def video(
input_file: Path = typer.Argument(
...,
file_okay=True,
dir_okay=False,
),
output_file: Path = typer.Option(
"./output/norfair-test.mp4",
file_okay=True,
dir_okay=False,
),
max_distance: int = typer.Option(60),
debug: bool = typer.Option(False),
):
"""
Runs vehicle detection on frames of a video.
Outputs a directory of images ready for processing with the ``images`` command.
XXX not actually ready yet, I'm currently testing `norfair` package which tracks
detections through time so I can be smart about outputing only the largest and
most clear frame of a vehicle rather than many similiar frames of the same vehicle.
"""
yolo_net, yolo_labels, yolo_colors, yolo_layers = load_yolo_net()
video = Video(input_path=str(input_file), output_path=str(output_file))
tracker = Tracker(
distance_function=euclidean_distance,
distance_threshold=max_distance,
)
for frame in video:
detections = detect_objects(yolo_net, yolo_labels, yolo_layers,
yolo_colors, frame)
detections = list(
filter(lambda d: d["label"] in VEHICLE_CLASSES, detections))
detections = [
Detection(get_centroid(box, frame.shape[0], frame.shape[1]),
data=box) for box in detections
]
tracked_objects = tracker.update(detections=detections)
import pdb
pdb.set_trace()
norfair.draw_points(frame, detections)
norfair.draw_tracked_objects(frame, tracked_objects)
video.write(frame)
x2 = yolo_box[2] * img_width
y2 = yolo_box[3] * img_height
return np.array([(x1 + x2) / 2, (y1 + y2) / 2])
parser = argparse.ArgumentParser(description="Track human poses in a video.")
parser.add_argument("files",
type=str,
nargs="+",
help="Video files to process")
args = parser.parse_args()
model = YOLO("yolov4.pth") # set use_cuda=False if using CPU
for input_path in args.files:
video = Video(input_path=input_path)
tracker = Tracker(
distance_function=euclidean_distance,
distance_threshold=max_distance_between_points,
)
for frame in video:
detections = model(frame)
detections = [
Detection(get_centroid(box, frame.shape[0], frame.shape[1]),
data=box) for box in detections if box[-1] == 2
]
tracked_objects = tracker.update(detections=detections)
norfair.draw_points(frame, detections)
norfair.draw_tracked_objects(frame, tracked_objects)
video.write(frame)
default=None,
help="list of classes for inference")
parser.add_argument("--classes_track",
type=int,
nargs='+',
default=None,
help="list of classes to present in the video")
parser.add_argument("--debug", action='store_true', help="debug text")
args = parser.parse_args()
print(args)
model = YOLO(args.weights) # set use_cuda=False if using CPU
max_distance_between_points = 30
for input_path in args.files:
video = Video(input_path=input_path,
output_path=os.path.dirname(input_path))
tracker = Tracker(
distance_function=euclidean_distance,
distance_threshold=max_distance_between_points,
)
#tracker_c1 = Tracker(distance_function=euclidean_distance, distance_threshold=max_distance_between_points)
for frame in video:
detections = model(
frame,
conf=args.conf,
iou=args.iou_thres,
classes_model=args.classes_model) #__call__ method
detections = [
Detection(get_centroid(box, frame.shape[0], frame.shape[1]),
data=box) for box in detections
]
tracked_objects = tracker.update(detections=detections)
# norfair.draw_boxes(frame, detections)
norfair.draw_tracked_objects(frame, tracked_objects)
tracker_q.put(frame)
def detect_objects(image_np):
boxes, _, _ = detector.detect(image_np)
return boxes
if __name__ == '__main__':
path_video = '/home/sonnh/Downloads/Counter_motpy/town.avi'
# video_capture = cv2.VideoCapture(path_video)
video = Video(input_path=path_video)
font = cv2.FONT_HERSHEY_SIMPLEX
input_q = Queue(1) # fps is better if queue is higher but then more lags
detect_q = Queue()
tracker_q = Queue()
tracker = Tracker(
distance_function=euclidean_distance,
distance_threshold=max_distance_between_points,
)
t_detect = Thread(target=worker_detect, args=(input_q, detect_q))
t_tracking = Thread(target=worker_tracking, args=(detect_q, tracker_q))
t_detect.daemon = True
t_detect.start()
t_tracking.daemon = True
y2 = bbox[3]
return np.array([(x1 + x2) / 2, (y1 + y2) / 2])
def get_centroid(yolo_box, img_height, img_width):
x1 = yolo_box[0] * img_width
y1 = yolo_box[1] * img_height
x2 = yolo_box[2] * img_width
y2 = yolo_box[3] * img_height
return np.array([(x1 + x2) / 2, (y1 + y2) / 2])
# set use_cuda=False if using CPU
# for input_path in args.files:
video = Video(input_path='/home/sonnh/Downloads/town_cut.mp4', output_fps=30.0)
tracker = Tracker(
distance_function=euclidean_distance,
distance_threshold=max_distance_between_points,
)
frame_num = -1
for frame in video:
frame_num += 1
if frame_num % 2 == 0:
frame = np.array(frame)
box_detects, _, _ = detector.detect(frame)
detections = [
Detection(get_center(box), data=box) for box in box_detects
]
from norfair import Detection, Tracker, Video, draw_tracked_objects
# Set up Detectron2 object detector
cfg = get_cfg()
cfg.merge_from_file("./detectron2_config.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
detector = DefaultPredictor(cfg)
# Distance function
def centroid_distance(detection, tracked_object):
return np.linalg.norm(detection.points - tracked_object.estimate)
# Norfair
video = Video(input_path="./video.mp4")
tracker = Tracker(distance_function=centroid_distance, distance_threshold=20)
for frame in video:
detections = detector(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# Wrap Detectron2 detections in Norfair's Detection objects
detections = [
Detection(p) for p, c in zip(
detections["instances"].pred_boxes.get_centers().cpu().numpy(),
detections["instances"].pred_classes) if c == 2
]
tracked_objects = tracker.update(detections=detections)
draw_tracked_objects(frame, tracked_objects)
video.write(frame)
from multiprocessing import Queue
from norfair import Video
from threads.read_thread import ReadThread
from threads.detect_thread import DetectThread
from threads.track_thread import TrackThread
from threads.prepare_image import PrepareImage
import time
if __name__ == '__main__':
input_q = Queue(50)
input_det_q = Queue(50)
detect_q = Queue()
tracker_q = Queue()
stt_q = Queue()
video = Video(input_path='town.avi')
thread_read = ReadThread(1, input_q, tracker_q, video, stt_q)
thread_prepares = [PrepareImage(2, input_q, input_det_q) for i in range(3)]
thread_detect = DetectThread(3, input_q, detect_q, Detector(), input_det_q)
thread_detect1 = DetectThread(3, input_q, detect_q, Detector(),
input_det_q)
# thread_track = TrackThread(3, detect_q, tracker_q, stt_q)
thread_read.start()
for thread_prepare in thread_prepares:
thread_prepare.start()
thread_detect.start()
thread_detect1.start()
# thread_track.start()
