models/object_tracking_vittrack/demo.py

# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.

import argparse

import numpy as np
import cv2 as cv

# Check OpenCV version
opencv_python_version = lambda str_version: tuple(map(int, (str_version.split("."))))
assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"), \
       "Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"

from vittrack import VitTrack

# Valid combinations of backends and targets
backend_target_pairs = [
    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA_FP16],
    [cv.dnn.DNN_BACKEND_TIMVX,  cv.dnn.DNN_TARGET_NPU],
    [cv.dnn.DNN_BACKEND_CANN,   cv.dnn.DNN_TARGET_NPU]
]

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='object_tracking_vittrack_2023sep.onnx',
                    help='Usage: Set model path')
parser.add_argument('--backend_target', '-bt', type=int, default=0,
                    help='''Choose one of the backend-target pair to run this demo:
                        {:d}: (default) OpenCV implementation + CPU,
                        {:d}: CUDA + GPU (CUDA),
                        {:d}: CUDA + GPU (CUDA FP16),
                        {:d}: TIM-VX + NPU,
                        {:d}: CANN + NPU
                    '''.format(*[x for x in range(len(backend_target_pairs))]))
parser.add_argument('--save', '-s', action='store_true', default=False,
                    help='Usage: Specify to save a file with results.')
parser.add_argument('--vis', '-v', action='store_true', default=True,
                    help='Usage: Specify to open a new window to show results.')
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+25), 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__':
    backend_id = backend_target_pairs[args.backend_target][0]
    target_id = backend_target_pairs[args.backend_target][1]

    model = VitTrack(
        model_path=args.model_path,
        backend_id=backend_id,
        target_id=target_id)

    # Read from args.input
    _input = 0 if args.input is None else args.input
    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, 25), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))
    cv.putText(first_frame_copy, "2. Press ENTER to confirm", (0, 50), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))
    roi = cv.selectROI('VitTrack Demo', first_frame_copy)

    if np.all(np.array(roi) == 0):
        print("No ROI is selected! Exiting ...")
        exit()
    else:
        print("Selected ROI: {}".format(roi))

    if args.save:
        fps = video.get(cv.CAP_PROP_FPS)
        frame_size = (first_frame.shape[1], first_frame.shape[0])
        output_video = cv.VideoWriter('output.mp4', cv.VideoWriter_fourcc(*'mp4v'), fps, frame_size)

    # 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, score = model.infer(frame)
        tm.stop()
        # Visualize
        frame = visualize(frame, bbox, score, isLocated, fps=tm.getFPS())
        if args.save:
            output_video.write(frame)

        if args.vis:
            cv.imshow('VitTrack Demo', frame)
        tm.reset()

    if args.save:
        output_video.release()

    video.release()
    cv.destroyAllWindows()