from ctypes import * import random import os import cv2 import time import darknet import argparse from threading import Thread, enumerate from queue import Queue def parser(): parser = argparse.ArgumentParser(description="YOLO Object Detection") parser.add_argument("--input", type=str, default=0, help="video source. If empty, uses webcam 0 stream") parser.add_argument("--out_filename", type=str, default="", help="inference video name. Not saved if empty") parser.add_argument("--weights", default="yolov4.weights", help="yolo weights path") parser.add_argument("--dont_show", action='store_true', help="windown inference display. For headless systems") parser.add_argument("--ext_output", action='store_true', help="display bbox coordinates of detected objects") parser.add_argument("--config_file", default="./cfg/yolov4.cfg", help="path to config file") parser.add_argument("--data_file", default="./cfg/coco.data", help="path to data file") parser.add_argument("--thresh", type=float, default=.25, help="remove detections with confidence below this value") return parser.parse_args() def str2int(video_path): """ argparse returns and string althout webcam uses int (0, 1 ...) Cast to int if needed """ try: return int(video_path) except ValueError: return video_path def check_arguments_errors(args): assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" if not os.path.exists(args.config_file): raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file)))) if not os.path.exists(args.weights): raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights)))) if not os.path.exists(args.data_file): raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file)))) if str2int(args.input) == str and not os.path.exists(args.input): raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input)))) def set_saved_video(input_video, output_video, size): fourcc = cv2.VideoWriter_fourcc(*"MJPG") fps = int(input_video.get(cv2.CAP_PROP_FPS)) video = cv2.VideoWriter(output_video, fourcc, fps, size) return video def convert2relative(bbox): """ YOLO format use relative coordinates for annotation """ x, y, w, h = bbox _height = darknet_height _width = darknet_width return x/_width, y/_height, w/_width, h/_height def convert2original(image, bbox): x, y, w, h = convert2relative(bbox) image_h, image_w, __ = image.shape orig_x = int(x * image_w) orig_y = int(y * image_h) orig_width = int(w * image_w) orig_height = int(h * image_h) bbox_converted = (orig_x, orig_y, orig_width, orig_height) return bbox_converted def convert4cropping(image, bbox): x, y, w, h = convert2relative(bbox) image_h, image_w, __ = image.shape orig_left = int((x - w / 2.) * image_w) orig_right = int((x + w / 2.) * image_w) orig_top = int((y - h / 2.) * image_h) orig_bottom = int((y + h / 2.) * image_h) if (orig_left < 0): orig_left = 0 if (orig_right > image_w - 1): orig_right = image_w - 1 if (orig_top < 0): orig_top = 0 if (orig_bottom > image_h - 1): orig_bottom = image_h - 1 bbox_cropping = (orig_left, orig_top, orig_right, orig_bottom) return bbox_cropping def video_capture(frame_queue, darknet_image_queue): while cap.isOpened(): ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame_resized = cv2.resize(frame_rgb, (darknet_width, darknet_height), interpolation=cv2.INTER_LINEAR) frame_queue.put(frame) img_for_detect = darknet.make_image(darknet_width, darknet_height, 3) darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes()) darknet_image_queue.put(img_for_detect) cap.release() def inference(darknet_image_queue, detections_queue, fps_queue): while cap.isOpened(): darknet_image = darknet_image_queue.get() prev_time = time.time() detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh) detections_queue.put(detections) fps = int(1/(time.time() - prev_time)) fps_queue.put(fps) print("FPS: {}".format(fps)) darknet.print_detections(detections, args.ext_output) darknet.free_image(darknet_image) cap.release() def drawing(frame_queue, detections_queue, fps_queue): random.seed(3) # deterministic bbox colors video = set_saved_video(cap, args.out_filename, (video_width, video_height)) while cap.isOpened(): frame = frame_queue.get() detections = detections_queue.get() fps = fps_queue.get() detections_adjusted = [] if frame is not None: for label, confidence, bbox in detections: bbox_adjusted = convert2original(frame, bbox) detections_adjusted.append((str(label), confidence, bbox_adjusted)) image = darknet.draw_boxes(detections_adjusted, frame, class_colors) if not args.dont_show: cv2.imshow('Inference', image) if args.out_filename is not None: video.write(image) if cv2.waitKey(fps) == 27: break cap.release() video.release() cv2.destroyAllWindows() if __name__ == '__main__': frame_queue = Queue() darknet_image_queue = Queue(maxsize=1) detections_queue = Queue(maxsize=1) fps_queue = Queue(maxsize=1) args = parser() check_arguments_errors(args) network, class_names, class_colors = darknet.load_network( args.config_file, args.data_file, args.weights, batch_size=1 ) darknet_width = darknet.network_width(network) darknet_height = darknet.network_height(network) input_path = str2int(args.input) cap = cv2.VideoCapture(input_path) video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start() Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start()