detect.py

import os
import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
    tf.config.experimental.set_memory_growth(physical_devices[0], True)
from absl import app, flags, logging
from absl.flags import FLAGS
import core.utils as utils
from core.yolov4 import filter_boxes
from core.functions import *
from tensorflow.python.saved_model import tag_constants
from PIL import Image
import cv2
import numpy as np
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession

flags.DEFINE_string('framework', 'tf', '(tf, tflite, trt')
flags.DEFINE_string('weights', './checkpoints/yolov4-416',
                    'path to weights file')
flags.DEFINE_integer('size', 416, 'resize images to')
flags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')
flags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')
flags.DEFINE_string('image', './data/kite.jpg', 'path to input image')
flags.DEFINE_string('output', 'result.png', 'path to output image')
flags.DEFINE_float('iou', 0.45, 'iou threshold')
flags.DEFINE_float('score', 0.25, 'score threshold')
flags.DEFINE_boolean('crop', False, 'crop objects(lp images) within images')
flags.DEFINE_boolean('ocr', False, 'perform generic OCR on detection regions')
flags.DEFINE_boolean('plate', False, 'perform license plate recognition')

def main(_argv):
    config = ConfigProto()
    config.gpu_options.allow_growth = True
    session = InteractiveSession(config=config)
    STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)
    input_size = FLAGS.size
    image_path = FLAGS.image

    original_image = cv2.imread(image_path)
    original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)

    # image_data = utils.image_preprocess(np.copy(original_image), [input_size, input_size])
    image_data = cv2.resize(original_image, (input_size, input_size))
    image_data = image_data / 255.
    # image_data = image_data[np.newaxis, ...].astype(np.float32)

    images_data = []
    for i in range(1):
        images_data.append(image_data)
    images_data = np.asarray(images_data).astype(np.float32)

    if FLAGS.framework == 'tflite':
        interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
        interpreter.allocate_tensors()
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()
        print(input_details)
        print(output_details)
        interpreter.set_tensor(input_details[0]['index'], images_data)
        interpreter.invoke()
        pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
        if FLAGS.model == 'yolov3' and FLAGS.tiny == True:
            boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25, input_shape=tf.constant([input_size, input_size]))
        else:
            boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25, input_shape=tf.constant([input_size, input_size]))
    else:
        saved_model_loaded = tf.saved_model.load(FLAGS.weights, tags=[tag_constants.SERVING])
        infer = saved_model_loaded.signatures['serving_default']
        batch_data = tf.constant(images_data)
        pred_bbox = infer(batch_data)
        for key, value in pred_bbox.items():
            boxes = value[:, :, 0:4]
            pred_conf = value[:, :, 4:]

    boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
        boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
        scores=tf.reshape(
            pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
        max_output_size_per_class=50,
        max_total_size=50,
        iou_threshold=FLAGS.iou,
        score_threshold=FLAGS.score
    )
    pred_bbox = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]
    image = utils.draw_bbox(original_image, pred_bbox)
    # image = utils.draw_bbox(image_data*255, pred_bbox)
    image = Image.fromarray(image.astype(np.uint8))
    image.show()
    image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
    cv2.imwrite(FLAGS.output, image)

    image_name = image_path.split('/')[-1]
    image_name = image_name.split('.')[0]

    # format bounding boxes from normalized ymin, xmin, ymax, xmax ---> xmin, ymin, xmax, ymax
    original_h, original_w, _ = original_image.shape
    bboxes = utils.format_boxes(boxes.numpy()[0], original_h, original_w)

    # hold all detection data in one variable
    pred_bbox = [bboxes, scores.numpy()[0], classes.numpy()[0], valid_detections.numpy()[0]]

    # read in all class names from config
    class_names = utils.read_class_names(cfg.YOLO.CLASSES)

    # by default allow all classes in .names file
    # allowed_classes = list(class_names.values())

    # custom allowed classes (uncomment line below to allow detections for only people)
    allowed_classes = ['license_plate']

    if FLAGS.crop:
        crop_path = './detections/crop/'+image_name
        print("Cropped LP images in '{}' folder location.".format(crop_path))
        try:
            os.mkdir(crop_path)
        except FileExistsError:
            pass
        crop_objects(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB), pred_bbox, crop_path, allowed_classes)
         
    if FLAGS.ocr:
        ocr_image_path = './detections/crop/'+image_name
        
        

if __name__ == '__main__':
    try:
        app.run(main)
    except SystemExit:
        pass