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import cv2 as cv
import argparse
# Check OpenCV version
assert cv.__version__ > "4.8.0", \
"Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"
parser = argparse.ArgumentParser(
description="VIT track opencv API")
parser.add_argument('--input', '-i', type=str,
help='Usage: Set path to the input video. Omit for using default camera.')
parser.add_argument('--model_path', type=str, default='vitTracker.onnx',
help='Usage: Set model path, defaults to vitTracker.onnx.')
args = parser.parse_args()
def visualize(image, bbox, score, isLocated, fps=None, box_color=(0, 255, 0),text_color=(0, 255, 0), fontScale = 1, fontSize = 1):
output = image.copy()
h, w, _ = output.shape
if fps is not None:
cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 30), cv.FONT_HERSHEY_DUPLEX, fontScale, text_color, fontSize)
if isLocated and score >= 0.3:
# bbox: Tuple of length 4
x, y, w, h = bbox
cv.rectangle(output, (x, y), (x+w, y+h), box_color, 2)
cv.putText(output, '{:.2f}'.format(score), (x, y+20), cv.FONT_HERSHEY_DUPLEX, fontScale, text_color, fontSize)
else:
text_size, baseline = cv.getTextSize('Target lost!', cv.FONT_HERSHEY_DUPLEX, fontScale, fontSize)
text_x = int((w - text_size[0]) / 2)
text_y = int((h - text_size[1]) / 2)
cv.putText(output, 'Target lost!', (text_x, text_y), cv.FONT_HERSHEY_DUPLEX, fontScale, (0, 0, 255), fontSize)
return output
if __name__ == '__main__':
params = cv.TrackerVit_Params()
params.net = args.model_path
model = cv.TrackerVit_create(params)
# Read from args.input
_input = args.input
if args.input is None:
device_id = 0
_input = device_id
video = cv.VideoCapture(_input)
# Select an object
has_frame, first_frame = video.read()
if not has_frame:
print('No frames grabbed!')
exit()
first_frame_copy = first_frame.copy()
cv.putText(first_frame_copy, "1. Drag a bounding box to track.", (0, 15), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))
cv.putText(first_frame_copy, "2. Press ENTER to confirm", (0, 35), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))
roi = cv.selectROI('vitTrack Demo', first_frame_copy)
print("Selected ROI: {}".format(roi))
# Init tracker with ROI
model.init(first_frame, roi)
# Track frame by frame
tm = cv.TickMeter()
while cv.waitKey(1) < 0:
has_frame, frame = video.read()
if not has_frame:
print('End of video')
break
# Inference
tm.start()
isLocated, bbox = model.update(frame)
score = model.getTrackingScore()
tm.stop()
# Visualize
frame = visualize(frame, bbox, score, isLocated, fps=tm.getFPS())
cv.imshow('vittrack Demo', frame)
tm.reset()
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