def keypoint_detection(img, detector, pose_net, ctx=mx.cpu(), axes=None):
x, img = gcv.data.transforms.presets.yolo.transform_test(img,
short=512,
max_size=350)
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = detector(x)
plt.cla()
pose_input, upscale_bbox = detector_to_simple_pose(img,
class_IDs,
scores,
bounding_boxs,
ctx=ctx)
if len(upscale_bbox) > 0:
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap,
upscale_bbox)
axes = plot_keypoints(img,
pred_coords,
confidence,
class_IDs,
bounding_boxs,
scores,
box_thresh=0.5,
keypoint_thresh=0.2,
ax=axes)
plt.draw()
plt.pause(0.001)
else:
axes = plot_image(frame, ax=axes)
plt.draw()
plt.pause(0.001)
return axes
def main():
args = parse_args()
network = None
scale = 1.0
detector = get_model('yolo_darknet53_coco', pretrained=True)
detector.reset_class(['person'], reuse_weights=['person'])
if args.type == 'ONNX':
network = cv2.dnn.readNetFromONNX(args.model)
elif args.type == 'OpenVINO':
network = cv2.dnn.readNetFromModelOptimizer(args.xml, args.model)
# default backend if wasn`t specified
if not args.backend:
network.setPreferableBackend(cv2.dnn.DNN_BACKEND_DEFAULT)
# in case you are going to use CUDA backend in OpenCV, make sure that opencv built with CUDA support
elif args.backend == 'CUDA':
network.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
network.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
# in case you are going to use OpenVINO model, make sure that inference engine already installed and opencv built with IE support
elif args.backend == 'INFERENCE':
network.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
network.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
x, img = data.transforms.presets.yolo.load_test(args.img, short=512)
class_IDs, scores, bounding_boxes = detector(x)
pose_input, upscaled_bbox = detector_to_simple_pose(
img, class_IDs, scores, bounding_boxes)
pose_input = pose_input.asnumpy()
bs = []
for i in range(pose_input.shape[0]):
input = cv2.dnn.blobFromImage(
np.transpose(np.squeeze(pose_input[i, :, :, :]), (1, 2, 0)), scale,
(args.width, args.height), (0, 0, 0), False)
network.setInput(input)
temp = network.forward()
bs.append(temp)
output = np.concatenate(bs, axis=0)
output = mx.nd.array(output)
pred_coords, confidence = heatmap_to_coord(output, upscaled_bbox)
ax = plot_keypoints(img,
pred_coords,
confidence,
class_IDs,
bounding_boxes,
scores,
box_thresh=0.5,
keypoint_thresh=0.2)
plt.show()
def keypoint_detection(img_path, detector, pose_net):
x, img = data.transforms.presets.yolo.load_test(img_path, short=512)
class_IDs, scores, bounding_boxs = detector(x)
pose_input, upscale_bbox = detector_to_simple_pose(img, class_IDs, scores, bounding_boxs)
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
ax = plot_keypoints(img, pred_coords, confidence, class_IDs, bounding_boxs, scores,
box_thresh=0.5, keypoint_thresh=0.2)
plt.show()
def keypoint_detection(img_path, detector, pose_net):
x, img = data.transforms.presets.yolo.load_test(img_path, short=512)
class_IDs, scores, bounding_boxs = detector(x)
pose_input, upscale_bbox = detector_to_simple_pose(img, class_IDs, scores, bounding_boxs)
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
ax = plot_keypoints(img, pred_coords, confidence, class_IDs, bounding_boxs, scores,
box_thresh=0.5, keypoint_thresh=0.2)
plt.show()
def keypoint_detection(i,
frame,
imagepath,
detector,
pose_net,
ctx=mx.cpu(),
axes=None):
global pause_time
x, img = gcv.data.transforms.presets.yolo.transform_test(frame,
short=512,
max_size=1024)
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = detector(x)
plt.cla()
pose_input, upscale_bbox = detector_to_simple_pose(img,
class_IDs,
scores,
bounding_boxs,
output_shape=(1024,
768),
ctx=ctx)
#print(pose_input,"\n")
if len(upscale_bbox) > 0:
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap,
upscale_bbox)
hackathon_action(i, frame, imagepath, pred_coords, confidence,
class_IDs, bounding_boxs, scores)
axes = plot_keypoints(img,
pred_coords,
confidence,
class_IDs,
bounding_boxs,
scores,
box_thresh=0.5,
keypoint_thresh=0.2,
ax=axes)
plt.draw()
plt.pause(pause_time)
#plt.pause(1.0)
else:
axes = plot_image(frame, ax=axes)
plt.draw()
plt.pause(pause_time)
return axes
def keypoint_detection(img, detector, pose_net, ctx=mx.cpu(), axes=None):
x, img = gcv.data.transforms.presets.yolo.transform_test(img, short=512, max_size=350)
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = detector(x)
plt.cla()
pose_input, upscale_bbox = detector_to_simple_pose(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)
axes = plot_keypoints(img, pred_coords, confidence, class_IDs, bounding_boxs, scores,
box_thresh=0.5, keypoint_thresh=0.2, ax=axes)
plt.draw()
plt.pause(0.001)
else:
axes = plot_image(frame, ax=axes)
plt.draw()
plt.pause(0.001)
return axes