pose_input, upscale_bbox = detector_to_simple_pose(scaled_img, class_IDs, scores, bounding_boxs,
output_shape=(128, 96), ctx=ctx)
if len(upscale_bbox) > 0:
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
scale = 1.0 * img.shape[0] / scaled_img.shape[0]
img = cv_plot_keypoints(img.asnumpy(), pred_coords, confidence, class_IDs, bounding_boxs, scores,
box_thresh=1, keypoint_thresh=0.3, scale=scale)
return img
if __name__ == '__main__':
ctx = mx.cpu()
detector_name = "ssd_512_mobilenet1.0_coco"
detector = get_model(detector_name, pretrained=True, ctx=ctx)
detector.reset_class(classes=['person'], reuse_weights={'person':'person'})
net = get_model('simple_pose_resnet18_v1b', pretrained='ccd24037', ctx=ctx)
cap = cv2.VideoCapture(0)
time.sleep(1) ### letting the camera autofocus
for i in range(opt.num_frames):
ret, frame = cap.read()
frame = mx.nd.array(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)).astype('uint8')
img = keypoint_detection(frame, detector, net, ctx=ctx)
cv_plot_image(img)
cv2.waitKey(1)
cap.release()
cv2.destroyAllWindows()
pred_coords, confidence = heatmap_to_coord(predicted_heatmap,
upscale_bbox)
img = cv_plot_keypoints(img, pred_coords, confidence, class_IDs,
bounding_boxs, scores)
# 动作对比
angles = AngeleCal.cal(pred_coords, confidence)
results = angeleCal.compare(angles).astype('U5')
# 缩放示例视频并合并显示
width = int(img.shape[1])
height = int(width * frame2.shape[0] / frame2.shape[1])
frame2 = cv.resize(frame2, (width, height), cv.INTER_AREA)
img = np.vstack((img, frame2))
cv_plot_image(img,
upperleft_txt=FPS.fps(),
upperleft_txt_corner=(10, 25),
left_txt_list=results)
# ESC键退出
if cv.waitKey(1) == 27:
break
ret1, frame1 = cap1.read()
ret2, frame2 = cap2.read()
cv.destroyAllWindows()
cap1.release()
cap2.release()
output_shape=(128, 96),
ctx=ctx)
# 检测到人时
if len(upscale_bbox) > 0:
predicted_heatmap = estimator(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap,
upscale_bbox)
img = cv_plot_keypoints(img, pred_coords, confidence, class_IDs,
bounding_boxs, scores)
# 动作识别
X = predicted_heatmap.asnumpy().flatten().reshape(
(len(upscale_bbox), -1))
X = pca.transform(X)
action = le.inverse_transform(cf.predict(X))
cv_plot_image(img,
upperleft_txt=f"FPS:{(1.0 / (time.time() - fps_time)):.2f}",
upperleft_txt_corner=(10, 25),
left_txt_list=action)
fps_time = time.time()
# ESC键退出
if cv.waitKey(1) == 27:
break
ret, frame = cap.read()
cv.destroyAllWindows()
cap.release()
detect_time_sum += (time.time() - starttime)
if pose_input is not None:
starttime = time.time()
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
jsonOutput.write(confidence, pred_coords, scores, frame_count + 1)
img = cv_plot_keypoints(frame, pred_coords, confidence, class_IDs, bounding_boxs, scores,
box_thresh=0.5, keypoint_thresh=0.2)
pose_time_sum += (time.time() - starttime)
active_pose_frame_count += 1
if showimage:
cv_plot_image(img)
vidout.write(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
else:
if showimage:
cv_plot_image(frame)
vidout.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
else:
if showimage:
cv2.imshow('Frame', frame)
vidout.write(frame)
