def keypoint_detection(img, detector, pose_net, ctx=mx.cpu()):
x, scaled_img = gcv.data.transforms.presets.yolo.transform_test(
img, short=480, max_size=1024)
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = detector(x)
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
def process_pose_frame(np_frame, resolution):
width, height = resolution
if np_frame is None:
return mxnet.nd.zeros((height, width, 3), ctx=gpu)
frame = mxnet.nd.array(np_frame, ctx=gpu)
x, img = data.transforms.presets.yolo.transform_test(frame, short=512)
class_IDs, scores, bounding_boxs = detector(x)
pose_input, upscale_bbox = detector_to_simple_pose(
img, class_IDs, scores, bounding_boxs)
if pose_input is None:
return
predicted_heatmap = pose_net(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,
box_thresh=0.5,
keypoint_thresh=0.2,
scale=1.0,
**kwargs)
print(img.size)
# for j in range(len(pred_coords)):
# for i in range(len(pred_coords[0])):
# x, y = pred_coords[j][i].astype(int).asnumpy()
# cv2.circle(img, (x,y), 2, (0, 255, 0), thickness=-1, lineType=cv2.FILLED)
return img
def keypoint_detection(self, frame):
img = mx.nd.array(cv2.cvtColor(frame,
cv2.COLOR_BGR2RGB)).astype('uint8')
x, scaled_img = gcv.data.transforms.presets.yolo.transform_test(
img, short=480, max_size=1024)
x = x.as_in_context(self.ctx)
class_IDs, scores, bounding_boxs = self.person_detector(x)
pred_coords = np.zeros(1)
pose_input, upscale_bbox = detector_to_simple_pose(scaled_img,
class_IDs,
scores,
bounding_boxs,
output_shape=(128,
96),
ctx=self.ctx)
if len(upscale_bbox) > 0:
predicted_heatmap = self.pose_estimator(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(frame,
pred_coords,
confidence,
class_IDs,
bounding_boxs,
scores,
box_thresh=1,
keypoint_thresh=0.3,
scale=scale)
pred_coords *= scale
if isinstance(img, mx.nd.NDArray):
img = frame
if isinstance(pred_coords, mx.nd.NDArray):
pred_coords = pred_coords.asnumpy()
return pred_coords, img
num_frames = 100
for i in range(num_frames):
ret, frame = cap.read()
frame = mx.nd.array(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)).astype('uint8')
x, frame = gcv.data.transforms.presets.ssd.transform_test(frame, short=512, max_size=350)
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = detector(x)
pose_input, upscale_bbox = detector_to_simple_pose(frame, class_IDs, scores, bounding_boxs,
output_shape=(128, 96), ctx=ctx)
if len(upscale_bbox) > 0:
predicted_heatmap = estimator(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
img = cv_plot_keypoints(frame, pred_coords, confidence, class_IDs, bounding_boxs, scores,
box_thresh=0.5, keypoint_thresh=0.2)
cv_plot_image(img)
cv2.waitKey(1)
'''We release the webcam before exiting:
.. code-block:: python'''
cap.release()
'''Results
-------
Download the script to run the demo
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = detector(x)
pose_input, upscale_bbox = detector_to_simple_pose(img,
class_IDs,
scores,
bounding_boxs,
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:
