oi/jfgl/ikhRSGBoh;iRSGoh;

GRIP/2019gaberoom.grip

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+<grip:Pipeline>
+  <sources>
+    <grip:Camera>
+      <property name="deviceNumber" value="2"/>
+    </grip:Camera>
+  </sources>
+  <steps>
+    <grip:Step name="HSL Threshold">
+      <grip:Input step="0" socket="0"/>
+      <grip:Input step="0" socket="1">
+        <value>
+          <double>1.0791356615025363</double>
+          <double>74.53925305259105</double>
+        </value>
+      </grip:Input>
+      <grip:Input step="0" socket="2">
+        <value>
+          <double>30.575547587099685</double>
+          <double>123.00341850254723</double>
+        </value>
+      </grip:Input>
+      <grip:Input step="0" socket="3">
+        <value>
+          <double>214.02876531477455</double>
+          <double>255.0</double>
+        </value>
+      </grip:Input>
+      <grip:Output step="0" socket="0" previewed="false"/>
+    </grip:Step>
+    <grip:Step name="Find Contours">
+      <grip:Input step="1" socket="0"/>
+      <grip:Input step="1" socket="1">
+        <value>false</value>
+      </grip:Input>
+      <grip:Output step="1" socket="0" previewed="false"/>
+    </grip:Step>
+    <grip:Step name="Filter Contours">
+      <grip:Input step="2" socket="0"/>
+      <grip:Input step="2" socket="1">
+        <value>100.0</value>
+      </grip:Input>
+      <grip:Input step="2" socket="2">
+        <value>0</value>
+      </grip:Input>
+      <grip:Input step="2" socket="3">
+        <value>0</value>
+      </grip:Input>
+      <grip:Input step="2" socket="4">
+        <value>1000</value>
+      </grip:Input>
+      <grip:Input step="2" socket="5">
+        <value>0</value>
+      </grip:Input>
+      <grip:Input step="2" socket="6">
+        <value>1000</value>
+      </grip:Input>
+      <grip:Input step="2" socket="7">
+        <value>
+          <double>79.73620617132391</double>
+          <double>100.0</double>
+        </value>
+      </grip:Input>
+      <grip:Input step="2" socket="8">
+        <value>200.0</value>
+      </grip:Input>
+      <grip:Input step="2" socket="9">
+        <value>4.0</value>
+      </grip:Input>
+      <grip:Input step="2" socket="10">
+        <value>0</value>
+      </grip:Input>
+      <grip:Input step="2" socket="11">
+        <value>1000</value>
+      </grip:Input>
+      <grip:Output step="2" socket="0" previewed="true"/>
+    </grip:Step>
+    <grip:Step name="Save Images to Disk">
+      <grip:Input step="3" socket="0"/>
+      <grip:Input step="3" socket="1">
+        <value>JPEG</value>
+      </grip:Input>
+      <grip:Input step="3" socket="2">
+        <value>100.0</value>
+      </grip:Input>
+      <grip:Input step="3" socket="3">
+        <value>1.0</value>
+      </grip:Input>
+      <grip:Input step="3" socket="4">
+        <value>false</value>
+      </grip:Input>
+      <grip:Output step="3" socket="0" previewed="true"/>
+    </grip:Step>
+  </steps>
+  <connections>
+    <grip:Connection>
+      <grip:Output step="1" socket="0" previewed="false"/>
+      <grip:Input step="2" socket="0"/>
+    </grip:Connection>
+    <grip:Connection>
+      <grip:Output source="0" socket="0" previewed="true"/>
+      <grip:Input step="3" socket="0"/>
+    </grip:Connection>
+    <grip:Connection>
+      <grip:Output source="0" socket="0" previewed="true"/>
+      <grip:Input step="0" socket="0"/>
+    </grip:Connection>
+  </connections>
+  <settings>
+    <teamNumber>0</teamNumber>
+    <publishAddress>roboRIO-0-FRC.local</publishAddress>
+    <deployAddress>roboRIO-0-FRC.local</deployAddress>
+    <deployDir>/home/lvuser</deployDir>
+    <deployUser>lvuser</deployUser>
+    <deployJavaHome>/usr/local/frc/JRE/</deployJavaHome>
+    <deployJvmOptions>-Xmx50m -XX:-OmitStackTraceInFastThrow -XX:+HeapDumpOnOutOfMemoryError -XX:MaxNewSize=16m</deployJvmOptions>
+  </settings>
+  <codeGenerationSettings>
+    <language>Python</language>
+    <className>GripPipeline</className>
+    <implementWpilibPipeline>false</implementWpilibPipeline>
+    <saveDir>C:\Users\gbryk\GRIP</saveDir>
+    <packageName></packageName>
+    <moduleName>grip</moduleName>
+  </codeGenerationSettings>
+</grip:Pipeline>

GRIP/camera.py

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+import numpy as np
+import cv2 as cv
+cap = cv.VideoCapture(2)
+while(True):
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+    # Our operations on the frame come here
+    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
+    # Display the resulting frame
+    cv.imshow('frame',gray)
+    if cv.waitKey(1) & 0xFF == ord('q'):
+        break
+# When everything done, release the capture
+cap.release()
+cv.destroyAllWindows()

GRIP/contourtest.py

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+import numpy as np
+import cv2
+
+im = cv2.imread('images/2019-01-09_00-57-35-005.png')
+imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
+ret,thresh = cv2.threshold(imgray,127,255,0)
+im2,contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+cv2.drawContours(im, contours, -1, (0,255,0), 3)
+cv2.imshow('frame',im)
+if cv2.waitKey(1) & 0xFF == ord('q'):
+
+    # When everything done, release the capture
+    cap.release()
+    cv2.destroyAllWindows()

GRIP/grip.py

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+import cv2
+import numpy
+import math
+from enum import Enum
+
+class GripPipeline:
+    """
+    An OpenCV pipeline generated by GRIP.
+    """
+
+    def __init__(self):
+        """initializes all values to presets or None if need to be set
+        """
+
+        self.__rgb_threshold_red = [0.0, 45.40101835752104]
+        self.__rgb_threshold_green = [0.0, 255.0]
+        self.__rgb_threshold_blue = [0.0, 255.0]
+
+        self.rgb_threshold_output = None
+
+        self.__find_contours_input = self.rgb_threshold_output
+        self.__find_contours_external_only = False
+
+        self.find_contours_output = None
+
+        self.__filter_contours_contours = self.find_contours_output
+        self.__filter_contours_min_area = 40.0
+        self.__filter_contours_min_perimeter = 10.0
+        self.__filter_contours_min_width = 0.0
+        self.__filter_contours_max_width = 1000.0
+        self.__filter_contours_min_height = 0.0
+        self.__filter_contours_max_height = 1000.0
+        self.__filter_contours_solidity = [0.0, 100.0]
+        self.__filter_contours_max_vertices = 5000.0
+        self.__filter_contours_min_vertices = 0.0
+        self.__filter_contours_min_ratio = 0.0
+        self.__filter_contours_max_ratio = 1000.0
+
+        self.filter_contours_output = None
+
+        self.__convex_hulls_contours = self.filter_contours_output
+
+        self.convex_hulls_output = None
+
+
+    def process(self, source0):
+        """
+        Runs the pipeline and sets all outputs to new values.
+        """
+        # Step RGB_Threshold0:
+        self.__rgb_threshold_input = source0
+        (self.rgb_threshold_output) = self.__rgb_threshold(self.__rgb_threshold_input, self.__rgb_threshold_red, self.__rgb_threshold_green, self.__rgb_threshold_blue)
+
+        # Step Find_Contours0:
+        self.__find_contours_input = self.rgb_threshold_output
+        (self.find_contours_output) = self.__find_contours(self.__find_contours_input, self.__find_contours_external_only)
+
+        # Step Filter_Contours0:
+        self.__filter_contours_contours = self.find_contours_output
+        (self.filter_contours_output) = self.__filter_contours(self.__filter_contours_contours, self.__filter_contours_min_area, self.__filter_contours_min_perimeter, self.__filter_contours_min_width, self.__filter_contours_max_width, self.__filter_contours_min_height, self.__filter_contours_max_height, self.__filter_contours_solidity, self.__filter_contours_max_vertices, self.__filter_contours_min_vertices, self.__filter_contours_min_ratio, self.__filter_contours_max_ratio)
+
+        # Step Convex_Hulls0:
+        self.__convex_hulls_contours = self.filter_contours_output
+        (self.convex_hulls_output) = self.__convex_hulls(self.__convex_hulls_contours)
+
+
+    @staticmethod
+    def __rgb_threshold(input, red, green, blue):
+        """Segment an image based on color ranges.
+        Args:
+            input: A BGR numpy.ndarray.
+            red: A list of two numbers the are the min and max red.
+            green: A list of two numbers the are the min and max green.
+            blue: A list of two numbers the are the min and max blue.
+        Returns:
+            A black and white numpy.ndarray.
+        """
+        out = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
+        return cv2.inRange(out, (red[0], green[0], blue[0]),  (red[1], green[1], blue[1]))
+
+    @staticmethod
+    def __find_contours(input, external_only):
+        """Sets the values of pixels in a binary image to their distance to the nearest black pixel.
+        Args:
+            input: A numpy.ndarray.
+            external_only: A boolean. If true only external contours are found.
+        Return:
+            A list of numpy.ndarray where each one represents a contour.
+        """
+        if(external_only):
+            mode = cv2.RETR_EXTERNAL
+        else:
+            mode = cv2.RETR_LIST
+        method = cv2.CHAIN_APPROX_SIMPLE
+        im2, contours, hierarchy =cv2.findContours(input, mode=mode, method=method)
+        return contours
+
+    @staticmethod
+    def __filter_contours(input_contours, min_area, min_perimeter, min_width, max_width,
+                        min_height, max_height, solidity, max_vertex_count, min_vertex_count,
+                        min_ratio, max_ratio):
+        """Filters out contours that do not meet certain criteria.
+        Args:
+            input_contours: Contours as a list of numpy.ndarray.
+            min_area: The minimum area of a contour that will be kept.
+            min_perimeter: The minimum perimeter of a contour that will be kept.
+            min_width: Minimum width of a contour.
+            max_width: MaxWidth maximum width.
+            min_height: Minimum height.
+            max_height: Maximimum height.
+            solidity: The minimum and maximum solidity of a contour.
+            min_vertex_count: Minimum vertex Count of the contours.
+            max_vertex_count: Maximum vertex Count.
+            min_ratio: Minimum ratio of width to height.
+            max_ratio: Maximum ratio of width to height.
+        Returns:
+            Contours as a list of numpy.ndarray.
+        """
+        output = []
+        for contour in input_contours:
+            x,y,w,h = cv2.boundingRect(contour)
+            if (w < min_width or w > max_width):
+                continue
+            if (h < min_height or h > max_height):
+                continue
+            area = cv2.contourArea(contour)
+            if (area < min_area):
+                continue
+            if (cv2.arcLength(contour, True) < min_perimeter):
+                continue
+            hull = cv2.convexHull(contour)
+            solid = 100 * area / cv2.contourArea(hull)
+            if (solid < solidity[0] or solid > solidity[1]):
+                continue
+            if (len(contour) < min_vertex_count or len(contour) > max_vertex_count):
+                continue
+            ratio = (float)(w) / h
+            if (ratio < min_ratio or ratio > max_ratio):
+                continue
+            output.append(contour)
+        return output
+
+    @staticmethod
+    def __convex_hulls(input_contours):
+        """Computes the convex hulls of contours.
+        Args:
+            input_contours: A list of numpy.ndarray that each represent a contour.
+        Returns:
+            A list of numpy.ndarray that each represent a contour.
+        """
+        output = []
+        for contour in input_contours:
+            output.append(cv2.convexHull(contour))
+        return output
+
+
+

README.md

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+# FRC-2019-JAVA
+
+OK, so basically, I write readme.

build.gradle

@@ -1,6 +1,6 @@
 plugins {
     id "java"
-    id "edu.wpi.first.GradleRIO" version "2019.1.1"
+    id "edu.wpi.first.GradleRIO" version "2019.4.1"
 }
 
 def ROBOT_MAIN_CLASS = "frc.robot.Main"