def input_task(self):
seq_num = 0
while self.is_running():
# Send images to next stage
# if camera - receive frames from camera
if camera:
try:
frame = self.device.getOutputQueue('cam_out').get()
self.frame_queue.put(frame)
except RuntimeError:
continue
# else if video - send frames down to NN
else:
# Get frame from video capture
read_correctly, vid_frame = self.cap.read()
if not read_correctly:
break
# Send to NN and to inference thread
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(seq_num)
frame_nn.setWidth(544)
frame_nn.setHeight(320)
frame_nn.setData(to_planar(vid_frame, (544, 320)))
self.device.getInputQueue("detection_in").send(frame_nn)
# if high quality, send original frames
if hq:
frame_orig = dai.ImgFrame()
frame_orig.setSequenceNum(seq_num)
frame_orig.setWidth(vid_frame.shape[1])
frame_orig.setHeight(vid_frame.shape[0])
frame_orig.setData(
to_planar(vid_frame,
(vid_frame.shape[1], vid_frame.shape[0])))
self.frame_queue.put(frame_orig)
# else send resized frames
else:
self.frame_queue.put(frame_nn)
seq_num = seq_num + 1
# Sleep at video framerate
time.sleep(1.0 / self.FRAMERATE)
# Stop execution after input task doesn't have
# any extra data anymore
self.running = False
def main(filename=None):
pipeline = createPipeline(filename is None)
with depthai.Device() as device:
device.startPipeline(pipeline)
if filename:
image_in = device.getInputQueue("image_in")
nn_out = device.getOutputQueue(name="nn_out", maxSize=4, blocking=True)
run = True
while run:
if filename:
origframe = cv2.imread(filename)
frame = cv2.cvtColor(origframe, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (224, 224)).transpose(2, 0,
1).flatten()
tstamp = time.monotonic()
lic_frame = depthai.ImgFrame()
lic_frame.setData(frame)
lic_frame.setTimestamp(tstamp)
lic_frame.setType(lic_frame.Type.RGB888p)
lic_frame.setWidth(224)
lic_frame.setHeight(224)
image_in.send(lic_frame)
data = nn_out.get()
result = np.array(data.getFirstLayerFp16()).reshape((5, 224, 224))
tags = createTagImage(result)
cv2.imshow("Source", origframe)
cv2.imshow("Tags", tags)
cv2.waitKey(0)
if filename is not None:
run = False
def lic_thread(det_queue, det_pass, rec_queue):
global license_detections, lic_last_seq
while running:
try:
in_det = det_queue.get().detections
in_pass = det_pass.get()
orig_frame = frame_seq_map.get(in_pass.getSequenceNum(), None)
if orig_frame is None:
continue
license_detections = [
detection for detection in in_det if detection.label == 2
]
for detection in license_detections:
bbox = frame_norm(orig_frame, (detection.xmin, detection.ymin,
detection.xmax, detection.ymax))
cropped_frame = orig_frame[bbox[1]:bbox[3], bbox[0]:bbox[2]]
tstamp = time.monotonic()
img = dai.ImgFrame()
img.setTimestamp(tstamp)
img.setType(dai.RawImgFrame.Type.BGR888p)
img.setData(to_planar(cropped_frame, (94, 24)))
img.setWidth(94)
img.setHeight(24)
rec_queue.send(img)
fps.tick('lic')
except RuntimeError:
continue
def veh_thread(det_queue, det_pass, attr_queue):
global vehicle_detections, veh_last_seq
while running:
try:
vehicle_detections = det_queue.get().detections
in_pass = det_pass.get()
orig_frame = frame_seq_map.get(in_pass.getSequenceNum(), None)
if orig_frame is None:
continue
veh_last_seq = in_pass.getSequenceNum()
for detection in vehicle_detections:
bbox = frame_norm(orig_frame, (detection.xmin, detection.ymin,
detection.xmax, detection.ymax))
cropped_frame = orig_frame[bbox[1]:bbox[3], bbox[0]:bbox[2]]
tstamp = time.monotonic()
img = dai.ImgFrame()
img.setTimestamp(tstamp)
img.setType(dai.RawImgFrame.Type.BGR888p)
img.setData(to_planar(cropped_frame, (72, 72)))
img.setWidth(72)
img.setHeight(72)
attr_queue.send(img)
fps.tick('veh')
except RuntimeError:
continue
def next_frame(self):
self.fps.update()
if self.input_type == "rgb":
# Send cropping information to manip node on device
cfg = dai.ImageManipConfig()
points = [
[self.crop_region.xmin, self.crop_region.ymin],
[self.crop_region.xmax - 1, self.crop_region.ymin],
[self.crop_region.xmax - 1, self.crop_region.ymax - 1],
[self.crop_region.xmin, self.crop_region.ymax - 1],
]
point2fList = []
for p in points:
pt = dai.Point2f()
pt.x, pt.y = p[0], p[1]
point2fList.append(pt)
cfg.setWarpTransformFourPoints(point2fList, False)
cfg.setResize(self.pd_input_length, self.pd_input_length)
cfg.setFrameType(dai.ImgFrame.Type.RGB888p)
self.q_manip_cfg.send(cfg)
# Get the device camera frame if wanted
if self.laconic:
frame = np.zeros((self.frame_size, self.frame_size, 3),
dtype=np.uint8)
else:
in_video = self.q_video.get()
frame = in_video.getCvFrame()
else:
if self.input_type == "image":
frame = self.img.copy()
else:
ok, frame = self.cap.read()
if not ok:
return None, None
# Cropping of the video frame
cropped = self.crop_and_resize(frame, self.crop_region)
cropped = cv2.cvtColor(cropped,
cv2.COLOR_BGR2RGB).transpose(2, 0, 1)
frame_nn = dai.ImgFrame()
frame_nn.setTimestamp(time.monotonic())
frame_nn.setWidth(self.pd_input_length)
frame_nn.setHeight(self.pd_input_length)
frame_nn.setData(cropped)
self.q_pd_in.send(frame_nn)
# Get result from device
inference = self.q_pd_out.get()
body = self.pd_postprocess(inference)
self.crop_region = body.next_crop_region
# Statistics
if self.stats:
self.nb_frames += 1
self.nb_pd_inferences += 1
return frame, body
def run_nn(img, input_queue, width, height):
frameNn = dai.ImgFrame()
frameNn.setType(dai.ImgFrame.Type.BGR888p)
frameNn.setWidth(width)
frameNn.setHeight(height)
frameNn.setData(toPlanar(img, (height, width)))
input_queue.send(frameNn)
def run_nn(x_in, frame, w, h):
nn_data = dai.ImgFrame()
nn_data.setData(toPlanar(frame, (w, h)))
nn_data.setType(dai.RawImgFrame.Type.BGR888p)
nn_data.setTimestamp(monotonic())
nn_data.setWidth(w)
nn_data.setHeight(h)
x_in.send(nn_data)
def send_mono(self, q, img, right):
self.lastFrame['right' if right else 'left'] = img
h, w = img.shape
frame = dai.ImgFrame()
frame.setData(img)
frame.setType(dai.RawImgFrame.Type.RAW8)
frame.setWidth(w)
frame.setHeight(h)
frame.setInstanceNum((2 if right else 1))
q.send(frame)
def send_color(self, q, img):
# Resize/crop color frame as specified by the user
img = self.resize_color(img)
self.lastFrame['color'] = img
h, w, c = img.shape
frame = dai.ImgFrame()
frame.setType(dai.RawImgFrame.Type.BGR888p)
frame.setData(self.to_planar(img))
frame.setWidth(w)
frame.setHeight(h)
frame.setInstanceNum(0)
q.send(frame)
def run_nn(img, input_queue, width, height):
"""
It takes an image, converts it to a planar image, and sends it to the input queue
:param img: The image to be processed
:param input_queue: The input queue to the neural network
:param width: The width of the image in pixels
:param height: The height of the image in pixels
"""
frameNn = dai.ImgFrame()
frameNn.setType(dai.ImgFrame.Type.BGR888p)
frameNn.setWidth(width)
frameNn.setHeight(height)
frameNn.setData(toPlanar(img, (height, width)))
input_queue.send(frameNn)
def send_depth(self, q, depth):
# TODO refactor saving depth. Reading will be from ROS bags.
# print("depth size", type(depth))
# depth_frame = np.array(depth).astype(np.uint8).view(np.uint16).reshape((400, 640))
# depthFrameColor = cv2.normalize(depth_frame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
# depthFrameColor = cv2.equalizeHist(depthFrameColor)
# depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
# cv2.imshow("depth", depthFrameColor)
frame = dai.ImgFrame()
frame.setType(dai.RawImgFrame.Type.RAW16)
frame.setData(depth)
frame.setWidth(640)
frame.setHeight(400)
frame.setInstanceNum(0)
q.send(frame)
# Show the frame
cv2.imshow(name, frame)
# cv2.imshow('out', mask)
size = 128
start_time = monotonic()
counter = 0
fps = 0
cap = cv2.VideoCapture(videoPath)
while cap.isOpened():
read_correctly, frame = cap.read()
if not read_correctly:
break
img = dai.ImgFrame()
img.setData(to_planar(frame, (size, size)))
img.setTimestamp(monotonic())
img.setWidth(size)
img.setHeight(size)
qIn.send(img)
inDet = qDet.get()
print(inDet.getAllLayerNames())
out = inDet.getLayerFp16("model_7/conv2d_transpose_36/Sigmoid")
output = np.array(out).reshape(size, size)
norm_image = cv2.normalize(output,
None,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX,
def test_pipeline():
#pipeline, streams = create_rgb_cam_pipeline()
#pipeline, streams = create_mono_cam_pipeline()
pipeline, streams = create_stereo_depth_pipeline(source_camera)
print("Creating DepthAI device")
with dai.Device(pipeline) as device:
print("Starting pipeline")
device.startPipeline()
in_streams = []
if not source_camera:
# Reversed order trick:
# The sync stage on device side has a timeout between receiving left
# and right frames. In case a delay would occur on host between sending
# left and right, the timeout will get triggered.
# We make sure to send first the right frame, then left.
in_streams.extend(['in_right', 'in_left'])
in_q_list = []
inStreamsCameraID = []
for s in in_streams:
q = device.getInputQueue(s)
in_q_list.append(q)
inStreamsCameraID = [
dai.CameraBoardSocket.RIGHT, dai.CameraBoardSocket.LEFT
]
# Create a receive queue for each stream
q_list = []
for s in streams:
q = device.getOutputQueue(s, 8, blocking=False)
q_list.append(q)
# Need to set a timestamp for input frames, for the sync stage in Stereo node
timestamp_ms = 0
index = 0
while True:
# Handle input streams, if any
if in_q_list:
dataset_size = 2 # Number of image pairs
frame_interval_ms = 33
for i, q in enumerate(in_q_list):
name = q.getName()
path = 'dataset/' + str(index) + '/' + name + '.png'
data = cv2.imread(path,
cv2.IMREAD_GRAYSCALE).reshape(720 * 1280)
tstamp = datetime.timedelta(seconds=timestamp_ms // 1000,
milliseconds=timestamp_ms %
1000)
img = dai.ImgFrame()
img.setData(data)
img.setTimestamp(tstamp)
img.setInstanceNum(inStreamsCameraID[i])
img.setType(dai.ImgFrame.Type.RAW8)
img.setWidth(1280)
img.setHeight(720)
q.send(img)
if timestamp_ms == 0: # Send twice for first iteration
q.send(img)
print("Sent frame: {:25s}".format(path), 'timestamp_ms:',
timestamp_ms)
timestamp_ms += frame_interval_ms
index = (index + 1) % dataset_size
if 1: # Optional delay between iterations, host driven pipeline
sleep(frame_interval_ms / 1000)
# Handle output streams
for q in q_list:
name = q.getName()
image = q.get()
#print("Received frame:", name)
# Skip some streams for now, to reduce CPU load
if name in ['left', 'right', 'depth']: continue
frame = convert_to_cv2_frame(name, image)
cv2.imshow(name, frame)
if cv2.waitKey(1) == ord('q'):
break
def run(self):
device = dai.Device(self.create_pipeline())
device.startPipeline()
# Define data queues
if self.camera:
q_video = device.getOutputQueue(name="cam_out", maxSize=1, blocking=False)
q_pd_out = device.getOutputQueue(name="pd_out", maxSize=1, blocking=False)
if self.use_lm:
q_lm_out = device.getOutputQueue(name="lm_out", maxSize=2, blocking=False)
q_lm_in = device.getInputQueue(name="lm_in")
else:
q_pd_in = device.getInputQueue(name="pd_in")
q_pd_out = device.getOutputQueue(name="pd_out", maxSize=4, blocking=True)
if self.use_lm:
q_lm_out = device.getOutputQueue(name="lm_out", maxSize=4, blocking=True)
q_lm_in = device.getInputQueue(name="lm_in")
self.fps = FPS(mean_nb_frames=20)
seq_num = 0
nb_pd_inferences = 0
nb_lm_inferences = 0
glob_pd_rtrip_time = 0
glob_lm_rtrip_time = 0
while True:
self.fps.update()
if self.camera:
in_video = q_video.get()
video_frame = in_video.getCvFrame()
else:
if self.image_mode:
vid_frame = self.img
else:
ok, vid_frame = self.cap.read()
if not ok:
break
h, w = vid_frame.shape[:2]
dx = (w - self.video_size) // 2
dy = (h - self.video_size) // 2
video_frame = vid_frame[dy:dy+self.video_size, dx:dx+self.video_size]
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(seq_num)
frame_nn.setWidth(self.pd_input_length)
frame_nn.setHeight(self.pd_input_length)
frame_nn.setData(to_planar(video_frame, (self.pd_input_length, self.pd_input_length)))
q_pd_in.send(frame_nn)
pd_rtrip_time = now()
seq_num += 1
annotated_frame = video_frame.copy()
# Get palm detection
inference = q_pd_out.get()
if not self.camera: glob_pd_rtrip_time += now() - pd_rtrip_time
self.pd_postprocess(inference)
self.pd_render(annotated_frame)
nb_pd_inferences += 1
# Hand landmarks
if self.use_lm:
for i,r in enumerate(self.regions):
img_hand = mpu.warp_rect_img(r.rect_points, video_frame, self.lm_input_length, self.lm_input_length)
nn_data = dai.NNData()
nn_data.setLayer("input_1", to_planar(img_hand, (self.lm_input_length, self.lm_input_length)))
q_lm_in.send(nn_data)
if i == 0: lm_rtrip_time = now() # We measure only for the first region
# Retrieve hand landmarks
for i,r in enumerate(self.regions):
inference = q_lm_out.get()
if i == 0: glob_lm_rtrip_time += now() - lm_rtrip_time
self.lm_postprocess(r, inference)
self.lm_render(annotated_frame, r)
nb_lm_inferences += 1
self.fps.display(annotated_frame, orig=(50,50),color=(240,180,100))
cv2.imshow("video", annotated_frame)
key = cv2.waitKey(1)
if key == ord('q') or key == 27:
break
elif key == 32:
# Pause on space bar
cv2.waitKey(0)
elif key == ord('1'):
self.show_pd_box = not self.show_pd_box
elif key == ord('2'):
self.show_pd_kps = not self.show_pd_kps
elif key == ord('3'):
self.show_rot_rect = not self.show_rot_rect
elif key == ord('4'):
self.show_landmarks = not self.show_landmarks
elif key == ord('5'):
self.show_handedness = not self.show_handedness
elif key == ord('6'):
self.show_scores = not self.show_scores
elif key == ord('7'):
self.show_gesture = not self.show_gesture
# Print some stats
if not self.camera:
print(f"# video files frames : {seq_num}")
print(f"# palm detection inferences received : {nb_pd_inferences}")
print(f"# hand landmark inferences received : {nb_lm_inferences}")
print(f"Palm detection round trip : {glob_pd_rtrip_time/nb_pd_inferences*1000:.1f} ms")
print(f"Hand landmark round trip : {glob_lm_rtrip_time/nb_lm_inferences*1000:.1f} ms")
def test_pipeline():
#pipeline, streams = create_rgb_cam_pipeline()
#pipeline, streams = create_mono_cam_pipeline()
pipeline, streams = create_stereo_depth_pipeline(source_camera)
print("Creating DepthAI device")
if 1:
device = dai.Device(pipeline)
else: # For debug mode, with the firmware already loaded
found, device_info = dai.XLinkConnection.getFirstDevice(
dai.XLinkDeviceState.X_LINK_BOOTED)
if found:
device = dai.Device(pipeline, device_info)
else:
raise RuntimeError("Device not found")
print("Starting pipeline")
device.startPipeline()
in_streams = []
if not source_camera:
# Reversed order trick:
# The sync stage on device side has a timeout between receiving left
# and right frames. In case a delay would occur on host between sending
# left and right, the timeout will get triggered.
# We make sure to send first the right frame, then left.
in_streams.extend(['in_right', 'in_left'])
in_q_list = []
for s in in_streams:
q = device.getInputQueue(s)
in_q_list.append(q)
# Create a receive queue for each stream
q_list = []
for s in streams:
q = device.getOutputQueue(s, 8, True)
q_list.append(q)
# Need to set a timestamp for input frames, for the sync stage in Stereo node
timestamp_ms = 0
index = 0
while True:
# Handle input streams, if any
if in_q_list:
dataset_size = 2 # Number of image pairs
frame_interval_ms = 33
for q in in_q_list:
name = q.getName()
path = 'dataset/' + str(index) + '/' + name + '.png'
data = cv2.imread(path,
cv2.IMREAD_GRAYSCALE).reshape(720 * 1280)
tstamp_ns = timestamp_ms * (1000 * 1000)
tstamp = dai.Timestamp()
tstamp.sec = tstamp_ns // (1000 * 1000 * 1000)
tstamp.nsec = tstamp_ns % (1000 * 1000 * 1000)
img = dai.ImgFrame()
img.setData(data)
img.setTimestamp(tstamp)
img.setWidth(1280)
img.setHeight(720)
q.send(img)
print("Sent frame: {:25s}".format(path), 'timestamp_ms:',
timestamp_ms)
timestamp_ms += frame_interval_ms
index = (index + 1) % dataset_size
if 1: # Optional delay between iterations, host driven pipeline
sleep(frame_interval_ms / 1000)
# Handle output streams
for q in q_list:
name = q.getName()
image = q.get()
#print("Received frame:", name)
# Skip some streams for now, to reduce CPU load
if name in ['left', 'right', 'depth']: continue
frame = convert_to_cv2_frame(name, image)
cv2.imshow(name, frame)
if cv2.waitKey(1) == ord('q'):
break
print("Closing device")
del device
def main(args):
"""
Main programm loop.
Parameters
----------
args : command line arguments parsed by parse_arguments
"""
# Set up PoseEstimator, pipeline, window with sliders for PoseEstimator
# options and load the dataset
if args["model"] not in model_list:
raise ValueError("Unknown model '{}'".format(args["model"]))
model_config = model_list[args["model"]]
pose_estimator = get_poseestimator(model_config, **args)
with dai.Device(create_pipeline(model_config, camera=False,
**args)) as device:
device.startPipeline()
pose_in_queue = device.getInputQueue("pose_in")
pose_queue = device.getOutputQueue("pose")
result_window = SliderWindow("Result")
result_window.add_poseestimator_options(pose_estimator, args)
cv2.namedWindow("Original")
# load coco keypoint annotations
coco_data = COCOData(**args)
# Start main loop
last_img_id = -1
cur_img_id = 0
original_frame = None
results_frame = None
raw_output = None
redraw = True
while True:
# Check for and handle slider changes, redraw if there was a change
slider_changes = result_window.get_changes()
for option_name, value in slider_changes.items():
pose_estimator.set_option(option_name, value)
redraw = True
# When new image was selected process it
if last_img_id != cur_img_id:
img, gt_keypoints = coco_data[cur_img_id]
# Send image off for inference
input_frame = pose_estimator.get_input_frame(img)
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(0)
frame_nn.setWidth(input_frame.shape[2])
frame_nn.setHeight(input_frame.shape[1])
frame_nn.setFrame(input_frame)
pose_in_queue.send(frame_nn)
# Draw ground truth annotations
original_frame = img.copy()
coco_data.draw(gt_keypoints, original_frame)
cv2.rectangle(original_frame, (0, 0), (150, 25), (0, 0, 0), -1)
cv2.putText(original_frame,
"#people: {}".format(len(gt_keypoints)), (2, 15),
cv2.FONT_HERSHEY_TRIPLEX,
0.4,
color=(255, 255, 255))
# Discard previous results
results_frame = None
raw_output = None
last_img_id = cur_img_id
# Store the raw results once available
if pose_queue.has():
raw_output = pose_queue.get()
# Once we've got the raw output and again whenever an option
# changes we need to decode and draw
if redraw and raw_output is not None:
results_frame = img.copy()
pred_keypoints = pose_estimator.get_pose_data(raw_output)
pose_estimator.draw_results(pred_keypoints, results_frame)
cv2.rectangle(results_frame, (0, 0), (150, 25), (0, 0, 0), -1)
cv2.putText(results_frame,
"#people: {}".format(len(pred_keypoints)), (2, 15),
cv2.FONT_HERSHEY_TRIPLEX,
0.4,
color=(255, 255, 255))
cv2.imshow("Original", original_frame)
if results_frame is not None:
cv2.imshow("Result", results_frame)
c = cv2.waitKey(1)
if c == ord("q"):
break
elif c == ord("n"):
if cur_img_id < len(coco_data) - 1:
cur_img_id += 1
elif c == ord("p"):
if cur_img_id > 0:
cur_img_id -= 1
def run(self):
device = dai.Device(self.create_pipeline())
device.startPipeline()
# Define data queues
if self.input_type == "internal":
q_video = device.getOutputQueue(name="cam_out",
maxSize=1,
blocking=False)
q_pd_out = device.getOutputQueue(name="pd_out",
maxSize=1,
blocking=False)
q_lm_out = device.getOutputQueue(name="lm_out",
maxSize=2,
blocking=False)
q_lm_in = device.getInputQueue(name="lm_in")
else:
q_pd_in = device.getInputQueue(name="pd_in")
q_pd_out = device.getOutputQueue(name="pd_out",
maxSize=4,
blocking=True)
q_lm_out = device.getOutputQueue(name="lm_out",
maxSize=4,
blocking=True)
q_lm_in = device.getInputQueue(name="lm_in")
self.fps = FPS(mean_nb_frames=20)
seq_num = 0
nb_pd_inferences = 0
nb_lm_inferences = 0
glob_pd_rtrip_time = 0
glob_lm_rtrip_time = 0
while True:
self.fps.update()
if self.input_type == "internal":
in_video = q_video.get()
video_frame = in_video.getCvFrame()
self.frame_size = video_frame.shape[
0] # The image is square cropped on the device
self.pad_w = self.pad_h = 0
else:
if self.input_type == "image":
vid_frame = self.img
else:
ok, vid_frame = self.cap.read()
if not ok:
break
h, w = vid_frame.shape[:2]
if self.crop:
# Cropping the long side to get a square shape
self.frame_size = min(h, w)
dx = (w - self.frame_size) // 2
dy = (h - self.frame_size) // 2
video_frame = vid_frame[dy:dy + self.frame_size,
dx:dx + self.frame_size]
else:
# Padding on the small side to get a square shape
self.frame_size = max(h, w)
self.pad_h = int((self.frame_size - h) / 2)
self.pad_w = int((self.frame_size - w) / 2)
video_frame = cv2.copyMakeBorder(vid_frame, self.pad_h,
self.pad_h, self.pad_w,
self.pad_w,
cv2.BORDER_CONSTANT)
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(seq_num)
frame_nn.setWidth(self.pd_input_length)
frame_nn.setHeight(self.pd_input_length)
frame_nn.setData(
to_planar(video_frame,
(self.pd_input_length, self.pd_input_length)))
pd_rtrip_time = now()
q_pd_in.send(frame_nn)
seq_num += 1
annotated_frame = video_frame.copy()
# Get pose detection
inference = q_pd_out.get()
if self.input_type != "internal":
pd_rtrip_time = now() - pd_rtrip_time
glob_pd_rtrip_time += pd_rtrip_time
self.pd_postprocess(inference)
self.pd_render(annotated_frame)
nb_pd_inferences += 1
# Landmarks
self.nb_active_regions = 0
if self.show_3d:
self.vis3d.clear_geometries()
self.vis3d.add_geometry(self.grid_floor,
reset_bounding_box=False)
self.vis3d.add_geometry(self.grid_wall,
reset_bounding_box=False)
for i, r in enumerate(self.regions):
frame_nn = mpu.warp_rect_img(r.rect_points, video_frame,
self.lm_input_length,
self.lm_input_length)
nn_data = dai.NNData()
nn_data.setLayer(
"input_1",
to_planar(frame_nn,
(self.lm_input_length, self.lm_input_length)))
if i == 0:
lm_rtrip_time = now(
) # We measure only for the first region
q_lm_in.send(nn_data)
# Get landmarks
inference = q_lm_out.get()
if i == 0:
lm_rtrip_time = now() - lm_rtrip_time
glob_lm_rtrip_time += lm_rtrip_time
nb_lm_inferences += 1
self.lm_postprocess(r, inference)
self.lm_render(annotated_frame, r)
if self.show_3d:
self.vis3d.poll_events()
self.vis3d.update_renderer()
if self.smoothing and self.nb_active_regions == 0:
self.filter.reset()
if self.input_type != "internal" and not self.crop:
annotated_frame = annotated_frame[self.pad_h:self.pad_h + h,
self.pad_w:self.pad_w + w]
if self.show_fps:
self.fps.display(annotated_frame,
orig=(50, 50),
size=1,
color=(240, 180, 100))
cv2.imshow("Blazepose", annotated_frame)
if self.output:
self.output.write(annotated_frame)
key = cv2.waitKey(1)
if key == ord('q') or key == 27:
break
elif key == 32:
# Pause on space bar
cv2.waitKey(0)
elif key == ord('1'):
self.show_pd_box = not self.show_pd_box
elif key == ord('2'):
self.show_pd_kps = not self.show_pd_kps
elif key == ord('3'):
self.show_rot_rect = not self.show_rot_rect
elif key == ord('4'):
self.show_landmarks = not self.show_landmarks
elif key == ord('5'):
self.show_scores = not self.show_scores
elif key == ord('6'):
self.show_gesture = not self.show_gesture
elif key == ord('f'):
self.show_fps = not self.show_fps
# Print some stats
print(f"# pose detection inferences : {nb_pd_inferences}")
print(f"# landmark inferences : {nb_lm_inferences}")
if self.input_type != "internal" and nb_pd_inferences != 0:
print(
f"Pose detection round trip : {glob_pd_rtrip_time/nb_pd_inferences*1000:.1f} ms"
)
if nb_lm_inferences != 0:
print(
f"Landmark round trip : {glob_lm_rtrip_time/nb_lm_inferences*1000:.1f} ms"
)
if self.output:
self.output.release()
if args.video:
return cap.read()
else:
return True, qRgb.get().getCvFrame()
while shouldRun():
read_correctly, frame = getFrame()
if not read_correctly:
break
fps.next_iter()
if not args.camera:
tstamp = time.monotonic()
lic_frame = dai.ImgFrame()
lic_frame.setData(to_planar(frame, (300, 300)))
lic_frame.setTimestamp(tstamp)
lic_frame.setType(dai.RawImgFrame.Type.BGR888p)
lic_frame.setWidth(300)
lic_frame.setHeight(300)
detIn.send(lic_frame)
detections = qDet.get().detections
cv2.putText(frame,
"Fps: {:.2f}".format(fps.fps()), (2, frame.shape[0] - 4),
cv2.FONT_HERSHEY_TRIPLEX,
0.4,
color=(255, 255, 255))
for detection in detections:
def main(args):
"""
Main programm loop.
Parameters
----------
args : command line arguments parsed by parse_arguments
"""
# Setup PoseEstimator, pipeline, windows with sliders for PoseEstimator
# options and load video if running on local video file
camera = args["video"] is None
if args["model"] not in model_list:
raise ValueError("Unknown model '{}'".format(args["model"]))
model_config = model_list[args["model"]]
pose_estimator = get_poseestimator(model_config, **args)
with dai.Device(
create_pipeline(model_config, camera, passthrough=True,
**args)) as device:
device.startPipeline()
if camera:
preview_queue = device.getOutputQueue("preview",
maxSize=4,
blocking=False)
else:
pose_in_queue = device.getInputQueue("pose_in")
pose_queue = device.getOutputQueue("pose")
passthrough_queue = device.getOutputQueue("passthrough")
# Load video if given in command line and set the variables used below
# to control FPS and looping of the video
if not camera:
if not os.path.exists(args["video"]):
raise ValueError("Video '{}' does not exist.".format(
args["video"]))
print("Loading video", args["video"])
video = cv2.VideoCapture(args["video"])
frame_interval = 1 / video.get(cv2.CAP_PROP_FPS)
last_frame_time = 0
frame_id = 0
else:
print("Running on OAK camera preview stream")
# Create windows for the original video and the video of frames from
# the NN passthrough. The window for the original video gets all the
# option sliders to change pose estimator config
video_window_name = "Original Video"
passthrough_window_name = "Processed Video"
video_window = SliderWindow(video_window_name)
cv2.namedWindow(passthrough_window_name)
video_window.add_poseestimator_options(pose_estimator, args)
# Start main loop
frame = None
keypoints = None
fps = FPS("Video", "NN", interval=0.1)
timer = Timer("inference", "decode")
while True:
# Check for and handle slider changes
slider_changes = video_window.get_changes()
for option_name, value in slider_changes.items():
pose_estimator.set_option(option_name, value)
fps.start_frame()
# Get next video frame (and submit for processing if local video)
if camera:
frame = preview_queue.get().getCvFrame()
fps.count("Video")
else:
frame_time = time.perf_counter()
# Only grab next frame from file at certain intervals to
# roughly preserve its original FPS
if frame_time - last_frame_time > frame_interval:
if video.grab():
__, frame = video.retrieve()
fps.count("Video")
last_frame_time = frame_time
# Create DepthAI ImgFrame object to pass to the
# camera
input_frame = pose_estimator.get_input_frame(frame)
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(frame_id)
frame_nn.setWidth(input_frame.shape[2])
frame_nn.setHeight(input_frame.shape[1])
frame_nn.setType(dai.RawImgFrame.Type.BGR888p)
frame_nn.setFrame(input_frame)
pose_in_queue.send(frame_nn)
frame_id += 1
else:
frame_id = 0
video.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
# Process pose data whenever a new packet arrives
if pose_queue.has():
raw_output = pose_queue.get()
timer.start_timer("decode")
keypoints = pose_estimator.get_pose_data(raw_output)
timer.stop_timer("decode")
fps.count("NN")
# When keypoints are available we should also have a
# passthrough frame to process and display. Make sure it is
# availabe to avoid suprises.
if passthrough_queue.has():
passthrough = passthrough_queue.get()
timer.frame_time("inference", passthrough)
passthrough_frame = passthrough.getCvFrame()
passthrough_frame = pose_estimator.get_original_frame(
passthrough_frame)
pose_estimator.draw_results(keypoints, passthrough_frame)
cv2.imshow(passthrough_window_name, passthrough_frame)
# Annotate current video frame with keypoints and FPS
if keypoints is not None:
pose_estimator.draw_results(keypoints, frame)
fps.update()
fps.display(frame)
cv2.imshow(video_window_name, frame)
if cv2.waitKey(1) == ord("q"):
break
fps.print_totals()
timer.print_times()
cv2.destroyAllWindows()
def run(self):
device = dai.Device(self.create_pipeline())
device.startPipeline()
q_video = device.getOutputQueue(name="cam_out",
maxSize=1,
blocking=False)
q_pd_in = device.getInputQueue(name="pd_in")
q_pd_out = device.getOutputQueue(name="pd_out",
maxSize=4,
blocking=True)
q_lm_out = device.getOutputQueue(name="lm_out",
maxSize=4,
blocking=True)
q_lm_in = device.getInputQueue(name="lm_in")
q_asl_out = device.getOutputQueue(name="asl_out",
maxSize=4,
blocking=True)
q_asl_in = device.getInputQueue(name="asl_in")
while True:
in_video = q_video.get()
video_frame = in_video.getCvFrame()
h, w = video_frame.shape[:2]
self.frame_size = max(h, w)
self.pad_h = int((self.frame_size - h) / 2)
self.pad_w = int((self.frame_size - w) / 2)
video_frame = cv2.copyMakeBorder(video_frame, self.pad_h,
self.pad_h, self.pad_w,
self.pad_w, cv2.BORDER_CONSTANT)
frame_nn = dai.ImgFrame()
frame_nn.setWidth(self.pd_input_length)
frame_nn.setHeight(self.pd_input_length)
frame_nn.setData(
to_planar(video_frame,
(self.pd_input_length, self.pd_input_length)))
q_pd_in.send(frame_nn)
annotated_frame = video_frame.copy()
# Get palm detection
inference = q_pd_out.get()
self.pd_postprocess(inference)
# Send data for hand landmarks
for i, r in enumerate(self.regions):
img_hand = mpu.warp_rect_img(r.rect_points, video_frame,
self.lm_input_length,
self.lm_input_length)
nn_data = dai.NNData()
nn_data.setLayer(
"input_1",
to_planar(img_hand,
(self.lm_input_length, self.lm_input_length)))
q_lm_in.send(nn_data)
# Retrieve hand landmarks
for i, r in enumerate(self.regions):
inference = q_lm_out.get()
self.lm_postprocess(r, inference)
hand_frame, handedness, hand_bbox = self.lm_render(
video_frame, annotated_frame, r)
# ASL recognition
if hand_frame is not None and self.asl_recognition:
hand_frame = cv2.resize(
hand_frame,
(self.asl_input_length, self.asl_input_length),
interpolation=cv2.INTER_NEAREST)
hand_frame = hand_frame.transpose(2, 0, 1)
nn_data = dai.NNData()
nn_data.setLayer("input", hand_frame)
q_asl_in.send(nn_data)
asl_result = np.array(q_asl_out.get().getFirstLayerFp16())
asl_idx = np.argmax(asl_result)
# Recognized ASL character is associated with a probability
asl_char = [
characters[asl_idx],
round(asl_result[asl_idx] * 100, 1)
]
selected_char = asl_char
current_char_queue = None
if handedness > 0.5:
current_char_queue = self.right_char_queue
else:
current_char_queue = self.left_char_queue
current_char_queue.append(selected_char)
# Peform filtering of recognition resuls using the previous 5 results
# If there aren't enough reults, take the first result as output
if len(current_char_queue) < 5:
selected_char = current_char_queue[0]
else:
char_candidate = {}
for i in range(5):
if current_char_queue[i][0] not in char_candidate:
char_candidate[current_char_queue[i][0]] = [
1, current_char_queue[i][1]
]
else:
char_candidate[current_char_queue[i]
[0]][0] += 1
char_candidate[current_char_queue[i][0]][
1] += current_char_queue[i][1]
most_voted_char = ""
max_votes = 0
most_voted_char_prob = 0
for key in char_candidate:
if char_candidate[key][0] > max_votes:
max_votes = char_candidate[key][0]
most_voted_char = key
most_voted_char_prob = round(
char_candidate[key][1] /
char_candidate[key][0], 1)
selected_char = (most_voted_char, most_voted_char_prob)
if self.show_asl:
gesture_string = "Letter: " + selected_char[
0] + ", " + str(selected_char[1]) + "%"
textSize = self.ft.getTextSize(gesture_string,
fontHeight=14,
thickness=-1)[0]
cv2.rectangle(video_frame,
(hand_bbox[0] - 5, hand_bbox[1]),
(hand_bbox[0] + textSize[0] + 5,
hand_bbox[1] - 18), (36, 152, 0), -1)
self.ft.putText(img=video_frame,
text=gesture_string,
org=(hand_bbox[0], hand_bbox[1] - 5),
fontHeight=14,
color=(255, 255, 255),
thickness=-1,
line_type=cv2.LINE_AA,
bottomLeftOrigin=True)
video_frame = video_frame[self.pad_h:self.pad_h + h,
self.pad_w:self.pad_w + w]
cv2.imshow("hand tracker", video_frame)
key = cv2.waitKey(1)
if key == ord('q') or key == 27:
break
elif key == 32:
# Pause on space bar
cv2.waitKey(0)
elif key == ord('1'):
self.show_hand_box = not self.show_hand_box
elif key == ord('2'):
self.show_landmarks = not self.show_landmarks
elif key == ord('3'):
self.show_asl = not self.show_asl
def main(args):
"""
Main programm loop.
Parameters
----------
args : command line arguments parsed by parse_arguments
"""
# Set up PoseEstimator and pipeline and load the dataset
if args["model"] not in model_list:
raise ValueError("Unknown model '{}'".format(args["model"]))
model_config = model_list[args["model"]]
pose_estimator = get_poseestimator(model_config, **args)
with dai.Device(
create_pipeline(model_config, camera=False, sync=True,
**args)) as device:
device.startPipeline()
pose_in_queue = device.getInputQueue("pose_in")
pose_queue = device.getOutputQueue("pose")
# Load coco keypoint annotations
coco_data = COCOData(**args)
# The keypoint selector allows to subset and re-order the predicted
# keypoints to align with the annotation format of the COCO dataset
keypoint_selector = coco_data.get_keypoint_selector(
pose_estimator.landmarks)
results_filename = "results_{model}_{conf}.json".format(
model=args["model"], conf=args["detection_threshold"])
# Re-use saved results if available. Iterate over dataset if not.
if not os.path.exists(results_filename):
timer = Timer("inference")
results = []
for img_id in trange(len(coco_data)):
img = coco_data.get_image(img_id)
input_frame = pose_estimator.get_input_frame(img)
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(img_id)
frame_nn.setWidth(input_frame.shape[2])
frame_nn.setHeight(input_frame.shape[1])
frame_nn.setFrame(input_frame)
timer.start_timer("inference")
pose_in_queue.send(frame_nn)
raw_output = pose_queue.get()
timer.stop_timer("inference")
pred_keypoints = pose_estimator.get_pose_data(raw_output)
# Convert each individual person into output format expected by
# COCO evaluation tools
for i in range(pred_keypoints.shape[0]):
score = pred_keypoints[i, :, 2]
score = np.sum(score) / np.count_nonzero(score)
pred_keypoints[i, :, 2] = 1
keypoints = np.around(pred_keypoints[i])
keypoints = keypoints[keypoint_selector]
results.append({
"image_id":
coco_data.get_coco_imageid(img_id),
"category_id":
1,
"keypoints":
keypoints.flatten().tolist(),
"score":
score
})
with open(results_filename, "w") as results_file:
json.dump(results, results_file)
timer.print_times()
coco_data.evaluate_results(results_filename)
roi = roi_data.roi.denormalize(depth_frame.shape[1],
depth_frame.shape[0])
top_left = roi.topLeft()
bottom_right = roi.bottomRight()
# Display SBB on the disparity map
cv2.rectangle(pv.get("depth"),
(int(top_left.x), int(top_left.y)),
(int(bottom_right.x), int(bottom_right.y)),
bbox_color, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
else:
read_correctly, host_frame = cap.read()
if not read_correctly:
break
scaled_frame = cv2.resize(host_frame, (in_w, in_h))
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(seq_num)
frame_nn.setWidth(in_w)
frame_nn.setHeight(in_h)
frame_nn.setData(to_planar(scaled_frame))
nn_in.send(frame_nn)
seq_num += 1
# if high quality, send original frames
if not conf.useHQ:
host_frame = scaled_frame
fps.tick('host')
in_nn = nn_out.tryGetAll()
if len(in_nn) > 0:
if nn_manager.output_format == "detection":
def main(args):
"""
Main programm loop.
Parameters
----------
args : command line arguments parsed by parse_arguments
"""
# Set up PoseEstimator, pipeline, window with sliders for PoseEstimator
# options and load image
if args["model"] not in model_list:
raise ValueError("Unknown model '{}'".format(args["model"]))
model_config = model_list[args["model"]]
pose_estimator = get_poseestimator(model_config, **args)
with dai.Device(create_pipeline(model_config, camera=False,
**args)) as device:
device.startPipeline()
pose_in_queue = device.getInputQueue("pose_in")
pose_queue = device.getOutputQueue("pose")
if not os.path.exists(args["image"]):
raise ValueError("Image '{}' does not exist.".format(
args["image"]))
print("Loading image", args["image"])
image = cv2.imread(args["image"])
window = SliderWindow("preview")
window.add_poseestimator_options(pose_estimator, args)
# Start main loop
frame = None
keypoints = None
raw_output = None
redraw = True
timer = Timer("inference", "decode")
while True:
# Check for and handle slider changes, redraw if there was a change
slider_changes = window.get_changes()
for option_name, value in slider_changes.items():
pose_estimator.set_option(option_name, value)
redraw = True
# On the first iteration pass the image to the NN for inference
# Raw results are kept after so changes in PoseEstimator options
# only require decoding the results again, not another inference
if frame is None:
frame = image.copy()
# Create DepthAI ImgFrame object to pass to the camera
input_frame = pose_estimator.get_input_frame(frame)
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(0)
frame_nn.setWidth(input_frame.shape[2])
frame_nn.setHeight(input_frame.shape[1])
frame_nn.setFrame(input_frame)
timer.start_timer("inference")
pose_in_queue.send(frame_nn)
# Store the raw results once available
if pose_queue.has():
raw_output = pose_queue.get()
timer.stop_timer("inference")
# Once we've got the raw output and again whenever an option
# changes we need to decode and draw
if redraw and raw_output is not None:
# keep a clean copy of the image for redrawing
frame = image.copy()
timer.start_timer("decode")
keypoints = pose_estimator.get_pose_data(raw_output)
timer.stop_timer("decode")
pose_estimator.draw_results(keypoints, frame)
redraw = False
cv2.imshow("preview", frame)
if cv2.waitKey(1) == ord("q"):
break
cv2.destroyAllWindows()
timer.print_times()
def process_image(self, img):
annotated_frame = img
if self.camera:
in_video = self.q_video.get()
# Convert NV12 to BGR
yuv = in_video.getData().reshape(
(in_video.getHeight() * 3 // 2, in_video.getWidth()))
video_frame = cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR_NV12)
else:
if self.image_mode is None:
vid_frame = img
height, width, _ = img.shape
self.video_size = int(min(width, height))
elif self.image_mode:
vid_frame = self.img
else:
ok, vid_frame = self.cap.read()
if not ok:
# print("not OK video frame")
return [], img #break
h, w = vid_frame.shape[:2]
dx = (w - self.video_size) // 2
dy = (h - self.video_size) // 2
video_frame = vid_frame[dy:dy + self.video_size,
dx:dx + self.video_size]
frame_nn = dai.ImgFrame()
frame_nn.setSequenceNum(self.seq_num)
frame_nn.setWidth(self.pd_input_length)
frame_nn.setHeight(self.pd_input_length)
frame_nn.setData(
to_planar(video_frame,
(self.pd_input_length, self.pd_input_length)))
self.q_pd_in.send(frame_nn)
self.seq_num += 1
annotated_frame = video_frame.copy()
inference = self.q_pd_out.get()
self.pd_postprocess(inference)
self.pd_render(annotated_frame)
# Hand landmarks
if self.use_lm:
for i, r in enumerate(self.regions):
img_hand = mpu.warp_rect_img(r.rect_points, video_frame,
self.lm_input_length,
self.lm_input_length)
nn_data = dai.NNData()
nn_data.setLayer(
"input_1",
to_planar(img_hand,
(self.lm_input_length, self.lm_input_length)))
self.q_lm_in.send(nn_data)
# Retrieve hand landmarks
for i, r in enumerate(self.regions):
inference = self.q_lm_out.get()
self.lm_postprocess(r, inference)
self.lm_render(annotated_frame, r)
return self.regions, annotated_frame
# TODO make it work taking args like in OpenCV:
# rr = ((256, 256), (128, 64), 30)
rr = dai.RotatedRect()
rr.center.x = rotated_rect[0][0]
rr.center.y = rotated_rect[0][1]
rr.size.width = rotated_rect[1][0]
rr.size.height = rotated_rect[1][1]
rr.angle = rotated_rect[2]
cfg = dai.ImageManipConfig()
cfg.setCropRotatedRect(rr, False)
cfg.setResize(120, 32)
# Send frame and config to device
if idx == 0:
w, h, c = frame.shape
imgFrame = dai.ImgFrame()
imgFrame.setData(to_planar(frame))
imgFrame.setType(dai.ImgFrame.Type.BGR888p)
imgFrame.setWidth(w)
imgFrame.setHeight(h)
q_manip_img.send(imgFrame)
else:
cfg.setReusePreviousImage(True)
q_manip_cfg.send(cfg)
# Get manipulated image from the device
transformed = q_manip_out.get().getCvFrame()
rec_placeholder_img = np.zeros((32, 200, 3), np.uint8)
transformed = np.hstack((transformed, rec_placeholder_img))
if cropped_stacked is None:
