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darknet_video.py
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import random
import os
import cv2
import time
import darknet
import argparse
import threading
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="window 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 strings although 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(output_video, size, fps):
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
return cv2.VideoWriter(output_video, fourcc, fps, size)
def convert2relative(bbox, preproc_h, preproc_w):
"""
YOLO format use relative coordinates for annotation
"""
x, y, w, h = bbox
return x / preproc_w, y / preproc_h, w / preproc_w, h / preproc_h
def convert2original(image, bbox, preproc_h, preproc_w):
x, y, w, h = convert2relative(bbox, preproc_h, preproc_w)
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
# @TODO - cfati: Unused
def convert4cropping(image, bbox, preproc_h, preproc_w):
x, y, w, h = convert2relative(bbox, preproc_h, preproc_w)
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(stop_flag, input_path, raw_frame_queue, preprocessed_frame_queue, preproc_h, preproc_w):
cap = cv2.VideoCapture(input_path)
while cap.isOpened() and not stop_flag.is_set():
ret, frame = cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (preproc_w, preproc_h),
interpolation=cv2.INTER_LINEAR)
raw_frame_queue.put(frame)
img_for_detect = darknet.make_image(preproc_w, preproc_h, 3)
darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
preprocessed_frame_queue.put(img_for_detect)
stop_flag.set()
cap.release()
def inference(stop_flag, preprocessed_frame_queue, detections_queue, fps_queue,
network, class_names, threshold):
while not stop_flag.is_set():
darknet_image = preprocessed_frame_queue.get()
prev_time = time.time()
detections = darknet.detect_image(network, class_names, darknet_image, thresh=threshold)
fps = 1 / (time.time() - prev_time)
detections_queue.put(detections)
fps_queue.put(int(fps))
print("FPS: {:.2f}".format(fps))
darknet.print_detections(detections, args.ext_output)
darknet.free_image(darknet_image)
def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid_w):
random.seed(3) # deterministic bbox colors
raw_frame_queue, preprocessed_frame_queue, detections_queue, fps_queue = queues
video = set_saved_video(args.out_filename, (vid_w, vid_h), input_video_fps)
fps = 1
while not stop_flag.is_set():
frame = raw_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, preproc_h, preproc_w)
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
stop_flag.set()
video.release()
cv2.destroyAllWindows()
timeout = 1 / (fps if fps > 0 else 0.5)
for q in (preprocessed_frame_queue, detections_queue, fps_queue):
try:
q.get(block=True, timeout=timeout)
except queue.Empty:
pass
if __name__ == "__main__":
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) # Open video twice :(
video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video_fps = int(cap.get(cv2.CAP_PROP_FPS))
cap.release()
del cap
ExecUnit = threading.Thread
Queue = queue.Queue
stop_flag = threading.Event()
raw_frame_queue = Queue()
preprocessed_frame_queue = Queue(maxsize=1)
detections_queue = Queue(maxsize=1)
fps_queue = Queue(maxsize=1)
exec_units = (
ExecUnit(target=video_capture, args=(stop_flag, input_path, raw_frame_queue, preprocessed_frame_queue,
darknet_height, darknet_width)),
ExecUnit(target=inference, args=(stop_flag, preprocessed_frame_queue, detections_queue, fps_queue,
network, class_names, args.thresh)),
ExecUnit(target=drawing, args=(stop_flag, video_fps,
(raw_frame_queue, preprocessed_frame_queue, detections_queue, fps_queue),
darknet_height, darknet_width, video_height, video_width)),
)
for exec_unit in exec_units:
exec_unit.start()
for exec_unit in exec_units:
exec_unit.join()
print("\nDone.")