy() + for ele in eles: + color = color_map[ele.category] + ele.visualize_element(img, color, line) + + if show: + cv2.imshow(win_name, img) + cv2.waitKey(0) + cv2.destroyWindow(win_name) + + if write_path is not None: + cv2.imwrite(write_path, img) + return img + + +def save_elements(elements, img_shape): + components = {"segments": [], "img_shape": img_shape} + for i, ele in enumerate(elements): + c = ele.wrap_info() + components["segments"].append(c) + return components + + +def reassign_ids(elements): + for i, element in enumerate(elements): + element.id = i + + return elements + + +def refine_texts(texts, img_shape, max_height_ratio): + refined_texts = [] + for text in texts: + # remove potential noise + if ( + len(text.text_content) > 1 + and text.height / img_shape[0] < max_height_ratio + ): + refined_texts.append(text) + return refined_texts + + +def merge_text_line_to_paragraph(elements, max_line_gap): + texts = [] + non_texts = [] + for ele in elements: + if ele.category == "Text": + texts.append(ele) + else: + non_texts.append(ele) + + changed = True + while changed: + changed = False + temp_set = [] + for text_a in texts: + merged = False + for text_b in temp_set: + inter_area, _, _, _ = text_a.calc_intersection_area( + text_b, bias=(0, max_line_gap) + ) + if inter_area > 0: + text_b.element_merge(text_a) + merged = True + changed = True + break + if not merged: + temp_set.append(text_a) + texts = temp_set.copy() + return non_texts + texts + + +def refine_elements( + compos, texts, intersection_bias=(2, 2), containment_ratio=0.8 +): + """ + 1. remove compos contained in text + 2. remove compos containing text area that's too large + 3. store text in a compo if it's contained by the compo as the compo's text child element + """ + elements = [] + contained_texts = [] + for compo in compos: + is_valid = True + text_area = 0 + for text in texts: + inter, iou, ioa, iob = compo.calc_intersection_area( + text, bias=intersection_bias + ) + if inter > 0: + # the non-text is contained in the text compo + if ioa >= containment_ratio: + is_valid = False + break + text_area += inter + # the text is contained in the non-text compo + if iob >= containment_ratio and compo.category != "Block": + contained_texts.append(text) + if is_valid and text_area / compo.area < containment_ratio: + # for t in contained_texts: + # t.parent_id = compo.id + # compo.children += contained_texts + elements.append(compo) + + # elements += texts + for text in texts: + if text not in contained_texts: + elements.append(text) + return elements + + +def check_containment(elements, bias=(2, 2)): + new_elements = elements.copy() + for i in range(len(new_elements) - 1): + for j in range(i + 1, len(new_elements)): + relation = new_elements[i].element_relation(new_elements[j], bias) + if relation == -1: + new_elements[j].children.append(new_elements[i]) + new_elements[i].parent_id = new_elements[j].id + if relation == 1: + new_elements[i].children.append(new_elements[j]) + new_elements[j].parent_id = new_elements[i].id + + return new_elements + + +def remove_top_bar(elements, img_height): + new_elements: List[Element] = [] + max_height: float = img_height * 0.04 + for ele in elements: + if ele.row_min < 10 and ele.height < max_height: + continue + new_elements.append(ele) + return new_elements diff --git a/backend/aim/segmentation/model.py b/backend/aim/segmentation/model.py new file mode 100644 index 0000000..a06a6cb --- /dev/null +++ b/backend/aim/segmentation/model.py @@ -0,0 +1,444 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Model: + UI Segmentation and Element Detection + + +Description: + The UI Segmentation is mostly based on old-fashioned computer vision approaches to detect UI components and texts. + Most part of this code is imported from https://github.com/MulongXie/UIED. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Xie, M., Feng, S., Xing, Z., Chen, J., & Chen, C. (2020).UIED: a hybrid tool for GUI element detection. + In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium + on the Foundations of Software Engineering, pp. 1655-1659. ACM. + doi: https://doi.org/10.1145/3368089.3417940 + + 2. Chen, J., Xie, M., Xing, Z., Chen, C., Xu, X., Zhu, L., & Li, G. (2020). Object detection for graphical user + interface: Old fashioned or deep learning or a combination?. In proceedings of the 28th ACM joint meeting on + European Software Engineering Conference and Symposium on the Foundations of Software Engineering pp. 1202-1214. + ACM. + doi: https://doi.org/10.1145/3368089.3409691 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import base64 +import os +from io import BytesIO +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import cv2 +import numpy as np +from paddleocr import PaddleOCR +from PIL import Image +from pydantic import HttpUrl +from tensorflow.keras.models import load_model + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.segmentation.image_seg_utils import ( + Component, + binarization, + compo_block_recognition, + compo_filter, + component_detection, + compos_containment, + compos_update, + corners2json, + draw_bounding_box, + merge_intersected_compos, + nesting_inspection, + resize_by_longest_edge, + rm_contained_compos_not_in_block, + rm_line, + transform_img, +) +from aim.segmentation.merge_seg_utils import ( + Element, + check_containment, + merge_text_line_to_paragraph, + reassign_ids, + refine_elements, + refine_texts, + remove_top_bar, + save_elements, + show_elements, +) +from aim.segmentation.text_seg_utils import ( + Text, + text2json, + text_cvt_orc_format_paddle, + visualize_texts, +) + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Segmentation: + """ + UI Segmentation and Element Detection. + + Reference: Based on Xie et al.'s Python implementation available at https://github.com/MulongXie/UIED (see + LICENSE within the distribution). + """ + + # Private constants + _IS_SHOW: bool = False # show outputs + _IS_CLF: bool = False # use cnn classifier to determine type of components + + _KEY_PARAMS_DESKTOP: Dict = { + "min-grad": 10, + "ffl-block": 5, + "min-ele-area": 300, + "merge-contained-ele": True, + "merge-line-to-paragraph": True, + "remove-bar": False, + "threshold-rec-min-evenness": 0.7, + "threshold-rec-max-dent-ratio": 0.25, + "threshold-line-thickness": 8, + "threshold-line-min-length": 0.95, + "max-line-gap": 20, + "intersection-bias": 2, + "max-text-height-ratio": 0.3, + "resize": 800, + } + + _KEY_PARAMS_MOBILE: Dict = { + "min-grad": 5, + "ffl-block": 5, + "min-ele-area": 20, + "merge-contained-ele": True, + "merge-line-to-paragraph": True, + "remove-bar": False, + "threshold-rec-min-evenness": 0.7, + "threshold-rec-max-dent-ratio": 0.25, + "threshold-line-thickness": 8, + "threshold-line-min-length": 0.95, + "max-line-gap": 10, + "intersection-bias": 2, + "max-text-height-ratio": 0.3, + "resize": 800, + } + + # RICO CNN model: https://drive.google.com/file/d/1Gzpi-V_Sj7SSFQMNzy6bcgkEwaZBhGWS/view?usp=sharing + # Component detection based on CNN architecture trained on RICO Dataset. It can classify UI elements into the + # following classes: (Button, CheckBox, Chronometer, EditText, ImageButton, ImageView, ProgressBar, RadioButton, + # RatingBar, SeekBar, Spinner, Switch, ToggleButton, VideoView , TextView) + _CNN_MODEL_PATH: str = os.path.join( + os.path.dirname(__file__), "cnn-rico-1.h5" + ) + # Shape of input images + _CNN_INPUT_SHAPE: Tuple = (64, 64, 3) + # Model output classes + _CLASS_MAP: List[str] = [ + "Button", + "CheckBox", + "Chronometer", + "EditText", + "ImageButton", + "ImageView", + "ProgressBar", + "RadioButton", + "RatingBar", + "SeekBar", + "Spinner", + "Switch", + "ToggleButton", + "VideoView", + "TextView", + ] + if _IS_CLF: + _CNN_MODEL = load_model(_CNN_MODEL_PATH) + + # Private methods + @classmethod + def compo_detection( + cls, + img: np.ndarray, + img_resized: np.ndarray, + key_params: Dict, + classifier: bool = False, + show: bool = False, + ): + # *** Step 1 *** pre-processing: img -> get binary map + grey: np.ndarray = cv2.cvtColor(img_resized, cv2.COLOR_BGR2GRAY) + binary: np.ndarray = binarization( + img_resized, grad_min=int(key_params["min-grad"]), show=False + ) + + # *** Step 2 *** element detection + binary = rm_line( + binary, + max_line_thickness=key_params["threshold-line-thickness"], + min_line_length_ratio=key_params["threshold-line-min-length"], + show=False, + ) + uicompos: List[Component] = component_detection( + binary, + min_obj_area=int(key_params["min-ele-area"]), + min_rec_evenness=key_params["threshold-rec-min-evenness"], + max_dent_ratio=key_params["threshold-rec-max-dent-ratio"], + ) + + # *** Step 3 *** results refinement + uicompos = compo_filter( + uicompos, + min_area=int(key_params["min-ele-area"]), + img_shape=binary.shape, + ) + uicompos = merge_intersected_compos(uicompos) + uicompos = compo_block_recognition(binary, uicompos) + if key_params["merge-contained-ele"]: + uicompos = rm_contained_compos_not_in_block(uicompos) + uicompos = compos_update(uicompos, img_resized.shape) + uicompos = compos_containment(uicompos) + + # *** Step 4 ** nesting inspection: check if big compos have nesting element + uicompos += nesting_inspection( + grey, + uicompos, + ffl_block=key_params["ffl-block"], + line_thickness=key_params["threshold-line-thickness"], + min_rec_evenness=key_params["threshold-rec-min-evenness"], + max_dent_ratio=key_params["threshold-rec-max-dent-ratio"], + ) + uicompos = compos_update(uicompos, img_resized.shape) + + # *** Step 5 *** element classification: all category classification + if classifier: + for compo in uicompos: + im = compo.compo_clipping(img_resized) + im_transformed: np.ndarray = transform_img( + im, cls._CNN_INPUT_SHAPE + ) + compo.subcategory = cls._CLASS_MAP[ + np.argmax(cls._CNN_MODEL.predict(im_transformed)) + ] + + # *** Step 6 *** save detection result + uicompos = compos_update(uicompos, img_resized.shape) + # resize to default + uicompos = [ + uic.resize(img.shape[0] / img_resized.shape[0]) for uic in uicompos + ] + # save json + compo_json: Dict = corners2json(uicompos, img.shape) + draw_bounding_box( + img, uicompos, name="Components", show=show, write_path=None + ) + return compo_json + + @classmethod + def text_detection( + cls, + img: np.ndarray, + paddle_model: PaddleOCR = None, + show: bool = False, + ): + """ + :param img: + :param paddle_model: the preload paddle model for paddle ocr + :param show: + """ + # If you have issues with tools.infer see this issue: + # https://github.com/PaddlePaddle/PaddleOCR/issues/1024#issuecomment-1105946934 + if paddle_model is None: + paddle_model = PaddleOCR( + use_angle_cls=True, lang="ch", show_log=False + ) + paddle_result = paddle_model.ocr(img, cls=True) + texts: List[Text] = text_cvt_orc_format_paddle(paddle_result) + + visualize_texts( + img, texts, color=(0, 0, 255), line=2, write_path=None, show=show + ) + text_json: Dict = text2json(texts, img.shape) + return text_json + + @classmethod + def merge( + cls, + img: np.ndarray, + compo_json: Dict, + text_json: Dict, + key_params: Dict, + show: bool = False, + ): + # load text and non-text compo + ele_id: int = 0 + compos: List[Element] = [] + for compo in compo_json["segments"]: + element: Element = Element( + ele_id, + ( + compo["column_min"], + compo["row_min"], + compo["column_max"], + compo["row_max"], + ), + compo["class"], + compo["subclass"], + ) + compos.append(element) + ele_id += 1 + texts: List[Element] = [] + for text in text_json["texts"]: + element = Element( + ele_id, + ( + text["column_min"], + text["row_min"], + text["column_max"], + text["row_max"], + ), + "Text", + text_content=text["content"], + ) + texts.append(element) + ele_id += 1 + + # show resized detected elements + show_elements( + img, texts + compos, show=show, win_name="Elements Before Merging" + ) + + # refine elements + texts = refine_texts( + texts, + img.shape, + max_height_ratio=key_params["max-text-height-ratio"], + ) + elements: List[Element] = refine_elements( + compos, + texts, + intersection_bias=( + key_params["intersection-bias"], + key_params["intersection-bias"], + ), + containment_ratio=0.8, + ) + + if key_params["remove-bar"]: + elements = remove_top_bar(elements, img_height=img.shape[0]) + + if key_params["merge-line-to-paragraph"]: + elements = merge_text_line_to_paragraph( + elements, max_line_gap=key_params["max-line-gap"] + ) + elements = reassign_ids(elements) + elements = check_containment( + elements, + bias=( + key_params["intersection-bias"], + key_params["intersection-bias"], + ), + ) + + # save all merged elements, clips and blank background + components: Dict = save_elements(elements, img.shape) + board: np.ndarray = show_elements( + img, + elements, + show=show, + win_name="Elements After Merging", + write_path=None, + ) + return board, components + + @classmethod + def execute( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_url: Optional[HttpUrl] = None, + ) -> Tuple[str, Dict[str, Any]]: + # Create PIL image + img: Image.Image = Image.open(BytesIO(base64.b64decode(gui_image))) + + # Convert image from ??? (e.g., RGBA) to RGB color space + img_rgb: Image.Image = img.convert("RGB") + + # Get NumPy array + img_rgb_nparray: np.ndarray = np.array(img_rgb) + + # Convert NumPy array to Cv2 format (BGR) + img_cv: np.ndarray = cv2.cvtColor(img_rgb_nparray, cv2.COLOR_RGB2BGR) + + # Select Parameters + KEY_PARAMS: Dict = ( + cls._KEY_PARAMS_MOBILE + if gui_type == GUI_TYPE_MOBILE + else cls._KEY_PARAMS_DESKTOP + ) + + # Resize Input image + img_cv_resized: np.ndarray = resize_by_longest_edge( + img_cv, KEY_PARAMS["resize"] + ) + + # Show Original Image + if cls._IS_SHOW: + cv2.imshow("Original Image", img_cv) + cv2.waitKey(0) + cv2.destroyWindow("Original Image") + + # Text detection + text_json: Dict = cls.text_detection( + img_cv, paddle_model=None, show=cls._IS_SHOW + ) + + # Image detection + compo_json: Dict = cls.compo_detection( + img_cv, + img_cv_resized, + KEY_PARAMS, + classifier=cls._IS_CLF, + show=cls._IS_SHOW, + ) + + # Merge + components: Dict[str, Any] + board_bgr: np.ndarray + board_bgr, components = cls.merge( + img_cv, compo_json, text_json, KEY_PARAMS, show=cls._IS_SHOW + ) + + # Convert board to Pillow image format + board_rgb: np.ndarray = cv2.cvtColor(board_bgr, cv2.COLOR_BGR2RGB) + board_im: Image.Image = Image.fromarray(board_rgb) + # Convert board to b64 (str) + board_b64: str = image_utils.to_png_image_base64(board_im) + + return board_b64, components diff --git a/backend/aim/segmentation/text_seg_utils.py b/backend/aim/segmentation/text_seg_utils.py new file mode 100644 index 0000000..9fbb2b5 --- /dev/null +++ b/backend/aim/segmentation/text_seg_utils.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Text Segmentation utility functions. +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Dict, List, Optional, Tuple, Union + +# Third-party modules +import cv2 +import numpy as np + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2021-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Text Segmentation utility functions +# ---------------------------------------------------------------------------- + + +class Text: + def __init__(self, id, content, location): + self.id = id + self.content = content + self.location = location + + self.width = self.location["right"] - self.location["left"] + self.height = self.location["bottom"] - self.location["top"] + self.area = self.width * self.height + self.word_width = self.width / len(self.content) + + def visualize_element(self, img, color, line, show=False): + loc = self.location + cv2.rectangle( + img, + (loc["left"], loc["top"]), + (loc["right"], loc["bottom"]), + color, + line, + ) + if show: + cv2.imshow("text", img) + cv2.waitKey() + cv2.destroyWindow("text") + + +def visualize_texts( + org_img, texts, color=(0, 0, 255), line=2, show=False, write_path=None +): + img = org_img.copy() + for text in texts: + text.visualize_element(img, color=color, line=line) + + if show: + cv2.imshow("Texts", img) + cv2.waitKey(0) + cv2.destroyWindow("Texts") + + if write_path is not None: + cv2.imwrite(write_path, img) + + +def text2json(texts, img_shape): + output = {"img_shape": img_shape, "texts": []} + for text in texts: + c = {"id": text.id, "content": text.content} + loc = text.location + c["column_min"], c["row_min"], c["column_max"], c["row_max"] = ( + loc["left"], + loc["top"], + loc["right"], + loc["bottom"], + ) + c["width"] = text.width + c["height"] = text.height + output["texts"].append(c) + return output + + +def text_cvt_orc_format_paddle(paddle_result): + texts = [] + for i, line 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ze0wuue`mSho?g1LpHM`J>i?XY11OaJugmW8P_~qT14VL~e@iO`zrV9`Uw6X)Q2vh| f|G!tR6IOmv5j|4@HSsw&s3;4rV|2Gs%PH|cpD1YM literal 0 HcmV?d00001 diff --git a/backend/tests/metrics/test_m21.py b/backend/tests/metrics/test_m21.py new file mode 100644 index 0000000..30f8f7c --- /dev/null +++ b/backend/tests/metrics/test_m21.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Tests for the 'Number of Visual Blocks' metric (m21). +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.m21.m21_num_visual_blocks import Metric +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("interfacemetrics_aalto.png", [27, 21]), + ("white.png", [0, 0]), + ], +) +def test_num_visual_blocks_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test Number of Visual Blocks (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] + _, result_segments = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_DESKTOP, + ) + + # Test result + if result is not None and all(isinstance(res, int) for res in result): + assert result == expected_results diff --git a/backend/tests/metrics/test_m22.py b/backend/tests/metrics/test_m22.py new file mode 100644 index 0000000..435a7e2 --- /dev/null +++ b/backend/tests/metrics/test_m22.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Tests for the 'Number of Alignment Points of Blocks' metric (m22). +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.m22.m22_num_alignment_points import Metric +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [("interfacemetrics_aalto.png", [91, 71])], +) +def test_num_alignment_points_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test Number of Alignment Points of Blocks (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] + _, result_segments = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_DESKTOP, + ) + + # Test result + if result is not None and all(isinstance(res, int) for res in result): + assert result == expected_results diff --git a/backend/tests/metrics/test_m23.py b/backend/tests/metrics/test_m23.py new file mode 100644 index 0000000..01a8774 --- /dev/null +++ b/backend/tests/metrics/test_m23.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Tests for the 'Number of Block Sizes' metric (m23). +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.m23.m23_num_block_sizes import Metric +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("interfacemetrics_aalto.png", [26, 20]), + ("white.png", [0, 0]), + ], +) +def test_num_block_sizes_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test Number of Block Sizes (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] + _, result_segments = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_DESKTOP, + ) + + # Test result + if result is not None and all(isinstance(res, int) for res in result): + assert result == expected_results diff --git a/backend/tests/metrics/test_m24.py b/backend/tests/metrics/test_m24.py new file mode 100644 index 0000000..302d35a --- /dev/null +++ b/backend/tests/metrics/test_m24.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Tests for the 'GUI Coverage' metric (m24). +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.m24.m24_gui_coverage import Metric +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("interfacemetrics_aalto.png", [0.2086, 0.2086]), + ("white.png", [0.0, 0.0]), + ], +) +def test_gui_coverage_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test GUI Coverage (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] + _, result_segments = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_DESKTOP, + ) + + # Test result + if result is not None and all(isinstance(res, float) for res in result): + assert [round(float(res), 4) for res in result] == expected_results diff --git a/backend/tests/metrics/test_m25.py b/backend/tests/metrics/test_m25.py new file mode 100644 index 0000000..be4cee4 --- /dev/null +++ b/backend/tests/metrics/test_m25.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Tests for the 'Number of Vertical Block Sizes' metric (m25). +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.m25.m25_num_vertical_block_sizes import Metric +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("interfacemetrics_aalto.png", [18, 14]), + ("white.png", [0, 0]), + ], +) +def test_num_vertical_block_sizes_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test Number of Vertical Block Sizes (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] + _, result_segments = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_DESKTOP, + ) + + # Test result + if result is not None and all(isinstance(res, int) for res in result): + assert result == expected_results diff --git a/backend/tests/metrics/test_m26.py b/backend/tests/metrics/test_m26.py new file mode 100644 index 0000000..8396ff9 --- /dev/null +++ b/backend/tests/metrics/test_m26.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Tests for the 'White Space' metric (m26). +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.m26.m26_white_space import Metric +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("aalto.fi_website.png", [0.2663]), + ("myhelsinki.fi_website.png", [0.884]), + ("wikipedia.org_website.png", [0.3033]), + ("black.png", [1.0]), + ], +) +def test_white_space_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test White Space (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] + _, result_segments = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_DESKTOP, + ) + + # Test result + if result is not None and isinstance(result[0], float): + assert round(result[0], 4) == expected_results[0] diff --git a/backend/tests/segmentation/__init__.py b/backend/tests/segmentation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/tests/segmentation/test_segmentation.py b/backend/tests/segmentation/test_segmentation.py new file mode 100644 index 0000000..d65b604 --- /dev/null +++ b/backend/tests/segmentation/test_segmentation.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Tests for the 'Segmentation'. +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("interfacemetrics_aalto.png", ["", {}]), + ], +) +def test_segmentation_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test Segmentation (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (List of Segmentation results (str and Dict)) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute metric + result: Tuple[str, Dict[str, Any]] = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Test result + if ( + result is not None + and isinstance(result[0], str) + and isinstance(result[1], Dict) + ): + assert True + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("uied_mobile.png", ["", {}]), + ], +) +def test_segmentation_mobile( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test Segmentation (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (List of Segmentation results (str and Dict)) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute metric + result: Tuple[str, Dict[str, Any]] = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_MOBILE + ) + + # Test result + if ( + result is not None + and isinstance(result[0], str) + and isinstance(result[1], Dict) + ): + assert True diff --git a/metrics.json b/metrics.json index a681b7d..fe5d544 100644 --- a/metrics.json +++ b/metrics.json @@ -33,6 +33,7 @@ "evidence": 1, "relevance": 2, "speed": 2, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -87,6 +88,7 @@ "evidence": 2, "relevance": 3, "speed": 2, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -149,6 +151,7 @@ "evidence": 2, "relevance": 2, "speed": 2, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -203,6 +206,7 @@ "evidence": 4, "relevance": 3, "speed": 2, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -261,6 +265,7 @@ "evidence": 3, "relevance": 4, "speed": 2, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -327,6 +332,7 @@ "evidence": 3, "relevance": 3, "speed": 1, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -393,6 +399,7 @@ "evidence": 3, "relevance": 3, "speed": 1, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -451,6 +458,7 @@ "evidence": 3, "relevance": 3, "speed": 0, + "segmentation_required": false, "references": [ { "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", @@ -520,6 +528,7 @@ "evidence": 4, "relevance": 5, "speed": 1, + "segmentation_required": false, "references": [ { "title": "Fosco, C., Casser, V., Bedi, A.K., O'Donovan, P., Hertzmann, A., and Bylinskii, Z. (2020). Predicting Visual Importance Across Graphic Design Types. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST '20), pp. 249-260. ACM. doi: https://doi.org/10.1145/3379337.3415825", @@ -542,6 +551,454 @@ "description": false } ] + }, + "m21": { + "id": "m21", + "name": "Number of Visual Blocks", + "description": "The number of visual blocks of GUI, with and without considering children blocks.", + "evidence": 1, + "relevance": 1, + "speed": 2, + "segmentation_required": true, + "references": [ + { + "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", + "url": "https://doi.org/10.1145/2702123.2702575" + }, + { + "title": "Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. In Digital publishing (Vol. 6076, pp. 52-63). SPIE. doi: https://doi.org/10.1117/12.642120", + "url": "https://doi.org/10.1117/12.642120" + } + ], + "results": [ + { + "id": "m21_0", + "index": 0, + "type": "int", + "name": "Number of Visual Blocks", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + }, + { + "id": "m21_1", + "index": 1, + "type": "int", + "name": "Number of Visual Blocks (W/O Children)", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + } + ] + }, + "m22": { + "id": "m22", + "name": "Number of Alignment Points of Blocks", + "description": "The number of alignment points of GUI blocks, with and without considering children blocks.", + "evidence": 1, + "relevance": 1, + "speed": 2, + "segmentation_required": true, + "references": [ + { + "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", + "url": "https://doi.org/10.1145/2702123.2702575" + }, + { + "title": "Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. In Digital publishing (Vol. 6076, pp. 52-63). SPIE. doi: https://doi.org/10.1117/12.642120", + "url": "https://doi.org/10.1117/12.642120" + } + ], + "results": [ + { + "id": "m22_0", + "index": 0, + "type": "int", + "name": "Number of Alignment Points of Blocks", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + }, + { + "id": "m22_1", + "index": 1, + "type": "int", + "name": "Number of Alignment Points of Blocks (W/O Children)", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + } + ] + }, + "m23": { + "id": "m23", + "name": "Number of Block Sizes", + "description": "The number of block sizes, grid proportionality, with and without considering children blocks.", + "evidence": 1, + "relevance": 1, + "speed": 2, + "segmentation_required": true, + "references": [ + { + "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", + "url": "https://doi.org/10.1145/2702123.2702575" + }, + { + "title": "Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. In Digital publishing (Vol. 6076, pp. 52-63). SPIE. doi: https://doi.org/10.1117/12.642120", + "url": "https://doi.org/10.1117/12.642120" + } + ], + "results": [ + { + "id": "m23_0", + "index": 0, + "type": "int", + "name": "Number of Block Sizes", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + }, + { + "id": "m23_1", + "index": 1, + "type": "int", + "name": "Number of Block Sizes (W/O Children)", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + } + ] + }, + "m24": { + "id": "m24", + "name": "GUI Coverage", + "description": "The proportion of GUI covered by same-size blocks, with and without considering children blocks.", + "evidence": 1, + "relevance": 1, + "speed": 2, + "segmentation_required": true, + "references": [ + { + "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", + "url": "https://doi.org/10.1145/2702123.2702575" + }, + { + "title": "Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. In Digital publishing (Vol. 6076, pp. 52-63). SPIE. doi: https://doi.org/10.1117/12.642120", + "url": "https://doi.org/10.1117/12.642120" + } + ], + "results": [ + { + "id": "m24_0", + "index": 0, + "type": "float", + "name": "GUI Coverage", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 0.1], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [0.2, 0.6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [0.7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + }, + { + "id": "m24_1", + "index": 1, + "type": "float", + "name": "GUI Coverage (W/O Children)", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 0.1], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [0.2, 0.6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [0.7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + } + ] + }, + "m25": { + "id": "m25", + "name": "Number of Vertical Block Sizes", + "description": "The number of vertical block sizes, i.e., vertical grid proportionality., with and without considering children blocks.", + "evidence": 1, + "relevance": 1, + "speed": 2, + "segmentation_required": true, + "references": [ + { + "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", + "url": "https://doi.org/10.1145/2702123.2702575" + } + ], + "results": [ + { + "id": "m25_0", + "index": 0, + "type": "int", + "name": "Number of Different Blocks Height", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + }, + { + "id": "m25_1", + "index": 1, + "type": "int", + "name": "Number of Different Blocks Height (W/O Children)", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 2], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [3, 6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + } + ] + }, + "m26": { + "id": "m26", + "name": "White Space", + "description": "White Space. Non covered portion of GUI with visual blocks.", + "evidence": 1, + "relevance": 1, + "speed": 2, + "segmentation_required": true, + "references": [ + { + "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", + "url": "https://doi.org/10.1145/2702123.2702575" + } + ], + "results": [ + { + "id": "m26_0", + "index": 0, + "type": "float", + "name": "White Space", + "description": false, + "scores": [ + { + "id": "r1", + "range": [0, 0.1], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r2", + "range": [0.2, 0.6], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + }, + { + "id": "r3", + "range": [0.7, null], + "description": "Meaningless", + "icon": [null, null], + "judgment": "normal" + } + ] + } + ] } } } From e39b279f4a77b84636f28831a3470093f35f26a1 Mon Sep 17 00:00:00 2001 From: Amir Hossein Kargaran Date: Mon, 7 Nov 2022 19:53:27 +0100 Subject: [PATCH 02/23] feat(metric): add tests and refactor m21 and m22 --- backend/aim/metrics/m21/m21_grid_quality.py | 581 ++++++++++++++++++ .../aim/metrics/m21/m21_num_visual_blocks.py | 126 ---- .../metrics/m22/m22_num_alignment_points.py | 191 ------ .../m22_white_space.py} | 10 +- backend/aim/metrics/m23/__init__.py | 0 .../aim/metrics/m23/m23_num_block_sizes.py | 245 -------- backend/aim/metrics/m24/__init__.py | 0 backend/aim/metrics/m24/m24_gui_coverage.py | 190 ------ backend/aim/metrics/m25/__init__.py | 0 .../m25/m25_num_vertical_block_sizes.py | 151 ----- backend/aim/metrics/m26/__init__.py | 0 backend/aim/segmentation/text_seg_utils.py | 2 +- backend/data/tests/input_values/seg1.png | Bin 0 -> 27612 bytes backend/data/tests/input_values/seg2.png | Bin 0 -> 16498 bytes backend/data/tests/input_values/seg3.png | Bin 0 -> 20288 bytes backend/data/tests/input_values/seg4.png | Bin 0 -> 16447 bytes backend/tests/metrics/test_m21.py | 22 +- backend/tests/metrics/test_m22.py | 19 +- backend/tests/metrics/test_m23.py | 82 --- backend/tests/metrics/test_m24.py | 82 --- backend/tests/metrics/test_m25.py | 82 --- backend/tests/metrics/test_m26.py | 84 --- 22 files changed, 614 insertions(+), 1253 deletions(-) create mode 100644 backend/aim/metrics/m21/m21_grid_quality.py delete mode 100644 backend/aim/metrics/m21/m21_num_visual_blocks.py delete mode 100644 backend/aim/metrics/m22/m22_num_alignment_points.py rename backend/aim/metrics/{m26/m26_white_space.py => m22/m22_white_space.py} (93%) delete mode 100644 backend/aim/metrics/m23/__init__.py delete mode 100644 backend/aim/metrics/m23/m23_num_block_sizes.py delete mode 100644 backend/aim/metrics/m24/__init__.py delete mode 100644 backend/aim/metrics/m24/m24_gui_coverage.py delete mode 100644 backend/aim/metrics/m25/__init__.py delete mode 100644 backend/aim/metrics/m25/m25_num_vertical_block_sizes.py delete mode 100644 backend/aim/metrics/m26/__init__.py create mode 100644 backend/data/tests/input_values/seg1.png create mode 100644 backend/data/tests/input_values/seg2.png create mode 100644 backend/data/tests/input_values/seg3.png create mode 100644 backend/data/tests/input_values/seg4.png delete mode 100644 backend/tests/metrics/test_m23.py delete mode 100644 backend/tests/metrics/test_m24.py delete mode 100644 backend/tests/metrics/test_m25.py delete mode 100644 backend/tests/metrics/test_m26.py diff --git a/backend/aim/metrics/m21/m21_grid_quality.py b/backend/aim/metrics/m21/m21_grid_quality.py new file mode 100644 index 0000000..b266c91 --- /dev/null +++ b/backend/aim/metrics/m21/m21_grid_quality.py @@ -0,0 +1,581 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + Grid Quality + + +Description: + Grid quality measures describe the quality of GUI layout. The code considers + each GUI element as a visual block. This metric is calculated in two ways, + by considering all blocks or only parent blocks. Parent blocks are those whose + area is on top of another block area (child). + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface + Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human + Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. + doi: https://doi.org/10.1145/2702123.2702575 + + 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. + In Digital publishing (Vol. 6076, pp. 52-63). SPIE. + doi: https://doi.org/10.1117/12.642120 + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import numpy as np +from pydantic import HttpUrl +from scipy.sparse.csgraph import connected_components + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: Grid Quality. + """ + + _G2_PIXEL_TOLERANCE_ROW: int = 2 # 0 or positive + _G2_PIXEL_TOLERANCE_COLUMN: int = 2 # 0 or positive + + _G3_PIXEL_TOLERANCE_WIDTH: int = 2 # 0 or positive + _G3_PIXEL_TOLERANCE_HEIGHT: int = 2 # 0 or positive + + _G4_PIXEL_TOLERANCE_WIDTH: int = 2 # 0 or positive + _G4_PIXEL_TOLERANCE_HEIGHT: int = 2 # 0 or positive + + _G5_PIXEL_TOLERANCE_VERTICAL: int = 2 # 0 or positive + + @classmethod + def g1_num_visual_blocks(cls, gui_type, segments: List) -> Tuple[int, int]: + """ + Count number of visual GUI blocks. For details, see G1 [1]. + + Args: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + segments: List of GUI elements + + Returns: + - number of visual GUI blocks (int, [0, +inf)) + - number of visual GUI blocks - without children (int, [0, +inf)) + """ + + # Based on [1] this metric should not apply for mobile GUIs. + if gui_type == GUI_TYPE_MOBILE: + raise ValueError( + "This Metric requires gui_type to be non mobile (e.g, desktop)" + ) + + # Count number of all elements + num_blocks: int = int(len(segments)) + + # Count number of elements - without children (filter elements that do not have 'parent' as a key) + num_blocks_woch: int = len( + list(filter(lambda element: "parent" not in element, segments)) + ) + + return num_blocks, num_blocks_woch + + @staticmethod + def count_points(points: List[int], tolerance: int) -> int: + """ + Count number of points with tolerance distance from each other. + + Args: + points: List of Alignment points + tolerance: tolerance distance + + Returns: + - number of points with tolerance + """ + + points_tol: List[int] + + if len(points) == 0: + points_tol = points + else: + # Sort alignment points + points = sorted(points) + + # Alignment points with at least tolerance + 1 distance are added + points_tol = [points[0]] + for p in points: + if p - points_tol[-1] > tolerance: + points_tol.append(p) + + # Count number of points + n_points: int = len(points_tol) + return n_points + + @classmethod + def g2_num_alignment_points( + cls, gui_type, segments: List + ) -> Tuple[int, int]: + """ + Count number of alignment points of GUI blocks. Due to segmentation errors, + this code considers only points with tolerance distances from each other. + For details, see G2 [1]. + + Args: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + segments: List of GUI elements + + Returns: + - number of alignment points (int, [0, +inf)) + - number of alignment points - without children (int, [0, +inf)) + """ + + # Based on [1] this metric should not apply for mobile GUIs. + if gui_type == GUI_TYPE_MOBILE: + raise ValueError( + "This Metric requires gui_type to be non mobile (e.g, desktop)" + ) + + # Compute alignment points based on column and rows + columns: List = [] + rows: List = [] + columns_woch: List = [] + rows_woch: List = [] + for element in segments: + position = element["position"] + columns = columns + [ + position["column_min"], + position["column_max"], + ] + rows = rows + [position["row_min"], position["row_max"]] + # - without children (filter elements that do not have 'parent' as a key) + if "parent" not in element: + columns_woch = columns_woch + [ + position["column_min"], + position["column_max"], + ] + rows_woch = rows_woch + [ + position["row_min"], + position["row_max"], + ] + + # Compute number of all alignment points + num_align_points: int = cls.count_points( + points=rows, tolerance=cls._G2_PIXEL_TOLERANCE_ROW + ) + cls.count_points( + points=columns, tolerance=cls._G2_PIXEL_TOLERANCE_COLUMN + ) + + # Count number of elements - without children (filter elements that do not have 'parent' as a key) + num_align_points_woch = cls.count_points( + points=rows_woch, tolerance=cls._G2_PIXEL_TOLERANCE_ROW + ) + cls.count_points( + points=columns_woch, tolerance=cls._G2_PIXEL_TOLERANCE_COLUMN + ) + + return num_align_points, num_align_points_woch + + @staticmethod + def intersection_list(blocks_list: List) -> np.ndarray: + """ + Compute array of intersection-relation of list elements. + + Args: + blocks_list: List of differrent shapes of each element with tolerance + + Returns: + - intersection-realtion array of input list + """ + + # Compute len of input list + blocks_list_len: int = len(blocks_list) + + # Output intersection array + blocks_intersection: np.ndarray = np.zeros( + (blocks_list_len, blocks_list_len), dtype=int + ) + + # Compute intersections + for i_block in range(0, blocks_list_len): + for j_block in range(0, blocks_list_len): + if j_block > i_block: + # check if there is an intersection + if bool( + set(blocks_list[i_block]) & set(blocks_list[j_block]) + ): + blocks_intersection[i_block, j_block] = 1 + blocks_intersection[j_block, i_block] = 1 + + return blocks_intersection + + @classmethod + def block_size_relation( + cls, segments: List, pixel_tolerance_width, pixel_tolerance_height + ) -> Tuple[np.ndarray, np.ndarray, List[int], List[int]]: + """ + Compute relation of elements in terms of shape (considering the tolerance). + + Args: + segments: List of GUI elements + pixel_tolerance_width: width pixel tolerance threshold + pixel_tolerance_height: height pixel tolerance threshold + + Returns: + - Relation of elements (blocks) shapes (np.ndarray) + - Relation of elements (blocks) shapes - W/O children (np.ndarray) + - List of ids of elements in relation matrix (blocks) (List) + - List of ids of elements in relation matrix - W/O children (List) + """ + + # Compute blocks shapes + blocks_shapes: List[List[Tuple[int, int]]] = [] + blocks_shapes_woch: List[List[Tuple[int, int]]] = [] + blocks_ids: List[int] = [] + blocks_ids_woch: List[int] = [] + + # For each of element we also add the shape with tolerances. + for element in segments: + + # Store different shapes with tolerances of each shape + temp_shapes: List[Tuple[int, int]] = [] + temp_shapes_woch: List[Tuple[int, int]] = [] + + # Save order of id of elements + blocks_ids.append(element["id"]) + # Save order of id of elements - W/O considering children + if "parent" not in element: + blocks_ids_woch.append(element["id"]) + + for w_tol in range( + pixel_tolerance_width, + -1 * pixel_tolerance_width - 1, + -1, + ): + for h_tol in range( + pixel_tolerance_height, + -1 * pixel_tolerance_height - 1, + -1, + ): + shape = ( + element["width"] + w_tol, + element["height"] + h_tol, + ) + temp_shapes.append(shape) + # W/O considering children + if "parent" not in element: + temp_shapes_woch.append(shape) + + blocks_shapes.append(temp_shapes) + # if element is not a child + if len(temp_shapes_woch) > 0: + blocks_shapes_woch.append(temp_shapes_woch) + + # Find relation between shapes, if they are the same. + blocks_relation = cls.intersection_list(blocks_shapes) + blocks_relation_woch = cls.intersection_list(blocks_shapes_woch) + + return ( + blocks_relation, + blocks_relation_woch, + blocks_ids, + blocks_ids_woch, + ) + + @classmethod + def g3_num_block_sizes(cls, segments: List) -> Tuple[int, int]: + """ + Count number of block sizes, grid proportionality. This code also supports + tolerance because of segmentation errors. For details, see G3 [1]. + + Args: + segments: List of GUI elements + + Returns: + - number of block sizes (int, [0, +inf)) + - number of block sizes - without children (int, [0, +inf)) + """ + + # Number of Unique block sizes + num_block_size: int = 0 + num_block_size_woch: int = 0 + + if len(segments) != 0: + # Compute block size relation + blocks_relation: np.ndarray + blocks_relation_woch: np.ndarray + ( + blocks_relation, + blocks_relation_woch, + _, + _, + ) = cls.block_size_relation( + segments, + pixel_tolerance_width=cls._G3_PIXEL_TOLERANCE_WIDTH, + pixel_tolerance_height=cls._G3_PIXEL_TOLERANCE_HEIGHT, + ) + + # Compute number of different block sizes the GUI have + num_block_size = connected_components(blocks_relation)[0] + num_block_size_woch = connected_components(blocks_relation_woch)[0] + + return num_block_size, num_block_size_woch + + @staticmethod + def coverage_mask( + segments: List, + blocks_ids: List[int], + mask_blocks_zeroes: np.ndarray, + height: int, + width: int, + ) -> float: + """ + Compute gui coverage of same size elements. + + Args: + segments: List of GUI elements + blocks_ids: List of GUI elements (blocks) ids + mask_blocks_zeroes: mask blocks with no same size pair + height: height of input GUI + width: width of input GUI + + Returns: + - Relation of elements (blocks) shapes (np.ndarray) + - Relation of elements (blocks) shapes - W/O children (np.ndarray) + - List of ids of elements in relation matrix (blocks) (List) + - List of ids of elements in relation matrix - W/O children (List) + """ + coverage_array = np.zeros((height, width), dtype=int) + + for mask_index in range(0, len(mask_blocks_zeroes)): + # if it has a same size pair, then: + if not mask_blocks_zeroes[mask_index]: + ele = list( + filter( + ( + lambda element: blocks_ids[mask_index] + == element["id"] + ), + segments, + ) + )[0] + position = ele["position"] + coverage_array[ + position["row_min"] : position["row_max"], + position["column_min"] : position["column_max"], + ] = 1 + + gui_coverage = float(np.mean(coverage_array)) + return gui_coverage + + @classmethod + def g4_gui_coverage( + cls, segments: List, height, width + ) -> Tuple[float, float]: + """ + The proportion of GUI covered by same-size blocks (the cell coverage computation from [2]). + This code computes the percentage coverage of gui blocks with same size. In other words, It does + not consider blocks with just one size appearance. For details, see G4 [1]. + + Args: + segments: List of GUI elements + height: height of input GUI + width: width of input GUI + + Returns: + - GUI Coverage (float, [0, 1]) + - GUI Coverage - without children (float, [0, 1]) + """ + + if len(segments) != 0: + # Compute block size relation + blocks_relation: np.ndarray + blocks_relation_woch: np.ndarray + ( + blocks_relation, + blocks_relation_woch, + blocks_ids, + blocks_ids_woch, + ) = cls.block_size_relation( + segments, + pixel_tolerance_width=cls._G4_PIXEL_TOLERANCE_WIDTH, + pixel_tolerance_height=cls._G4_PIXEL_TOLERANCE_HEIGHT, + ) + + # Find blocks with no same size pair (reverse: find block with at least one same size pair) + mask_blocks_zeroes = np.all(blocks_relation == 0, axis=1) + mask_block_woch_zeroes = np.all(blocks_relation_woch == 0, axis=1) + + # Compute GUI coverage percentage with the same size blocks + gui_coverage: float = cls.coverage_mask( + segments, blocks_ids, mask_blocks_zeroes, height, width + ) + gui_coverage_woch: float = cls.coverage_mask( + segments, + blocks_ids_woch, + mask_block_woch_zeroes, + height, + width, + ) + + return gui_coverage, gui_coverage_woch + + @classmethod + def count_vertical_sizes(cls, vertical_sizes: List[int]) -> int: + """ + Count number of vertical sizes with tolerance distance from each other. + + Args: + vertical_sizes: List of Alignment points + + Returns: + number of vertical sizes with tolerance + """ + + vertical_sizes_tol: List[int] + + if len(vertical_sizes) == 0: + vertical_sizes_tol = vertical_sizes + else: + # Sort vertical sizes + vertical_sizes = sorted(vertical_sizes) + + # Vertical sizes with at least tolerance + 1 distance are added + vertical_sizes_tol = [vertical_sizes[0]] + for p in vertical_sizes: + if ( + p - vertical_sizes_tol[-1] + > cls._G5_PIXEL_TOLERANCE_VERTICAL + ): + vertical_sizes_tol.append(p) + + # Count number of vertical sizes + n_vertical_sizes: int = len(vertical_sizes_tol) + return n_vertical_sizes + + @classmethod + def g5_num_vertical_block_sizes(cls, segments: List) -> Tuple[int, int]: + """ + The number of vertical block sizes, i.e., vertical grid + proportionality. This code computes the number of different + heights for blocks in the GUI. For details, see G5 [1]. + + Args: + segments: List of GUI elements + + Returns: + - number of vertical block sizes (int, [0, +inf)) + - number of vertical block sizes - without children (int, [0, +inf)) + + """ + + # Number of Unique vertical sizes + num_vertical_sizes: int = 0 + num_vertical_sizes_woch: int = 0 + + # Compute vertical sizes (height of elements) + if len(segments) != 0: + vertical_sizes: List[int] = [] + vertical_sizes_woch: List[int] = [] + for element in segments: + height = element["height"] + vertical_sizes.append(height) + # W/O considering children + if "parent" not in element: + vertical_sizes_woch.append(height) + + # Compute unique vertical sizes + num_vertical_sizes = cls.count_vertical_sizes(vertical_sizes) + num_vertical_sizes_woch = cls.count_vertical_sizes( + vertical_sizes_woch + ) + + return num_vertical_sizes, num_vertical_sizes_woch + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - number of visual GUI blocks (int, [0, +inf)) + - number of visual GUI blocks - without children (int, [0, +inf)) + """ + + # Get all elements + if gui_segments is not None: + segments: List = gui_segments["segments"] + height, width, _ = gui_segments["img_shape"] + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + # Based on [1], G1 and G2 metrics should not apply for mobile GUIs. + # Compute (non-mobile) grid quality metrics + g1: Tuple[int, int] = cls.g1_num_visual_blocks(gui_type, segments) + g2: Tuple[int, int] = cls.g2_num_alignment_points(gui_type, segments) + + # Comptute rest of grid quality metrics + g3: Tuple[int, int] = cls.g3_num_block_sizes(segments) + g4: Tuple[float, float] = cls.g4_gui_coverage(segments, height, width) + g5: Tuple[int, int] = cls.g5_num_vertical_block_sizes(segments) + + return [ + g1[0], + g1[1], + g2[0], + g2[1], + g3[0], + g3[1], + g4[0], + g4[1], + g5[0], + g5[1], + ] diff --git a/backend/aim/metrics/m21/m21_num_visual_blocks.py b/backend/aim/metrics/m21/m21_num_visual_blocks.py deleted file mode 100644 index b9d43f2..0000000 --- a/backend/aim/metrics/m21/m21_num_visual_blocks.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Metric: - Number of Visual Blocks - - -Description: - The number of visual GUI blocks. - The code considers each GUI element as a visual block. This metric is - calculated in two ways, by considering all blocks or only parent blocks. - Parent blocks are those whose area is on top of another block area (child). - - Category: Grid Quality > Number of Visual Blocks. - For details, see G1 [1]. - - -Funding information and contact: - This work was funded by Technology Industries of Finland in a three-year - project grant on self-optimizing web services. The principal investigator - is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. - - -References: - 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface - Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human - Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. - doi: https://doi.org/10.1145/2702123.2702575 - - 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. - In Digital publishing (Vol. 6076, pp. 52-63). SPIE. - doi: https://doi.org/10.1117/12.642120 - - -Change log: - v1.0 (2022-08-05) - * Initial implementation -""" - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -from typing import Any, Dict, List, Optional, Union - -# Third-party modules -from pydantic import HttpUrl - -# First-party modules -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.interfaces import AIMMetricInterface - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Metric -# ---------------------------------------------------------------------------- - - -class Metric(AIMMetricInterface): - """ - Metric: Number of Visual Blocks. - """ - - # Public methods - @classmethod - def execute_metric( - cls, - gui_image: str, - gui_type: int = GUI_TYPE_DESKTOP, - gui_segments: Optional[Dict[str, Any]] = None, - gui_url: Optional[HttpUrl] = None, - ) -> Optional[List[Union[int, float, str]]]: - """ - Execute the metric. - - Args: - gui_image: GUI image (PNG) encoded in Base64 - - Kwargs: - gui_type: GUI type, desktop = 0 (default), mobile = 1 - gui_segments: GUI segmentation (defaults to None) - gui_url: GUI URL (defaults to None) - - Returns: - Results (list of measures) - - Number of Visual Blocks (int, [0, +inf)) - """ - - # Based on [1] this metric should not apply for mobile GUIs. - if gui_type == GUI_TYPE_MOBILE: - raise ValueError( - "This Metric requires gui_type to be non mobile (e.g, desktop)" - ) - - # Get all elements - if gui_segments is not None: - segments: List = gui_segments["segments"] - else: - raise ValueError( - "This Metric requires gui_segments to be not None" - ) - - # Get elements without considering children: Filter elements that do not have 'parent' as a key - segments_without_children: List = list( - filter(lambda element: not element.get("parent", None), segments) - ) - - # Count number of all elements - num_vis_blocks_with_children: int = int(len(segments)) - # Count number of elements without considering children - num_vis_blocks_without_children: int = int( - len(segments_without_children) - ) - - return [num_vis_blocks_with_children, num_vis_blocks_without_children] diff --git a/backend/aim/metrics/m22/m22_num_alignment_points.py b/backend/aim/metrics/m22/m22_num_alignment_points.py deleted file mode 100644 index b02b889..0000000 --- a/backend/aim/metrics/m22/m22_num_alignment_points.py +++ /dev/null @@ -1,191 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Metric: - Number of Alignment Points of Blocks - - -Description: - The number of alignment points of GUI blocks. This metric is - calculated in two ways, by considering all blocks or only parent blocks. - Parent blocks are those whose area is on top of another block area (child). - Moreover, due to segmentation errors, this code considers only points with - tolerance distances from each other. - - Category: Grid Quality > Number of Alignment Points of Blocks. - For details, see G2 [1]. - - -Funding information and contact: - This work was funded by Technology Industries of Finland in a three-year - project grant on self-optimizing web services. The principal investigator - is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. - - -References: - 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface - Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human - Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. - doi: https://doi.org/10.1145/2702123.2702575 - - 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. - In Digital publishing (Vol. 6076, pp. 52-63). SPIE. - doi: https://doi.org/10.1117/12.642120 - - -Change log: - v1.0 (2022-08-05) - * Initial implementation -""" - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -from typing import Any, Dict, List, Optional, Tuple, Union - -# Third-party modules -from pydantic import HttpUrl - -# First-party modules -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.interfaces import AIMMetricInterface - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Metric -# ---------------------------------------------------------------------------- - - -class Metric(AIMMetricInterface): - """ - Metric: Number of Alignment Points of Blocks. - """ - - _PIXEL_TOLERANCE_ROW: int = 0 # 0 or positive - _PIXEL_TOLERANCE_COLUMN: int = 0 # 0 or positive - - @classmethod - def count_points(cls, points: List[int], axis: int = 0) -> int: - """ - Count number of points with tolerance distance from each other. - - Args: - points: List of Alignment points - axis: 0 for rows and 1 for columns, default 0 - - Returns: - number of points with tolerance - """ - - if axis == 1: - tolerance = cls._PIXEL_TOLERANCE_COLUMN - else: - tolerance = cls._PIXEL_TOLERANCE_ROW - - # Sort alignment points - points = sorted(points) - # Alignment points with at least tolerance + 1 distance are added - points_tol: List[int] = [points[0]] - for p in points: - if p - points_tol[-1] > tolerance: - points_tol.append(p) - - # Count number of points - n_points: int = len(points_tol) - return n_points - - # Public methods - @classmethod - def execute_metric( - cls, - gui_image: str, - gui_type: int = GUI_TYPE_DESKTOP, - gui_segments: Optional[Dict[str, Any]] = None, - gui_url: Optional[HttpUrl] = None, - ) -> Optional[List[Union[int, float, str]]]: - """ - Execute the metric. - - Args: - gui_image: GUI image (PNG) encoded in Base64 - - Kwargs: - gui_type: GUI type, desktop = 0 (default), mobile = 1 - gui_segments: GUI segmentation (defaults to None) - gui_url: GUI URL (defaults to None) - - Returns: - Results (list of measures) - - Number of Alignment Points of Blocks (int, [0, +inf)) - """ - - # Based on [1] this metric should not apply for mobile GUIs. - if gui_type == GUI_TYPE_MOBILE: - raise ValueError( - "This Metric requires gui_type to be non mobile (e.g, desktop)" - ) - - # Get all elements - if gui_segments is not None: - segments: List = gui_segments["segments"] - else: - raise ValueError( - "This Metric requires gui_segments to be not None" - ) - - # Compute alignment points based on column and rows - columns: List = [] - rows: List = [] - columns_wo_children: List = [] - rows_wo_children: List = [] - for element in segments: - position = element["position"] - columns = columns + [ - position["column_min"], - position["column_max"], - ] - rows = rows + [position["row_min"], position["row_max"]] - # W/O considering children - if not element.get("parent", None): - columns_wo_children = columns_wo_children + [ - position["column_min"], - position["column_max"], - ] - rows_wo_children = rows_wo_children + [ - position["row_min"], - position["row_max"], - ] - - # Compute number of alignment point for row W and W/O children - num_align_points_row: int = cls.count_points(points=rows, axis=0) - num_align_points_row_wo_children = cls.count_points( - points=rows_wo_children, axis=0 - ) - - # Compute number of alignment point for column W and W/O children - num_align_points_column = cls.count_points(points=columns, axis=1) - num_align_points_column_wo_children = cls.count_points( - points=columns_wo_children, axis=1 - ) - - # Compute number of unique alignment points - num_align_points: int = num_align_points_row + num_align_points_column - # Compute number of unique alignment points W/O considering children - num_align_points_wo_children = ( - num_align_points_row_wo_children - + num_align_points_column_wo_children - ) - - return [num_align_points, num_align_points_wo_children] diff --git a/backend/aim/metrics/m26/m26_white_space.py b/backend/aim/metrics/m22/m22_white_space.py similarity index 93% rename from backend/aim/metrics/m26/m26_white_space.py rename to backend/aim/metrics/m22/m22_white_space.py index b43aec7..ef1b536 100644 --- a/backend/aim/metrics/m26/m26_white_space.py +++ b/backend/aim/metrics/m22/m22_white_space.py @@ -7,10 +7,7 @@ Description: - White Space. Non covered portion of GUI with visual blocks. - - Category: White Space. - For details, see [1]. + White Space. Non-covered portion of GUI with visual blocks. Funding information and contact: @@ -98,10 +95,13 @@ def execute_metric( ) if gui_segments is not None: + + # Create a binary array (default = 1) with the same size of image height, width, _ = gui_segments["img_shape"] segments: List = gui_segments["segments"] img_binary = np.ones((height, width), dtype=int) + # Fill the binary array with 0 for the elements for element in segments: position = element["position"] img_binary[ @@ -109,7 +109,9 @@ def execute_metric( position["column_min"] : position["column_max"], ] = 0 + # Compute white area percentage white_percentage: float = float(np.mean(img_binary)) + else: raise ValueError( "This Metric requires gui_segments to be not None" diff --git a/backend/aim/metrics/m23/__init__.py b/backend/aim/metrics/m23/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/backend/aim/metrics/m23/m23_num_block_sizes.py b/backend/aim/metrics/m23/m23_num_block_sizes.py deleted file mode 100644 index 725564e..0000000 --- a/backend/aim/metrics/m23/m23_num_block_sizes.py +++ /dev/null @@ -1,245 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Metric: - Number of Block Sizes - - -Description: - The number of block sizes, grid proportionality. This metric is - calculated in two ways, by considering all blocks or only parent blocks. - Parent blocks are those whose area is on top of another block area (child). - This code also supports tolerance because of segmentation errors. - - Category: Grid Quality > Number of Block Sizes. - For details, see G3 [1]. - -Funding information and contact: - This work was funded by Technology Industries of Finland in a three-year - project grant on self-optimizing web services. The principal investigator - is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. - - -References: - 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface - Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human - Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. - doi: https://doi.org/10.1145/2702123.2702575 - - 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. - In Digital publishing (Vol. 6076, pp. 52-63). SPIE. - doi: https://doi.org/10.1117/12.642120 - - -Change log: - v1.0 (2022-08-05) - * Initial implementation -""" - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -from typing import Any, Dict, List, Optional, Tuple, Union - -# Third-party modules -import numpy as np -from pydantic import HttpUrl -from scipy.sparse.csgraph import connected_components - -# First-party modules -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.interfaces import AIMMetricInterface - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Metric -# ---------------------------------------------------------------------------- - - -class Metric(AIMMetricInterface): - """ - Metric: Number of Block Sizes. - """ - - _PIXEL_TOLERANCE_WIDTH: int = 0 # 0 or positive - _PIXEL_TOLERANCE_HEIGHT: int = 0 # 0 or positive - - @staticmethod - def block_size_relation( - segments: List, pixel_tolerance_width, pixel_tolerance_height - ) -> Tuple[np.ndarray, np.ndarray, List[int], List[int]]: - """ - Execute the metric. - - Args: - segments: List of GUI elements - pixel_tolerance_width: width pixel tolerance threshold - pixel_tolerance_height: height pixel tolerance threshold - - Returns: - Relation of elements (blocks) sizes W children (np.ndarray) - Relation of elements (blocks) sizes W/O children (np.ndarray) - List of ids of elements in relation matrix (blocks) W children (List) - List of ids of elements in relation matrix W/O children (List) - """ - - # Compute block sizes - block_size_total: List[List[Tuple[int, int]]] = [] - block_size_total_without_children: List[List[Tuple[int, int]]] = [] - block_ids: List[int] = [] - block_ids_without_children: List[int] = [] - - # For each of element we also add the shape with tolerances. - for element in segments: - - # Store different shapes with tolerances of each shape - block_shapes: List[Tuple[int, int]] = [] - block_shapes_without_children: List[Tuple[int, int]] = [] - - # Save order of id of elements - block_ids.append(element["id"]) - # W/O considering children - if not element.get("parent", None): - block_ids_without_children.append(element["id"]) - - for w_tol in range( - pixel_tolerance_width, - -1 * pixel_tolerance_width - 1, - -1, - ): - for h_tol in range( - pixel_tolerance_height, - -1 * pixel_tolerance_height - 1, - -1, - ): - shape = ( - element["width"] + w_tol, - element["height"] + h_tol, - ) - block_shapes.append(shape) - # W/O considering children - if not element.get("parent", None): - block_shapes_without_children.append(shape) - - block_size_total.append(block_shapes) - # if element is not a child - if len(block_shapes_without_children) > 0: - block_size_total_without_children.append( - block_shapes_without_children - ) - - # Find relation between shapes, if they are the same. - blocks_total_len: int = len(block_size_total) - blocks_relation: np.ndarray = np.zeros( - (blocks_total_len, blocks_total_len), dtype=int - ) - for i_block in range(0, blocks_total_len): - for j_block in range(0, blocks_total_len): - if j_block > i_block: - if bool( - set(block_size_total[i_block]) - & set(block_size_total[j_block]) - ): - blocks_relation[i_block, j_block] = 1 - blocks_relation[j_block, i_block] = 1 - - # Find relation between shapes, if they are the same (without children) - blocks_total_without_children_len: int = len( - block_size_total_without_children - ) - blocks_relation_without_children: np.ndarray = np.zeros( - ( - blocks_total_without_children_len, - blocks_total_without_children_len, - ), - dtype=int, - ) - for i_block in range(0, blocks_total_without_children_len): - for j_block in range(0, blocks_total_without_children_len): - if j_block > i_block: - if bool( - set(block_size_total_without_children[i_block]) - & set(block_size_total_without_children[j_block]) - ): - blocks_relation_without_children[i_block, j_block] = 1 - blocks_relation_without_children[j_block, i_block] = 1 - - return ( - blocks_relation, - blocks_relation_without_children, - block_ids, - block_ids_without_children, - ) - - # Public methods - @classmethod - def execute_metric( - cls, - gui_image: str, - gui_type: int = GUI_TYPE_DESKTOP, - gui_segments: Optional[Dict[str, Any]] = None, - gui_url: Optional[HttpUrl] = None, - ) -> Optional[List[Union[int, float, str]]]: - """ - Execute the metric. - - Args: - gui_image: GUI image (PNG) encoded in Base64 - - Kwargs: - gui_type: GUI type, desktop = 0 (default), mobile = 1 - gui_segments: GUI segmentation (defaults to None) - gui_url: GUI URL (defaults to None) - - Returns: - Results (list of measures) - - Number of Block Sizes (int, [0, +inf)) - """ - - # Get all elements - if gui_segments is not None: - segments: List = gui_segments["segments"] - else: - raise ValueError( - "This Metric requires gui_segments to be not None" - ) - - # Number of Unique block sizes - num_block_size: int = 0 - num_block_size_without_children: int = 0 - - if len(segments) != 0: - - # Compute block size relation - blocks_relation: np.ndarray - blocks_relation_without_children: np.ndarray - ( - blocks_relation, - blocks_relation_without_children, - _, - _, - ) = cls.block_size_relation( - segments, - pixel_tolerance_width=cls._PIXEL_TOLERANCE_WIDTH, - pixel_tolerance_height=cls._PIXEL_TOLERANCE_HEIGHT, - ) - - # Compute number of different block sizes the GUI have - num_block_size = connected_components(blocks_relation)[0] - num_block_size_without_children = connected_components( - blocks_relation_without_children - )[0] - - return [num_block_size, num_block_size_without_children] diff --git a/backend/aim/metrics/m24/__init__.py b/backend/aim/metrics/m24/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/backend/aim/metrics/m24/m24_gui_coverage.py b/backend/aim/metrics/m24/m24_gui_coverage.py deleted file mode 100644 index a74b0f1..0000000 --- a/backend/aim/metrics/m24/m24_gui_coverage.py +++ /dev/null @@ -1,190 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Metric: - GUI Coverage - - -Description: - The proportion of GUI covered by same-size blocks (the cell coverage computation from [2]). - This code computes the number of gui blocks with same size. It does not consider blocks with - just one size appearance. - - Category: Grid Quality > GUI Coverage. - For details, see G4 [1]. - - -Funding information and contact: - This work was funded by Technology Industries of Finland in a three-year - project grant on self-optimizing web services. The principal investigator - is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. - - -References: - 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface - Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human - Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. - doi: https://doi.org/10.1145/2702123.2702575 - - 2. Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. - In Digital publishing (Vol. 6076, pp. 52-63). SPIE. - doi: https://doi.org/10.1117/12.642120 - - -Change log: - v1.0 (2022-08-05) - * Initial implementation -""" - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -from typing import Any, Dict, List, Optional, Tuple, Union - -# Third-party modules -import numpy as np -from pydantic import HttpUrl -from scipy.sparse.csgraph import connected_components - -# First-party modules -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.interfaces import AIMMetricInterface -from aim.metrics.m23.m23_num_block_sizes import Metric as m23 - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Metric -# ---------------------------------------------------------------------------- - - -class Metric(AIMMetricInterface): - """ - Metric: GUI Coverage. - """ - - _PIXEL_TOLERANCE_WIDTH: int = 2 # 0 or positive - _PIXEL_TOLERANCE_HEIGHT: int = 2 # 0 or positive - - # Public methods - @classmethod - def execute_metric( - cls, - gui_image: str, - gui_type: int = GUI_TYPE_DESKTOP, - gui_segments: Optional[Dict[str, Any]] = None, - gui_url: Optional[HttpUrl] = None, - ) -> Optional[List[Union[int, float, str]]]: - """ - Execute the metric. - - Args: - gui_image: GUI image (PNG) encoded in Base64 - - Kwargs: - gui_type: GUI type, desktop = 0 (default), mobile = 1 - gui_segments: GUI segmentation (defaults to None) - gui_url: GUI URL (defaults to None) - - Returns: - Results (list of measures) - - GUI Coverage (float, [0, 1]) - """ - # Get all elements - if gui_segments is not None: - segments: List = gui_segments["segments"] - height, width, _ = gui_segments["img_shape"] - else: - raise ValueError( - "This Metric requires gui_segments to be not None" - ) - - # GUI coverage percent with the same size blocks - gui_coverage: float = 0.0 - gui_coverage_without_children: float = 0.0 - - if len(segments) != 0: - # Compute block size relation - blocks_relation: np.ndarray - blocks_relation_without_children: np.ndarray - ( - blocks_relation, - blocks_relation_without_children, - block_ids, - block_ids_without_children, - ) = m23.block_size_relation( - segments, - pixel_tolerance_width=cls._PIXEL_TOLERANCE_WIDTH, - pixel_tolerance_height=cls._PIXEL_TOLERANCE_HEIGHT, - ) - - # Coverage array - coverage_array = np.zeros((height, width), dtype=int) - coverage_array_without_children = np.zeros( - (height, width), dtype=int - ) - - # Find blocks with no same size pair (reverse: find block with at least one same size pair) - mask_blocks_zeroes = np.all(blocks_relation == 0, axis=1) - mask_block_without_children_zeroes = np.all( - blocks_relation_without_children == 0, axis=1 - ) - - # Compute coverage array - for mask_index in range(0, len(mask_blocks_zeroes)): - if not mask_blocks_zeroes[mask_index]: - ele = list( - filter( - ( - lambda element: block_ids[mask_index] - == element["id"] - ), - segments, - ) - )[0] - position = ele["position"] - coverage_array[ - position["row_min"] : position["row_max"], - position["column_min"] : position["column_max"], - ] = 1 - - # Compute coverage array (without children) - for mask_index in range( - 0, len(mask_block_without_children_zeroes) - ): - if not mask_block_without_children_zeroes[mask_index]: - ele = list( - filter( - ( - lambda element: block_ids_without_children[ - mask_index - ] - == element["id"] - ), - segments, - ) - )[0] - position = ele["position"] - coverage_array_without_children[ - position["row_min"] : position["row_max"], - position["column_min"] : position["column_max"], - ] = 1 - - # Compute percentage - gui_coverage = float(np.mean(coverage_array)) - gui_coverage_without_children = float( - np.mean(coverage_array_without_children) - ) - - return [gui_coverage, gui_coverage_without_children] diff --git a/backend/aim/metrics/m25/__init__.py b/backend/aim/metrics/m25/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/backend/aim/metrics/m25/m25_num_vertical_block_sizes.py b/backend/aim/metrics/m25/m25_num_vertical_block_sizes.py deleted file mode 100644 index 484df1d..0000000 --- a/backend/aim/metrics/m25/m25_num_vertical_block_sizes.py +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Metric: - Number of Vertical Block Sizes - - -Description: - The number of vertical block sizes, i.e., vertical grid - proportionality. This code computes the number of different - heights for blocks in the GUI. - - Category: Grid Quality > Number of Vertical Block Sizes. - For details, see G5 [1]. - - -Funding information and contact: - This work was funded by Technology Industries of Finland in a three-year - project grant on self-optimizing web services. The principal investigator - is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. - - -References: - 1. Miniukovich, A. and De Angeli, A. (2015). Computation of Interface - Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human - Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. - doi: https://doi.org/10.1145/2702123.2702575 - - -Change log: - v1.0 (2022-08-05) - * Initial implementation -""" - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -from typing import Any, Dict, List, Optional, Union - -# Third-party modules -import numpy as np -from pydantic import HttpUrl - -# First-party modules -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.interfaces import AIMMetricInterface - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Metric -# ---------------------------------------------------------------------------- - - -class Metric(AIMMetricInterface): - """ - Metric: Number of Vertical Block Sizes. - """ - - _PIXEL_TOLERANCE_VERTICAL: int = 0 # 0 or positive - - @classmethod - def count_vertical_sizes(cls, vertical_sizes: List[int]) -> int: - """ - Count number of vertical sizes with tolerance distance from each other. - - Args: - vertical_sizes: List of Alignment points - - Returns: - number of vertical sizes with tolerance - """ - - # Sort vertical sizes - vertical_sizes = sorted(vertical_sizes) - # Vertical sizes with at least tolerance + 1 distance are added - vertical_sizes_tol: List[int] = [vertical_sizes[0]] - for p in vertical_sizes: - if p - vertical_sizes_tol[-1] > cls._PIXEL_TOLERANCE_VERTICAL: - vertical_sizes_tol.append(p) - - # Count number of vertical sizes - n_vertical_sizes: int = len(vertical_sizes_tol) - return n_vertical_sizes - - # Public methods - @classmethod - def execute_metric( - cls, - gui_image: str, - gui_type: int = GUI_TYPE_DESKTOP, - gui_segments: Optional[Dict[str, Any]] = None, - gui_url: Optional[HttpUrl] = None, - ) -> Optional[List[Union[int, float, str]]]: - """ - Execute the metric. - - Args: - gui_image: GUI image (PNG) encoded in Base64 - - Kwargs: - gui_type: GUI type, desktop = 0 (default), mobile = 1 - gui_segments: GUI segmentation (defaults to None) - gui_url: GUI URL (defaults to None) - - Returns: - Results (list of measures) - - Number of Vertical Block Sizes (int, [0, +inf)) - """ - - # Get all elements - if gui_segments is not None: - segments: List = gui_segments["segments"] - else: - raise ValueError( - "This Metric requires gui_segments to be not None" - ) - - # Number of Unique vertical sizes - num_vertical_sizes: int = 0 - num_vertical_sizes_without_children: int = 0 - - # Compute vertical sizes (height of elements) - if len(segments) != 0: - vertical_sizes: List[int] = [] - vertical_sizes_without_children: List[int] = [] - for element in segments: - height = element["height"] - vertical_sizes.append(height) - # W/O considering children - if not element.get("parent", None): - vertical_sizes_without_children.append(height) - - # Compute unique vertical sizes - num_vertical_sizes = cls.count_vertical_sizes(vertical_sizes) - num_vertical_sizes_without_children = cls.count_vertical_sizes( - vertical_sizes_without_children - ) - - return [num_vertical_sizes, num_vertical_sizes_without_children] diff --git a/backend/aim/metrics/m26/__init__.py b/backend/aim/metrics/m26/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/backend/aim/segmentation/text_seg_utils.py b/backend/aim/segmentation/text_seg_utils.py index 9fbb2b5..ff5faf3 100644 --- a/backend/aim/segmentation/text_seg_utils.py +++ b/backend/aim/segmentation/text_seg_utils.py @@ -93,7 +93,7 @@ def text2json(texts, img_shape): def text_cvt_orc_format_paddle(paddle_result): texts = [] - for i, line in enumerate(paddle_result): + for i, line 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""" @@ -20,7 +20,7 @@ # First-party modules from aim.common import image_utils from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.m21.m21_num_visual_blocks import Metric +from aim.metrics.m21.m21_grid_quality import Metric from aim.segmentation.model import Segmentation from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR @@ -42,15 +42,21 @@ @pytest.mark.parametrize( ["input_value", "expected_results"], [ - ("interfacemetrics_aalto.png", [27, 21]), - ("white.png", [0, 0]), + ("seg1.png", [12, 8, 26, 18, 5, 4, 0.2555, 0.2555, 5, 4]), + ("seg2.png", [2, 2, 8, 8, 2, 2, 0.0, 0.0, 2, 2]), + ("seg3.png", [4, 2, 16, 8, 3, 2, 0.0211, 0.0, 3, 2]), + ("seg4.png", [2, 2, 6, 6, 1, 1, 0.5608, 0.5608, 1, 1]), + ( + "interfacemetrics_aalto.png", + [27, 21, 80, 62, 22, 17, 0.2086, 0.2086, 11, 9], + ), ], ) -def test_num_visual_blocks_desktop( +def test_grid_quality_desktop( input_value: str, expected_results: List[Any] ) -> None: """ - Test Number of Visual Blocks (desktop GUIs). + Test Grid Quality (desktop GUIs). Args: input_value: GUI image file name @@ -78,5 +84,5 @@ def test_num_visual_blocks_desktop( ) # Test result - if result is not None and all(isinstance(res, int) for res in result): - assert result == expected_results + if result is not None and all(isinstance(i, (float, int)) for i in result): + assert [round(float(i), 4) for i in result] == expected_results diff --git a/backend/tests/metrics/test_m22.py b/backend/tests/metrics/test_m22.py index 435a7e2..1ffab6e 100644 --- a/backend/tests/metrics/test_m22.py +++ b/backend/tests/metrics/test_m22.py @@ -2,7 +2,7 @@ # -*- coding: utf-8 -*- """ -Tests for the 'Number of Alignment Points of Blocks' metric (m22). +Tests for the 'White Space' metric (m22). """ @@ -20,7 +20,7 @@ # First-party modules from aim.common import image_utils from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.m22.m22_num_alignment_points import Metric +from aim.metrics.m22.m22_white_space import Metric from aim.segmentation.model import Segmentation from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR @@ -41,13 +41,18 @@ @pytest.mark.parametrize( ["input_value", "expected_results"], - [("interfacemetrics_aalto.png", [91, 71])], + [ + ("aalto.fi_website.png", [0.2663]), + ("myhelsinki.fi_website.png", [0.884]), + ("wikipedia.org_website.png", [0.3033]), + ("black.png", [1.0]), + ], ) -def test_num_alignment_points_desktop( +def test_white_space_desktop( input_value: str, expected_results: List[Any] ) -> None: """ - Test Number of Alignment Points of Blocks (desktop GUIs). + Test White Space (desktop GUIs). Args: input_value: GUI image file name @@ -75,5 +80,5 @@ def test_num_alignment_points_desktop( ) # Test result - if result is not None and all(isinstance(res, int) for res in result): - assert result == expected_results + if result is not None and isinstance(result[0], float): + assert round(result[0], 4) == expected_results[0] diff --git a/backend/tests/metrics/test_m23.py b/backend/tests/metrics/test_m23.py deleted file mode 100644 index 01a8774..0000000 --- a/backend/tests/metrics/test_m23.py +++ /dev/null @@ -1,82 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Tests for the 'Number of Block Sizes' metric (m23). -""" - - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -import pathlib -from typing import Any, Dict, List, Optional, Tuple, Union - -# Third-party modules -import pytest - -# First-party modules -from aim.common import image_utils -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.m23.m23_num_block_sizes import Metric -from aim.segmentation.model import Segmentation -from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Tests -# ---------------------------------------------------------------------------- - - -@pytest.mark.parametrize( - ["input_value", "expected_results"], - [ - ("interfacemetrics_aalto.png", [26, 20]), - ("white.png", [0, 0]), - ], -) -def test_num_block_sizes_desktop( - input_value: str, expected_results: List[Any] -) -> None: - """ - Test Number of Block Sizes (desktop GUIs). - - Args: - input_value: GUI image file name - expected_results: Expected results (list of measures) - """ - # Build GUI image file path - gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value - ) - - # Read GUI image (PNG) - gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) - - # Execute segmentation - result_segments: Dict[str, Any] - _, result_segments = Segmentation.execute( - gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP - ) - - # Execute metric - result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( - gui_image_png_base64, - gui_segments=result_segments, - gui_type=GUI_TYPE_DESKTOP, - ) - - # Test result - if result is not None and all(isinstance(res, int) for res in result): - assert result == expected_results diff --git a/backend/tests/metrics/test_m24.py b/backend/tests/metrics/test_m24.py deleted file mode 100644 index 302d35a..0000000 --- a/backend/tests/metrics/test_m24.py +++ /dev/null @@ -1,82 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Tests for the 'GUI Coverage' metric (m24). -""" - - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -import pathlib -from typing import Any, Dict, List, Optional, Tuple, Union - -# Third-party modules -import pytest - -# First-party modules -from aim.common import image_utils -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.m24.m24_gui_coverage import Metric -from aim.segmentation.model import Segmentation -from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Tests -# ---------------------------------------------------------------------------- - - -@pytest.mark.parametrize( - ["input_value", "expected_results"], - [ - ("interfacemetrics_aalto.png", [0.2086, 0.2086]), - ("white.png", [0.0, 0.0]), - ], -) -def test_gui_coverage_desktop( - input_value: str, expected_results: List[Any] -) -> None: - """ - Test GUI Coverage (desktop GUIs). - - Args: - input_value: GUI image file name - expected_results: Expected results (list of measures) - """ - # Build GUI image file path - gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value - ) - - # Read GUI image (PNG) - gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) - - # Execute segmentation - result_segments: Dict[str, Any] - _, result_segments = Segmentation.execute( - gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP - ) - - # Execute metric - result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( - gui_image_png_base64, - gui_segments=result_segments, - gui_type=GUI_TYPE_DESKTOP, - ) - - # Test result - if result is not None and all(isinstance(res, float) for res in result): - assert [round(float(res), 4) for res in result] == expected_results diff --git a/backend/tests/metrics/test_m25.py b/backend/tests/metrics/test_m25.py deleted file mode 100644 index be4cee4..0000000 --- a/backend/tests/metrics/test_m25.py +++ /dev/null @@ -1,82 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Tests for the 'Number of Vertical Block Sizes' metric (m25). -""" - - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -import pathlib -from typing import Any, Dict, List, Optional, Tuple, Union - -# Third-party modules -import pytest - -# First-party modules -from aim.common import image_utils -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.m25.m25_num_vertical_block_sizes import Metric -from aim.segmentation.model import Segmentation -from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Tests -# ---------------------------------------------------------------------------- - - -@pytest.mark.parametrize( - ["input_value", "expected_results"], - [ - ("interfacemetrics_aalto.png", [18, 14]), - ("white.png", [0, 0]), - ], -) -def test_num_vertical_block_sizes_desktop( - input_value: str, expected_results: List[Any] -) -> None: - """ - Test Number of Vertical Block Sizes (desktop GUIs). - - Args: - input_value: GUI image file name - expected_results: Expected results (list of measures) - """ - # Build GUI image file path - gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value - ) - - # Read GUI image (PNG) - gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) - - # Execute segmentation - result_segments: Dict[str, Any] - _, result_segments = Segmentation.execute( - gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP - ) - - # Execute metric - result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( - gui_image_png_base64, - gui_segments=result_segments, - gui_type=GUI_TYPE_DESKTOP, - ) - - # Test result - if result is not None and all(isinstance(res, int) for res in result): - assert result == expected_results diff --git a/backend/tests/metrics/test_m26.py b/backend/tests/metrics/test_m26.py deleted file mode 100644 index 8396ff9..0000000 --- a/backend/tests/metrics/test_m26.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Tests for the 'White Space' metric (m26). -""" - - -# ---------------------------------------------------------------------------- -# Imports -# ---------------------------------------------------------------------------- - -# Standard library modules -import pathlib -from typing import Any, Dict, List, Optional, Tuple, Union - -# Third-party modules -import pytest - -# First-party modules -from aim.common import image_utils -from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE -from aim.metrics.m26.m26_white_space import Metric -from aim.segmentation.model import Segmentation -from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR - -# ---------------------------------------------------------------------------- -# Metadata -# ---------------------------------------------------------------------------- - -__author__ = "Amir Hossein Kargaran, Markku Laine" -__date__ = "2022-08-05" -__email__ = "markku.laine@aalto.fi" -__version__ = "1.0" - - -# ---------------------------------------------------------------------------- -# Tests -# ---------------------------------------------------------------------------- - - -@pytest.mark.parametrize( - ["input_value", "expected_results"], - [ - ("aalto.fi_website.png", [0.2663]), - ("myhelsinki.fi_website.png", [0.884]), - ("wikipedia.org_website.png", [0.3033]), - ("black.png", [1.0]), - ], -) -def test_white_space_desktop( - input_value: str, expected_results: List[Any] -) -> None: - """ - Test White Space (desktop GUIs). - - Args: - input_value: GUI image file name - expected_results: Expected results (list of measures) - """ - # Build GUI image file path - gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value - ) - - # Read GUI image (PNG) - gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) - - # Execute segmentation - result_segments: Dict[str, Any] - _, result_segments = Segmentation.execute( - gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP - ) - - # Execute metric - result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( - gui_image_png_base64, - gui_segments=result_segments, - gui_type=GUI_TYPE_DESKTOP, - ) - - # Test result - if result is not None and isinstance(result[0], float): - assert round(result[0], 4) == expected_results[0] From 4f789e9082b600480bf86f558975fbc8c3ff876d Mon Sep 17 00:00:00 2001 From: Amir Hossein Kargaran Date: Tue, 8 Nov 2022 02:24:16 +0100 Subject: [PATCH 03/23] feat(metric, evaluator): add evaluator support for segmentation --- backend/aim/metrics/m21/m21_grid_quality.py | 8 + backend/aim/segmentation/model.py | 6 +- backend/aim/tools.py | 19 +- .../evaluations/ALEXA_500/m21_0_histogram.png | Bin 0 -> 13443 bytes .../evaluations/ALEXA_500/m21_1_histogram.png | Bin 0 -> 14947 bytes .../evaluations/ALEXA_500/m21_2_histogram.png | Bin 0 -> 13947 bytes .../evaluations/ALEXA_500/m21_3_histogram.png | Bin 0 -> 16371 bytes .../evaluations/ALEXA_500/m21_4_histogram.png | Bin 0 -> 12194 bytes .../evaluations/ALEXA_500/m21_5_histogram.png | Bin 0 -> 14302 bytes .../evaluations/ALEXA_500/m21_6_histogram.png | Bin 0 -> 13300 bytes .../evaluations/ALEXA_500/m21_7_histogram.png | Bin 0 -> 14836 bytes .../evaluations/ALEXA_500/m21_8_histogram.png | Bin 0 -> 13755 bytes .../evaluations/ALEXA_500/m21_9_histogram.png | Bin 0 -> 16099 bytes .../evaluations/ALEXA_500/m22_0_histogram.png | Bin 0 -> 11704 bytes backend/tests/metrics/test_m21.py | 3 +- backend/tests/metrics/test_m22.py | 3 +- .../tests/segmentation/test_segmentation.py | 32 +- frontend/src/assets/results/m21_results.json | 5126 +++++++++++++++++ frontend/src/assets/results/m22_results.json | 1283 +++++ metrics.json | 200 +- 20 files changed, 6513 insertions(+), 167 deletions(-) create mode 100644 backend/data/evaluations/ALEXA_500/m21_0_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_1_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_2_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_3_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_4_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_5_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_6_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_7_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_8_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m21_9_histogram.png create mode 100644 backend/data/evaluations/ALEXA_500/m22_0_histogram.png create mode 100644 frontend/src/assets/results/m21_results.json create mode 100644 frontend/src/assets/results/m22_results.json diff --git a/backend/aim/metrics/m21/m21_grid_quality.py b/backend/aim/metrics/m21/m21_grid_quality.py index b266c91..4374c0b 100644 --- a/backend/aim/metrics/m21/m21_grid_quality.py +++ b/backend/aim/metrics/m21/m21_grid_quality.py @@ -546,6 +546,14 @@ def execute_metric( Results (list of measures) - number of visual GUI blocks (int, [0, +inf)) - number of visual GUI blocks - without children (int, [0, +inf)) + - number of alignment points (int, [0, +inf)) + - number of alignment points - without children (int, [0, +inf)) + - number of block sizes (int, [0, +inf)) + - number of block sizes - without children (int, [0, +inf)) + - GUI Coverage (float, [0, 1]) + - GUI Coverage - without children (float, [0, 1]) + - number of vertical block sizes (int, [0, +inf)) + - number of vertical block sizes - without children (int, [0, +inf)) """ # Get all elements diff --git a/backend/aim/segmentation/model.py b/backend/aim/segmentation/model.py index a06a6cb..78907ee 100644 --- a/backend/aim/segmentation/model.py +++ b/backend/aim/segmentation/model.py @@ -383,7 +383,7 @@ def execute( gui_image: str, gui_type: int = GUI_TYPE_DESKTOP, gui_url: Optional[HttpUrl] = None, - ) -> Tuple[str, Dict[str, Any]]: + ) -> Dict[str, Any]: # Create PIL image img: Image.Image = Image.open(BytesIO(base64.b64decode(gui_image))) @@ -440,5 +440,7 @@ def execute( board_im: Image.Image = Image.fromarray(board_rgb) # Convert board to b64 (str) board_b64: str = image_utils.to_png_image_base64(board_im) + # Save in component dictionary + components["img_b64"] = board_b64 - return board_b64, components + return components diff --git a/backend/aim/tools.py b/backend/aim/tools.py index 8b69ea8..3f705d4 100644 --- a/backend/aim/tools.py +++ b/backend/aim/tools.py @@ -39,6 +39,7 @@ METRICS_DIR, METRICS_FILE_PATTERN, ) +from aim.segmentation.model import Segmentation # ---------------------------------------------------------------------------- # Metadata @@ -298,12 +299,28 @@ def _execute_metrics(self): ) ) + # Execute segmentation + if self.metrics_configurations["metrics"][metric][ + "segmentation_required" + ]: + start_time: float = time.time() + result_segments: Dict[ + str, Any + ] = Segmentation.execute(image_png_base64) + end_time: float = time.time() + results_row["segmentation_time"] = round( + end_time - start_time, 4 + ) + else: + result_segments = None + # Execute metric start_time: float = time.time() metric_results: Optional[ List[Union[int, float, str]] ] = metric_module.Metric.execute_metric( - image_png_base64 + gui_image=image_png_base64, + gui_segments=result_segments, ) end_time: float = time.time() results_row[metric + "_time"] = round( diff --git a/backend/data/evaluations/ALEXA_500/m21_0_histogram.png b/backend/data/evaluations/ALEXA_500/m21_0_histogram.png new file mode 100644 index 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---------------------------------------------------------------------------- + +# Standard library modules +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party modules +import pytest + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.m23.m23_uied_segmentation import Metric +from aim.segmentation.model import Segmentation +from tests.common.constants import DATA_TESTS_INPUT_VALUES_DIR, IDIFF_TOLERANCE +from tests.common.utils import load_expected_result + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Tests +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("interfacemetrics_aalto.png", [load_expected_result("m23_0_interfacemetrics_aalto.png")]), + ], +) +def test_uied_segmentation_desktop( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test UIED Segmentation (desktop GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_DESKTOP, + ) + + # Test result + if ( + result is not None + and isinstance(result[0], str) + ): + assert ( + image_utils.idiff(result[0], expected_results[0]) + <= IDIFF_TOLERANCE + ) + + +@pytest.mark.parametrize( + ["input_value", "expected_results"], + [ + ("uied_mobile.png", [load_expected_result("m23_0_uied_mobile.png")]), + ], +) +def test_uied_segmentation_mobile( + input_value: str, expected_results: List[Any] +) -> None: + """ + Test UIED Segmentation (mobile GUIs). + + Args: + input_value: GUI image file name + expected_results: Expected results (list of measures) + """ + # Build GUI image file path + gui_image_filepath: pathlib.Path = ( + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + ) + + # Read GUI image (PNG) + gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) + + # Execute segmentation + result_segments: Dict[str, Any] = Segmentation.execute( + gui_image=gui_image_png_base64, gui_type=GUI_TYPE_MOBILE + ) + + # Execute metric + result: Optional[List[Union[int, float, str]]] = Metric.execute_metric( + gui_image_png_base64, + gui_segments=result_segments, + gui_type=GUI_TYPE_MOBILE, + ) + + # Test result + if ( + result is not None + and isinstance(result[0], str) + ): + assert ( + image_utils.idiff(result[0], expected_results[0]) + <= IDIFF_TOLERANCE + ) diff --git a/backend/tests/segmentation/test_segmentation.py b/backend/tests/segmentation/test_segmentation.py index 64e88b0..5d4d981 100644 --- a/backend/tests/segmentation/test_segmentation.py +++ b/backend/tests/segmentation/test_segmentation.py @@ -38,24 +38,22 @@ @pytest.mark.parametrize( - ["input_value", "expected_results"], + ["input_value"], [ - ("interfacemetrics_aalto.png", {}), + (["interfacemetrics_aalto.png"]), + (["black.png"]), ], ) -def test_segmentation_desktop( - input_value: str, expected_results: Dict[str, Any] -) -> None: +def test_segmentation_desktop(input_value: str) -> None: """ Test Segmentation (desktop GUIs). Args: input_value: GUI image file name - expected_results: Segmentation result (Dict) """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value ) # Read GUI image (PNG) @@ -68,28 +66,27 @@ def test_segmentation_desktop( # Test result if result is not None and isinstance(result, Dict): - assert True + assert ('segments' in result) == True + assert ('img_shape' in result) == True + assert ('img_b64' in result) == True @pytest.mark.parametrize( - ["input_value", "expected_results"], + ["input_value"], [ - ("uied_mobile.png", {}), + (["uied_mobile.png"]), ], ) -def test_segmentation_mobile( - input_value: str, expected_results: Dict[str, Any] -) -> None: +def test_segmentation_mobile(input_value: str) -> None: """ Test Segmentation (desktop GUIs). Args: input_value: GUI image file name - expected_results: Segmentation result (Dict) """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value ) # Read GUI image (PNG) @@ -102,4 +99,6 @@ def test_segmentation_mobile( # Test result if result is not None and isinstance(result, Dict): - assert True + assert ('segments' in result) == True + assert ('img_shape' in result) == True + assert ('img_b64' in result) == True diff --git a/frontend/src/assets/results/m23_results.json b/frontend/src/assets/results/m23_results.json new file mode 100644 index 0000000..9492f4c --- /dev/null +++ b/frontend/src/assets/results/m23_results.json @@ -0,0 +1,429 @@ +[ + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {} +] diff --git a/metrics.json b/metrics.json index a9e6a35..23eefd2 100644 --- a/metrics.json +++ b/metrics.json @@ -919,6 +919,34 @@ ] } ] + }, + "m23": { + "id": "m23", + "name": "UIED segmentation", + "description": "This UI Segmentation is method mostly based on old-fashioned computer vision approaches to detect UI components and texts.", + "evidence": 3, + "relevance": 3, + "speed": 1, + "segmentation_required": true, + "references": [ + { + "title": "Xie, M., Feng, S., Xing, Z., Chen, J., & Chen, C. (2020).UIED: a hybrid tool for GUI element detection. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 1655-1659. ACM. doi: https://doi.org/10.1145/3368089.3417940", + "url": "https://doi.org/10.1145/3368089.3417940" + }, + { + "title": "Chen, J., Xie, M., Xing, Z., Chen, C., Xu, X., Zhu, L., & Li, G. (2020). Object detection for graphical user interface: Old fashioned or deep learning or a combination?. In proceedings of the 28th ACM joint meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering pp. 1202-1214. ACM. doi: https://doi.org/10.1145/3368089.3409691", + "url": "https://doi.org/10.1145/3368089.3409691" + } + ], + "results": [ + { + "id": "m23_0", + "index": 0, + "type": "b64", + "name": "UIED segmented image", + "description": false + } + ] } } } From 172874741fe1f9ca47b1163cabb2cc3bf33f7e97 Mon Sep 17 00:00:00 2001 From: Amir Hossein Kargaran Date: Wed, 9 Nov 2022 16:36:21 +0100 Subject: [PATCH 07/23] run(pre-commit): :bug: fix assertion --- backend/tests/metrics/test_m23.py | 31 +++++++++---------- .../tests/segmentation/test_segmentation.py | 18 +++++------ 2 files changed, 23 insertions(+), 26 deletions(-) diff --git a/backend/tests/metrics/test_m23.py b/backend/tests/metrics/test_m23.py index 4fe6ae9..f580166 100644 --- a/backend/tests/metrics/test_m23.py +++ b/backend/tests/metrics/test_m23.py @@ -42,11 +42,14 @@ @pytest.mark.parametrize( ["input_value", "expected_results"], [ - ("interfacemetrics_aalto.png", [load_expected_result("m23_0_interfacemetrics_aalto.png")]), + ( + "interfacemetrics_aalto.png", + [load_expected_result("m23_0_interfacemetrics_aalto.png")], + ), ], ) def test_uied_segmentation_desktop( - input_value: str, expected_results: List[Any] + input_value: str, expected_results: List[Any] ) -> None: """ Test UIED Segmentation (desktop GUIs). @@ -57,7 +60,7 @@ def test_uied_segmentation_desktop( """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value ) # Read GUI image (PNG) @@ -76,13 +79,10 @@ def test_uied_segmentation_desktop( ) # Test result - if ( - result is not None - and isinstance(result[0], str) - ): + if result is not None and isinstance(result[0], str): assert ( - image_utils.idiff(result[0], expected_results[0]) - <= IDIFF_TOLERANCE + image_utils.idiff(result[0], expected_results[0]) + <= IDIFF_TOLERANCE ) @@ -93,7 +93,7 @@ def test_uied_segmentation_desktop( ], ) def test_uied_segmentation_mobile( - input_value: str, expected_results: List[Any] + input_value: str, expected_results: List[Any] ) -> None: """ Test UIED Segmentation (mobile GUIs). @@ -104,7 +104,7 @@ def test_uied_segmentation_mobile( """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value ) # Read GUI image (PNG) @@ -123,11 +123,8 @@ def test_uied_segmentation_mobile( ) # Test result - if ( - result is not None - and isinstance(result[0], str) - ): + if result is not None and isinstance(result[0], str): assert ( - image_utils.idiff(result[0], expected_results[0]) - <= IDIFF_TOLERANCE + image_utils.idiff(result[0], expected_results[0]) + <= IDIFF_TOLERANCE ) diff --git a/backend/tests/segmentation/test_segmentation.py b/backend/tests/segmentation/test_segmentation.py index 5d4d981..605484a 100644 --- a/backend/tests/segmentation/test_segmentation.py +++ b/backend/tests/segmentation/test_segmentation.py @@ -41,7 +41,7 @@ ["input_value"], [ (["interfacemetrics_aalto.png"]), - (["black.png"]), + (["black.png"]), ], ) def test_segmentation_desktop(input_value: str) -> None: @@ -53,7 +53,7 @@ def test_segmentation_desktop(input_value: str) -> None: """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value ) # Read GUI image (PNG) @@ -66,9 +66,9 @@ def test_segmentation_desktop(input_value: str) -> None: # Test result if result is not None and isinstance(result, Dict): - assert ('segments' in result) == True - assert ('img_shape' in result) == True - assert ('img_b64' in result) == True + assert "segments" in result + assert "img_shape" in result + assert "img_b64" in result @pytest.mark.parametrize( @@ -86,7 +86,7 @@ def test_segmentation_mobile(input_value: str) -> None: """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( - pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value + pathlib.Path(DATA_TESTS_INPUT_VALUES_DIR) / input_value ) # Read GUI image (PNG) @@ -99,6 +99,6 @@ def test_segmentation_mobile(input_value: str) -> None: # Test result if result is not None and isinstance(result, Dict): - assert ('segments' in result) == True - assert ('img_shape' in result) == True - assert ('img_b64' in result) == True + assert "segments" in result + assert "img_shape" in result + assert "img_b64" in result From 6c5ea17941ce22f2e35b375e5691582b21fe661c Mon Sep 17 00:00:00 2001 From: Amir Hossein Kargaran Date: Wed, 9 Nov 2022 21:57:30 +0100 Subject: [PATCH 08/23] feat(metric): add AIM Legacy segmentation --- backend/aim/metrics/m24/__init__.py | 0 .../m24/m24_aim_legacy_segmentation.py | 271 +++++++++++ backend/aim/metrics/m24/utils.py | 112 +++++ backend/aim/segmentation/image_seg_utils.py | 2 +- backend/aim/segmentation/merge_seg_utils.py | 2 +- backend/aim/segmentation/model.py | 2 +- .../m23_0_interfacemetrics_aalto.png | Bin 188186 -> 194133 bytes .../expected_results/m23_0_uied_mobile.png | Bin 182535 -> 193114 bytes .../m24_0_interfacemetrics_aalto.png | Bin 0 -> 193137 bytes .../expected_results/m24_0_uied_mobile.png | Bin 0 -> 194527 bytes backend/tests/metrics/test_m24.py | 117 +++++ frontend/src/assets/results/m24_results.json | 429 ++++++++++++++++++ metrics.json | 24 + 13 files changed, 956 insertions(+), 3 deletions(-) create mode 100644 backend/aim/metrics/m24/__init__.py create mode 100644 backend/aim/metrics/m24/m24_aim_legacy_segmentation.py create mode 100644 backend/aim/metrics/m24/utils.py create mode 100644 backend/data/tests/expected_results/m24_0_interfacemetrics_aalto.png create mode 100644 backend/data/tests/expected_results/m24_0_uied_mobile.png create mode 100644 backend/tests/metrics/test_m24.py create mode 100644 frontend/src/assets/results/m24_results.json diff --git a/backend/aim/metrics/m24/__init__.py b/backend/aim/metrics/m24/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m24/m24_aim_legacy_segmentation.py b/backend/aim/metrics/m24/m24_aim_legacy_segmentation.py new file mode 100644 index 0000000..8ea8a68 --- /dev/null +++ b/backend/aim/metrics/m24/m24_aim_legacy_segmentation.py @@ -0,0 +1,271 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + AIM Legacy Segmentation + + +Description: + AIM Legacy Segmentation. Tool for segmentation of images. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Oulasvirta, A., De Pascale, S., Koch, J., ... & Weinkauf, T. (2018). + Aalto Interface Metrics (AIM) A Service and Codebase for Computational + GUI Evaluation. In The 31st Annual ACM Symposium on User Interface Software + and Technology Adjunct Proceedings, pp. 16-19. + doi: https://doi.org/10.1145/2702123.2702575 + + +Change log: + v2.0 (2022-08-05) + * Revised implementation + + v1.0.1 (2018-10-18) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import base64 +from io import BytesIO +from typing import Any, Dict, List, Optional, Union + +# Third-party modules +import cv2 +import numpy as np +from PIL import Image +from pydantic import HttpUrl +from skimage import img_as_ubyte +from skimage.feature import canny +from skimage.filters import rank +from skimage.morphology import disk + +# First-party modules +from aim.common import image_utils +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface +from aim.metrics.m24 import utils + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine, Khushhall Chandra Mahajan, Janin Koch, Samuli De Pascale" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "2.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: AIM Legacy Segmentation. + """ + + # Private constants + _H_BLUR: int = 13 # 11 # Horizontal Blurring filter + _V_BLUR: int = 9 # 13 # Vertical Blurring filter + _H_THRESHOLD_MIN_SIZE: int = 10 # Minimum acceptable height of element + _W_THRESHOLD_MIN_SIZE: int = 15 # Minimum acceptable width of element + _DETAILED: bool = True + _SHOW: bool = False + _BOUNDING_BOX: bool = False # Bounding Box + + # Private methods + @classmethod + def segment(cls, img_bgr: np.ndarray): + + BW = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) + + ret, thresh = cv2.threshold( + BW, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU + ) + + denoised = rank.median(BW, disk(5)) + gradient_denoised = rank.gradient(denoised, disk(1)) + + gradient_0 = rank.gradient(img_bgr[:, :, 0], disk(1)) + gradient_1 = rank.gradient(img_bgr[:, :, 1], disk(1)) + gradient_2 = rank.gradient(img_bgr[:, :, 2], disk(1)) + + sobelx64f = cv2.Sobel(BW, cv2.CV_64F, 1, 0, ksize=5) + abs_sobel64f = np.absolute(sobelx64f) + sobel_8u = np.uint8(abs_sobel64f) + img_canny = canny(BW) + + contours_thresh, _ = cv2.findContours( + thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + contours_0, _ = cv2.findContours( + gradient_0, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + contours_1, _ = cv2.findContours( + gradient_1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + contours_2, _ = cv2.findContours( + gradient_2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + contours_denoised, _ = cv2.findContours( + gradient_denoised, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + contours_sobel, _ = cv2.findContours( + sobel_8u, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + contours_canny, _ = cv2.findContours( + img_as_ubyte(img_canny), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + + contours = ( + contours_0 + + contours_1 + + contours_2 + + contours_denoised + + contours_sobel + + contours_canny + ) + + temp = np.zeros_like(BW) + if cls._BOUNDING_BOX: + bbox = utils.remove_overlaps(contours) + for bb in bbox: + temp = cv2.rectangle( + temp, (bb.x1, bb.y1), (bb.x2, bb.y2), (255, 255, 255), 1 + ) + + for c in contours_thresh: + x, y, w, h = cv2.boundingRect(c) + temp = cv2.rectangle( + temp, (x, y), (x + w, y + h), (255, 255, 255), 1 + ) + + # Horizontal Blurring filter + size: int = cls._H_BLUR + kmb: np.ndarray = np.zeros((size, size)) + kmb[int(size / 2), :] = np.ones(size) + kmb = kmb / size + + # Apply horizontal blurring here + temp = cv2.filter2D(temp, -1, kmb) + contours_all_h, _ = cv2.findContours( + temp, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + + # Vertical Blurring filter + size = cls._V_BLUR + kmb = np.zeros((size, size)) + kmb[:, int(size / 2)] = np.ones(size) + kmb = kmb / size + + # Apply vertical blurring here + temp = cv2.filter2D(temp, -1, kmb) + contours_all_v, _ = cv2.findContours( + temp, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + + return contours_all_v, contours_all_h + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - AIM Legacy segmented image (str, image (PNG) encoded in Base64) + """ + + # Create PIL image + img: Image.Image = Image.open(BytesIO(base64.b64decode(gui_image))) + + # Convert image from ??? (should be RGBA) to RGB color space + img_rgb: Image.Image = img.convert("RGB") + img_rgb_nparray: np.ndarray = np.array(img_rgb) + img_bgr_nparray = cv2.cvtColor(img_rgb_nparray, cv2.COLOR_RGB2BGR) + img_out_bgr_nparray = np.copy(img_bgr_nparray) + + contours_all_v, contours_all_h = cls.segment(img_bgr_nparray) + + thickness = 1 + if cls._DETAILED: + contours_all = contours_all_h + else: + contours_all = contours_all_v + thickness = 2 + + elements = [] + for i, c in enumerate(contours_all): + x, y, w, h = cv2.boundingRect(c) + + if w <= cls._W_THRESHOLD_MIN_SIZE: + continue + if h <= cls._H_THRESHOLD_MIN_SIZE: + continue + + img_out_bgr_nparray = cv2.rectangle( + img_out_bgr_nparray, + (x, y), + (x + w, y + h), + (0, 0, 255), + thickness, + ) + + elements.append( + { + "id": i, + "x_position": x, + "y_position": y, + "width": w, + "height": h, + } + ) + + if cls._SHOW: + cv2.imshow("Output", img_out_bgr_nparray) + cv2.waitKey(0) + cv2.destroyAllWindows() + + # Convert output segmented array Pillow Image + img_out_rgb_nparray: np.ndarray = cv2.cvtColor( + img_out_bgr_nparray, cv2.COLOR_BGR2RGB + ) + img_out: Image.Image = Image.fromarray(img_out_rgb_nparray) + + # Convert output segmented image to b64 (str) + img_b64: str = image_utils.to_png_image_base64(img_out) + + # Result + result: Dict[str, Any] = {"segments": elements, "img_shape": list(img_out_rgb_nparray.shape), "img_b64": img_b64} + + return [ + result["img_b64"], + ] diff --git a/backend/aim/metrics/m24/utils.py b/backend/aim/metrics/m24/utils.py new file mode 100644 index 0000000..008d241 --- /dev/null +++ b/backend/aim/metrics/m24/utils.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +AIM Legacy Segmentation functions. +""" + + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import base64 + +# Third-party modules +import cv2 +import numpy as np +import scipy.spatial as spatial + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine, Khushhall Chandra Mahajan, Janin Koch, Samuli De Pascale" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "2.0" + + +# ---------------------------------------------------------------------------- +# Image utility functions +# ---------------------------------------------------------------------------- + + +class BBox(object): + def __init__(self, x1, y1, x2, y2): + # (x1, y1) is the upper left corner, + # (x2, y2) is the lower right corner, + if x1 > x2: + x1, x2 = x2, x1 + if y1 > y2: + y1, y2 = y2, y1 + self.x1 = x1 + self.y1 = y1 + self.x2 = x2 + self.y2 = y2 + + def taxicab_diagonal(self): + return self.x2 - self.x1 + self.y2 - self.y1 + + def overlaps(self, other): + # Return True if self and other overlap. + return not ( + (self.x1 > other.x2) + or (self.x2 < other.x1) + or (self.y1 > other.y2) + or (self.y2 < other.y1) + ) + + +def makeClassFromCont(contours): + bboxes = [] + for c in contours: + x, y, w, h = cv2.boundingRect(c) + if w < 5 or h < 5: + continue + bboxes.append(BBox(x, y, x + w, y + h)) + return bboxes + + +def remove_overlaps(contours): + # This function returns a set of bboxes after removing the overlapping contours + bboxes = makeClassFromCont(contours) + + corners = [] + ulcorners = [] + + # dict mapping corners to Bboxes. + bbox_map = {} + + for bbox in bboxes: + ul = (bbox.x1, bbox.y1) + lr = (bbox.x2, bbox.y2) + bbox_map[ul] = bbox + bbox_map[lr] = bbox + ulcorners.append(ul) + corners.append(ul) + corners.append(lr) + + try: + tree = spatial.KDTree(np.asarray(corners)) + except Exception: + return 0 + + for corner in ulcorners: + bbox = bbox_map[corner] + + # Find all points which are within a taxicab distance of corner + indices = tree.query_ball_point( + corner, bbox_map[corner].taxicab_diagonal(), p=1 + ) + for near_corner in tree.data[indices]: + near_bbox = bbox_map[tuple(near_corner)] + if bbox != near_bbox and bbox.overlaps(near_bbox): + # Expand both the bboxes + bbox.x1 = near_bbox.x1 = min(bbox.x1, near_bbox.x1) + bbox.y1 = near_bbox.y1 = min(bbox.y1, near_bbox.y1) + bbox.x2 = near_bbox.x2 = max(bbox.x2, near_bbox.x2) + bbox.y2 = near_bbox.y2 = max(bbox.y2, near_bbox.y2) + + return set(bbox_map.values()) diff --git a/backend/aim/segmentation/image_seg_utils.py b/backend/aim/segmentation/image_seg_utils.py index 6981757..09275db 100644 --- a/backend/aim/segmentation/image_seg_utils.py +++ b/backend/aim/segmentation/image_seg_utils.py @@ -79,7 +79,7 @@ def draw_bounding_box( org, components, color=(0, 255, 0), - line=2, + line=1, show=False, write_path=None, name="board", diff --git a/backend/aim/segmentation/merge_seg_utils.py b/backend/aim/segmentation/merge_seg_utils.py index 598bf0f..c0f934f 100644 --- a/backend/aim/segmentation/merge_seg_utils.py +++ b/backend/aim/segmentation/merge_seg_utils.py @@ -159,7 +159,7 @@ def visualize_element(self, img, color, line, show=False): def show_elements( - org_img, eles, show=False, win_name="Element", line=2, write_path=None + org_img, eles, show=False, win_name="Element", line=1, write_path=None ): color_map = { "Text": (0, 0, 255), diff --git a/backend/aim/segmentation/model.py b/backend/aim/segmentation/model.py index 6d8c0eb..edd836c 100644 --- a/backend/aim/segmentation/model.py +++ b/backend/aim/segmentation/model.py @@ -284,7 +284,7 @@ def text_detection( texts: List[Text] = text_cvt_orc_format_paddle(paddle_result) visualize_texts( - img, texts, color=(0, 0, 255), line=2, write_path=None, show=show + img, texts, color=(0, 0, 255), line=1, write_path=None, show=show ) text_json: Dict = text2json(texts, img.shape) return text_json diff --git a/backend/data/tests/expected_results/m23_0_interfacemetrics_aalto.png 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str = image_utils.read_image(gui_image_filepath) # Execute segmentation - result_segments: Dict[str, Any] - _, result_segments = Segmentation.execute( + result_segments: Dict[str, Any] = Segmentation.execute( gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP ) diff --git a/backend/tests/segmentation/test_segmentation.py b/backend/tests/segmentation/test_segmentation.py index d65b604..64e88b0 100644 --- a/backend/tests/segmentation/test_segmentation.py +++ b/backend/tests/segmentation/test_segmentation.py @@ -40,18 +40,18 @@ @pytest.mark.parametrize( ["input_value", "expected_results"], [ - ("interfacemetrics_aalto.png", ["", {}]), + ("interfacemetrics_aalto.png", {}), ], ) def test_segmentation_desktop( - input_value: str, expected_results: List[Any] + input_value: str, expected_results: Dict[str, Any] ) -> None: """ Test Segmentation (desktop GUIs). Args: input_value: GUI image file name - expected_results: Expected results (List of Segmentation results (str and Dict)) + expected_results: Segmentation result (Dict) """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( @@ -61,35 +61,31 @@ def test_segmentation_desktop( # Read GUI image (PNG) gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) - # Execute metric - result: Tuple[str, Dict[str, Any]] = Segmentation.execute( + # Execute segmentation + result: Dict[str, Any] = Segmentation.execute( gui_image=gui_image_png_base64, gui_type=GUI_TYPE_DESKTOP ) # Test result - if ( - result is not None - and isinstance(result[0], str) - and isinstance(result[1], Dict) - ): + if result is not None and isinstance(result, Dict): assert True @pytest.mark.parametrize( ["input_value", "expected_results"], [ - ("uied_mobile.png", ["", {}]), + ("uied_mobile.png", {}), ], ) def test_segmentation_mobile( - input_value: str, expected_results: List[Any] + input_value: str, expected_results: Dict[str, Any] ) -> None: """ Test Segmentation (desktop GUIs). Args: input_value: GUI image file name - expected_results: Expected results (List of Segmentation results (str and Dict)) + expected_results: Segmentation result (Dict) """ # Build GUI image file path gui_image_filepath: pathlib.Path = ( @@ -99,15 +95,11 @@ def test_segmentation_mobile( # Read GUI image (PNG) gui_image_png_base64: str = image_utils.read_image(gui_image_filepath) - # Execute metric - result: Tuple[str, Dict[str, Any]] = Segmentation.execute( + # Execute segmentation + result: Dict[str, Any] = Segmentation.execute( gui_image=gui_image_png_base64, gui_type=GUI_TYPE_MOBILE ) # Test result - if ( - result is not None - and isinstance(result[0], str) - and isinstance(result[1], Dict) - ): + if result is not None and isinstance(result, Dict): assert True diff --git a/frontend/src/assets/results/m21_results.json b/frontend/src/assets/results/m21_results.json new file mode 100644 index 0000000..d0d6896 --- /dev/null +++ b/frontend/src/assets/results/m21_results.json @@ -0,0 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true, "references": [ @@ -580,21 +580,21 @@ "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 18], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [19, 34], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [35, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" @@ -610,72 +610,51 @@ "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 14], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [15, 28], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [29, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" } ] - } - ] - }, - "m22": { - "id": "m22", - "name": "Number of Alignment Points of Blocks", - "description": "The number of alignment points of GUI blocks, with and without considering children blocks.", - "evidence": 1, - "relevance": 1, - "speed": 2, - "segmentation_required": true, - "references": [ - { - "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", - "url": "https://doi.org/10.1145/2702123.2702575" }, { - "title": "Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. In Digital publishing (Vol. 6076, pp. 52-63). SPIE. doi: https://doi.org/10.1117/12.642120", - "url": "https://doi.org/10.1117/12.642120" - } - ], - "results": [ - { - "id": "m22_0", - "index": 0, + "id": "m21_2", + "index": 2, "type": "int", "name": "Number of Alignment Points of Blocks", "description": false, "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 52], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [53, 87], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [88, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" @@ -683,80 +662,59 @@ ] }, { - "id": "m22_1", - "index": 1, + "id": "m21_3", + "index": 3, "type": "int", "name": "Number of Alignment Points of Blocks (W/O Children)", "description": false, "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 43], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [44, 77], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [78, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" } ] - } - ] - }, - "m23": { - "id": "m23", - "name": "Number of Block Sizes", - "description": "The number of block sizes, grid proportionality, with and without considering children blocks.", - "evidence": 1, - "relevance": 1, - "speed": 2, - "segmentation_required": true, - "references": [ - { - "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", - "url": "https://doi.org/10.1145/2702123.2702575" }, { - "title": "Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. In Digital publishing (Vol. 6076, pp. 52-63). SPIE. doi: https://doi.org/10.1117/12.642120", - "url": "https://doi.org/10.1117/12.642120" - } - ], - "results": [ - { - "id": "m23_0", - "index": 0, + "id": "m21_4", + "index": 4, "type": "int", "name": "Number of Block Sizes", "description": false, "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 14], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [15, 22], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [23, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" @@ -764,80 +722,59 @@ ] }, { - "id": "m23_1", - "index": 1, + "id": "m21_5", + "index": 5, "type": "int", "name": "Number of Block Sizes (W/O Children)", "description": false, "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 12], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [13, 19], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [20, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" } ] - } - ] - }, - "m24": { - "id": "m24", - "name": "GUI Coverage", - "description": "The proportion of GUI covered by same-size blocks, with and without considering children blocks.", - "evidence": 1, - "relevance": 1, - "speed": 2, - "segmentation_required": true, - "references": [ - { - "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", - "url": "https://doi.org/10.1145/2702123.2702575" }, { - "title": "Balinsky, H. (2006). Evaluating interface aesthetics: measure of symmetry. In Digital publishing (Vol. 6076, pp. 52-63). SPIE. doi: https://doi.org/10.1117/12.642120", - "url": "https://doi.org/10.1117/12.642120" - } - ], - "results": [ - { - "id": "m24_0", - "index": 0, + "id": "m21_6", + "index": 6, "type": "float", "name": "GUI Coverage", "description": false, "scores": [ { "id": "r1", - "range": [0, 0.1], + "range": [0, 0.0084], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [0.2, 0.6], + "range": [0.0085, 0.1366], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [0.7, null], + "range": [0.1367, 1], "description": "Meaningless", "icon": [null, null], "judgment": "normal" @@ -845,76 +782,59 @@ ] }, { - "id": "m24_1", - "index": 1, + "id": "m21_7", + "index": 7, "type": "float", "name": "GUI Coverage (W/O Children)", "description": false, "scores": [ { "id": "r1", - "range": [0, 0.1], + "range": [0, 0.0044], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [0.2, 0.6], + "range": [0.0045, 0.1155], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [0.7, null], + "range": [0.1156, 1], "description": "Meaningless", "icon": [null, null], "judgment": "normal" } ] - } - ] - }, - "m25": { - "id": "m25", - "name": "Number of Vertical Block Sizes", - "description": "The number of vertical block sizes, i.e., vertical grid proportionality., with and without considering children blocks.", - "evidence": 1, - "relevance": 1, - "speed": 2, - "segmentation_required": true, - "references": [ - { - "title": "Miniukovich, A. and De Angeli, A. (2015). Computation of Interface Aesthetics. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), pp. 1163-1172. ACM. doi: https://doi.org/10.1145/2702123.2702575", - "url": "https://doi.org/10.1145/2702123.2702575" - } - ], - "results": [ + }, { - "id": "m25_0", - "index": 0, + "id": "m21_8", + "index": 8, "type": "int", "name": "Number of Different Blocks Height", "description": false, "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 9], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [10, 14], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [15, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" @@ -922,29 +842,29 @@ ] }, { - "id": "m25_1", - "index": 1, + "id": "m21_9", + "index": 9, "type": "int", "name": "Number of Different Blocks Height (W/O Children)", "description": false, "scores": [ { "id": "r1", - "range": [0, 2], + "range": [0, 7], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [3, 6], + "range": [8, 11], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [7, null], + "range": [12, null], "description": "Meaningless", "icon": [null, null], "judgment": "normal" @@ -953,12 +873,12 @@ } ] }, - "m26": { - "id": "m26", + "m22": { + "id": "m22", "name": "White Space", - "description": "White Space. Non covered portion of GUI with visual blocks.", - "evidence": 1, - "relevance": 1, + "description": "White Space. Non-covered portion of GUI with visual blocks.", + "evidence": 2, + "relevance": 4, "speed": 2, "segmentation_required": true, "references": [ @@ -969,7 +889,7 @@ ], "results": [ { - "id": "m26_0", + "id": "m22_0", "index": 0, "type": "float", "name": "White Space", @@ -977,21 +897,21 @@ "scores": [ { "id": "r1", - "range": [0, 0.1], + "range": [0, 0.3402], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r2", - "range": [0.2, 0.6], + "range": [0.3403, 0.6366], "description": "Meaningless", "icon": [null, null], "judgment": "normal" }, { "id": "r3", - "range": [0.7, null], + "range": [0.6367, 1], "description": "Meaningless", "icon": [null, null], "judgment": "normal" From e211ce335f1c2053658bf76d898c0b378cc93a94 Mon Sep 17 00:00:00 2001 From: Amir Hossein Kargaran Date: Tue, 8 Nov 2022 22:14:58 +0100 Subject: [PATCH 04/23] feat: support segmentation metrics execution --- backend/aim/handlers.py | 20 +++++++ backend/aim/segmentation/Readme.md | 87 ++++++++++++++++++++++++++++++ backend/aim/tools.py | 35 ++++++------ 3 files changed, 127 insertions(+), 15 deletions(-) diff --git a/backend/aim/handlers.py b/backend/aim/handlers.py index ca474b5..9e80c60 100644 --- a/backend/aim/handlers.py +++ b/backend/aim/handlers.py @@ -42,6 +42,7 @@ ) from aim.exceptions import ValidationError from aim.models import MessageBase, MessageImage, MessageInput, MessageURL +from aim.segmentation.model import Segmentation from aim.tools import Screenshot # ---------------------------------------------------------------------------- @@ -147,6 +148,24 @@ def on_message(self, message: Union[str, bytes]): } ) + # Execute segmentation + # Check if segmnetation is needed + metrics_configurations: Dict[ + str, Any + ] = utils.load_metrics_configurations() + metrics_conf_segmnetation: List = [ + metrics_configurations["metrics"][metric][ + "segmentation_required" + ] + for metric in msg.metrics.keys() + ] + if any(item is True for item in metrics_conf_segmnetation): + result_segments: Optional[ + Dict[str, Any] + ] = Segmentation.execute(png_image_base64) + else: + result_segments = None + # Iterate over selected metrics and execute them one by one for metric in {k: v for k, v in msg.metrics.items()}: logger.debug("Executing metric {}...".format(metric)) @@ -176,6 +195,7 @@ def on_message(self, message: Union[str, bytes]): results: Optional[List[Union[int, float, str]]] = metric_module.Metric.execute_metric( # type: ignore png_image_base64, gui_type=GUI_TYPE_DESKTOP, + gui_segments=result_segments, gui_url=msg.url, ) end_time: float = time.time() diff --git a/backend/aim/segmentation/Readme.md b/backend/aim/segmentation/Readme.md index e69de29..5d1e3cc 100644 --- a/backend/aim/segmentation/Readme.md +++ b/backend/aim/segmentation/Readme.md @@ -0,0 +1,87 @@ +# UI Segmentation and Element Detection +## A sample segmentation output +```json +{ + "segments": [ + { + "id": 0, + "class": "Component", + "subclass": null, + "height": 29, + "width": 51, + "position": { + "column_min": 88, + "row_min": 86, + "column_max": 139, + "row_max": 115 + } + }, + { + "id": 1, + "class": "Block", + "subclass": null, + "height": 189, + "width": 300, + "position": { + "column_min": 116, + "row_min": 206, + "column_max": 416, + "row_max": 395 + }, + "children": [ + 2, + 4 + ] + }, + { + "id": 2, + "class": "Component", + "subclass": null, + "height": 22, + "width": 205, + "position": { + "column_min": 158, + "row_min": 240, + "column_max": 363, + "row_max": 262 + }, + "parent": 1 + }, + { + "id": 3, + "class": "Text", + "subclass": null, + "height": 35, + "width": 44, + "position": { + "column_min": 319, + "row_min": 72, + "column_max": 363, + "row_max": 107 + }, + "text_content": "Ui" + }, + { + "id": 4, + "class": "Text", + "subclass": null, + "height": 14, + "width": 171, + "position": { + "column_min": 160, + "row_min": 214, + "column_max": 331, + "row_max": 228 + }, + "text_content": "AIM - Aalto Interface Metrics service", + "parent": 1 + } + ], + "img_shape": [ + 800, + 1280, + 3 + ], + "img_b64": "iV...I=" +} +``` \ No newline at end of file diff --git a/backend/aim/tools.py b/backend/aim/tools.py index 3f705d4..8606a01 100644 --- a/backend/aim/tools.py +++ b/backend/aim/tools.py @@ -277,6 +277,26 @@ def _execute_metrics(self): end_time - start_time, 4 ) + # Execute segmentation + # Check if segmnetation is needed + metrics_conf_segmnetation: List = [ + self.metrics_configurations["metrics"][metric][ + "segmentation_required" + ] + for metric in self.metrics + ] + if any(item is True for item in metrics_conf_segmnetation): + start_time: float = time.time() + result_segments: Optional[ + Dict[str, Any] + ] = Segmentation.execute(image_png_base64) + end_time: float = time.time() + results_row["segmentation_time"] = round( + end_time - start_time, 4 + ) + else: + result_segments = None + # Iterate over selected metrics for metric in self.metrics: # Locate metric implementation @@ -299,21 +319,6 @@ def _execute_metrics(self): ) ) - # Execute segmentation - if self.metrics_configurations["metrics"][metric][ - "segmentation_required" - ]: - start_time: float = time.time() - result_segments: Dict[ - str, Any - ] = Segmentation.execute(image_png_base64) - end_time: float = time.time() - results_row["segmentation_time"] = round( - end_time - start_time, 4 - ) - else: - result_segments = None - # Execute metric start_time: float = time.time() metric_results: Optional[ From 02a9ebb56e2e349e5a69c57cc95385bb0839aede Mon Sep 17 00:00:00 2001 From: Amir Hossein Kargaran Date: Tue, 8 Nov 2022 22:31:59 +0100 Subject: [PATCH 05/23] update(dependency): add paddleocr --- backend/aim/segmentation/model.py | 4 +++- backend/requirements.txt | 3 +++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/backend/aim/segmentation/model.py b/backend/aim/segmentation/model.py index 78907ee..073ad0c 100644 --- a/backend/aim/segmentation/model.py +++ b/backend/aim/segmentation/model.py @@ -51,7 +51,6 @@ from paddleocr import PaddleOCR from PIL import Image from pydantic import HttpUrl -from tensorflow.keras.models import load_model # First-party modules from aim.common import image_utils @@ -180,6 +179,9 @@ class Segmentation: "TextView", ] if _IS_CLF: + # Third-party modules + from tensorflow.keras.models import load_model + _CNN_MODEL = load_model(_CNN_MODEL_PATH) # Private methods diff --git a/backend/requirements.txt b/backend/requirements.txt index ebaf371..fae80a0 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -56,6 +56,9 @@ opencv-python==4.5.3.56 opt-einsum==3.3.0 outcome==1.1.0 packaging==21.3 +paddle-bfloat==0.1.7 +paddleocr==2.6.1.0 +paddlepaddle==2.3.2 pandas==1.3.3 parso==0.8.3 pathspec==0.9.0 From 6e4ab2b2b971863a00d9105f7945eb2b061a27a7 Mon Sep 17 00:00:00 2001 From: Amir Hossein Kargaran Date: Wed, 9 Nov 2022 16:13:52 +0100 Subject: [PATCH 06/23] feat(metric): add UIED segmentation metric --- backend/aim/metrics/m23/__init__.py | 0 .../aim/metrics/m23/m23_uied_segmentation.py | 111 +++++ backend/aim/segmentation/model.py | 2 +- .../m23_0_interfacemetrics_aalto.png | Bin 0 -> 188186 bytes .../expected_results/m23_0_uied_mobile.png | Bin 0 -> 182535 bytes backend/tests/metrics/test_m23.py | 133 ++++++ .../tests/segmentation/test_segmentation.py | 31 +- frontend/src/assets/results/m23_results.json | 429 ++++++++++++++++++ metrics.json | 28 ++ 9 files changed, 717 insertions(+), 17 deletions(-) create mode 100644 backend/aim/metrics/m23/__init__.py create mode 100644 backend/aim/metrics/m23/m23_uied_segmentation.py create mode 100644 backend/data/tests/expected_results/m23_0_interfacemetrics_aalto.png create mode 100644 backend/data/tests/expected_results/m23_0_uied_mobile.png create mode 100644 backend/tests/metrics/test_m23.py create mode 100644 frontend/src/assets/results/m23_results.json diff --git a/backend/aim/metrics/m23/__init__.py b/backend/aim/metrics/m23/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/aim/metrics/m23/m23_uied_segmentation.py b/backend/aim/metrics/m23/m23_uied_segmentation.py new file mode 100644 index 0000000..abd74fd --- /dev/null +++ b/backend/aim/metrics/m23/m23_uied_segmentation.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Metric: + UIED Segmentation + + +Description: + This UI Segmentation is method mostly based on old-fashioned computer vision approaches to detect UI components + and texts. Model code is imported from https://github.com/MulongXie/UIED. + + +Funding information and contact: + This work was funded by Technology Industries of Finland in a three-year + project grant on self-optimizing web services. The principal investigator + is Antti Oulasvirta (antti.oulasvirta@aalto.fi) of Aalto University. + + +References: + 1. Xie, M., Feng, S., Xing, Z., Chen, J., & Chen, C. (2020).UIED: a hybrid tool for GUI element detection. + In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium + on the Foundations of Software Engineering, pp. 1655-1659. ACM. + doi: https://doi.org/10.1145/3368089.3417940 + + 2. Chen, J., Xie, M., Xing, Z., Chen, C., Xu, X., Zhu, L., & Li, G. (2020). Object detection for graphical user + interface: Old fashioned or deep learning or a combination?. In proceedings of the 28th ACM joint meeting on + European Software Engineering Conference and Symposium on the Foundations of Software Engineering pp. 1202-1214. + ACM. + doi: https://doi.org/10.1145/3368089.3409691 + + + +Change log: + v1.0 (2022-08-05) + * Initial implementation +""" + +# ---------------------------------------------------------------------------- +# Imports +# ---------------------------------------------------------------------------- + +# Standard library modules +import base64 +from io import BytesIO +from typing import Any, Dict, List, Optional, Union + +# Third-party modules +from PIL import Image +from pydantic import HttpUrl + +# First-party modules +from aim.common.constants import GUI_TYPE_DESKTOP, GUI_TYPE_MOBILE +from aim.metrics.interfaces import AIMMetricInterface + +# ---------------------------------------------------------------------------- +# Metadata +# ---------------------------------------------------------------------------- + +__author__ = "Amir Hossein Kargaran, Markku Laine" +__date__ = "2022-08-05" +__email__ = "markku.laine@aalto.fi" +__version__ = "1.0" + + +# ---------------------------------------------------------------------------- +# Metric +# ---------------------------------------------------------------------------- + + +class Metric(AIMMetricInterface): + """ + Metric: UIED Segmentation. + """ + + # Public methods + @classmethod + def execute_metric( + cls, + gui_image: str, + gui_type: int = GUI_TYPE_DESKTOP, + gui_segments: Optional[Dict[str, Any]] = None, + gui_url: Optional[HttpUrl] = None, + ) -> Optional[List[Union[int, float, str]]]: + """ + Execute the metric. + + Args: + gui_image: GUI image (PNG) encoded in Base64 + + Kwargs: + gui_type: GUI type, desktop = 0 (default), mobile = 1 + gui_segments: GUI segmentation (defaults to None) + gui_url: GUI URL (defaults to None) + + Returns: + Results (list of measures) + - UIED segmented image (str, image (PNG) encoded in Base64) + """ + + # Get all elements + if gui_segments is not None: + segmented_im_b64: str = gui_segments["img_b64"] + else: + raise ValueError( + "This Metric requires gui_segments to be not None" + ) + + return [ + segmented_im_b64, + ] diff --git a/backend/aim/segmentation/model.py b/backend/aim/segmentation/model.py index 073ad0c..6d8c0eb 100644 --- a/backend/aim/segmentation/model.py +++ b/backend/aim/segmentation/model.py @@ -7,7 +7,7 @@ Description: - The UI Segmentation is mostly based on old-fashioned computer vision approaches to detect UI components and texts. + This UI Segmentation is mostly based on old-fashioned computer vision approaches to detect UI components and texts. 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