demo.py

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import argparse
 
import numpy as np
import cv2 as cv
 
from mp_palmdet import MPPalmDet
 
def str2bool(v):
    if v.lower() in ['on', 'yes', 'true', 'y', 't']:
        return True
    elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
        return False
    else:
        raise NotImplementedError
 
backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
try:
    backends += [cv.dnn.DNN_BACKEND_TIMVX]
    targets += [cv.dnn.DNN_TARGET_NPU]
    help_msg_backends += "; {:d}: TIMVX"
    help_msg_targets += "; {:d}: NPU"
except:
    print('This version of OpenCV does not support TIM-VX and NPU. Visit https://gist.github.com/fengyuentau/5a7a5ba36328f2b763aea026c43fa45f for more information.')
 
parser = argparse.ArgumentParser(description='Hand Detector from MediaPipe')
parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.')
parser.add_argument('--model', '-m', type=str, default='./palm_detection_mediapipe_2022may.onnx', help='Path to the model.')
parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
parser.add_argument('--score_threshold', type=float, default=0.99, help='Filter out faces of confidence < conf_threshold. An empirical score threshold for the quantized model is 0.49.')
parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.')
parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
args = parser.parse_args()
 
def visualize(image, score, palm_box, palm_landmarks, fps=None):
    output = image.copy()
 
    if fps is not None:
        cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
 
    # put score
    palm_box = palm_box.astype(np.int32)
    cv.putText(output, '{:.4f}'.format(score), (palm_box[0], palm_box[1]+12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 255, 0))
 
    # draw box
    cv.rectangle(output, (palm_box[0], palm_box[1]), (palm_box[2], palm_box[3]), (0, 255, 0), 2)
 
    # draw points
    palm_landmarks = palm_landmarks.astype(np.int32)
    for p in palm_landmarks:
        cv.circle(output, p, 2, (0, 0, 255), 2)
 
    return output
 
if __name__ == '__main__':
    # Instantiate MPPalmDet
    model = MPPalmDet(modelPath=args.model,
                      nmsThreshold=args.nms_threshold,
                      scoreThreshold=args.score_threshold,
                      backendId=args.backend,
                      targetId=args.target)
 
    # If input is an image
    if args.input is not None:
        image = cv.imread(args.input)
 
        # Inference
        score, palm_box, palm_landmarks = model.infer(image)
        if score is None or palm_box is None or palm_landmarks is None:
            print('Hand not detected')
        else:
            # Print results
            print('score: {:.2f}'.format(score))
            print('palm box: {}'.format(palm_box))
            print('palm_landmarks: ')
            for plm in enumerate(palm_landmarks):
                print('\t{}'.format(plm))
 
            # Draw results on the input image
            image = visualize(image, score, palm_box, palm_landmarks)
 
            # Save results if save is true
            if args.save:
                print('Resutls saved to result.jpg\n')
                cv.imwrite('result.jpg', image)
 
            # Visualize results in a new window
            if args.vis:
                cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
                cv.imshow(args.input, image)
                cv.waitKey(0)
    else: # Omit input to call default camera
        deviceId = 0
        cap = cv.VideoCapture(deviceId)
 
        tm = cv.TickMeter()
        while cv.waitKey(1) < 0:
            hasFrame, frame = cap.read()
            if not hasFrame:
                print('No frames grabbed!')
                break
 
            # Inference
            tm.start()
            score, palm_box, palm_landmarks = model.infer(frame)
            tm.stop()
 
            # Draw results on the input image
            if score is not None and palm_box is not None and palm_landmarks is not None:
                frame = visualize(frame, score, palm_box, palm_landmarks, fps=tm.getFPS())
 
            # Visualize results in a new Window
            cv.imshow('MPPalmDet Demo', frame)
 
            tm.reset()