def on_msg_camera_stream(self, msg):
"""Camera stream callback method.
Invoked upon the receipt of a message on the camera stream.
"""
self._logger.info('{} received frame {}'.format(
self.name, msg.timestamp))
start_time = time.time()
assert msg.encoding == 'BGR', 'Expects BGR frames'
image = np.expand_dims(msg.frame.transpose([2, 0, 1]), axis=0)
tensor = torch.tensor(image).float().cuda() / 255.0
output = self._network(tensor)
# XXX(ionel): Check if the model outputs Carla Cityscapes values or
# correct Cityscapes values.
output = transform_to_cityscapes_palette(
torch.argmax(output, dim=1).cpu().numpy()[0])
output = rgb_to_bgr(output)
if self._flags.visualize_segmentation_output:
add_timestamp(msg.timestamp, output)
cv2.imshow(self.name, output)
cv2.waitKey(1)
# Get runtime in ms.
runtime = (time.time() - start_time) * 1000
self._csv_logger.info('{},{},"{}",{}'.format(time_epoch_ms(),
self.name, msg.timestamp,
runtime))
output_msg = SegmentedFrameMessage(output, runtime, msg.timestamp)
self.get_output_stream(self._output_stream_name).send(output_msg)
def on_msg_camera_stream(self, msg):
"""Camera stream callback method.
Invoked upon the receipt of a message on the camera stream.
"""
self._logger.info('{} received frame {}'.format(
self.name, msg.timestamp))
start_time = time.time()
assert msg.encoding == 'BGR', 'Expects BGR frames'
image = torch.from_numpy(msg.frame.transpose([2, 0, 1
])).unsqueeze(0).float()
image_var = Variable(image, requires_grad=False, volatile=True)
final = self._model(image_var)[0]
_, pred = torch.max(final, 1)
pred = pred.cpu().data.numpy()[0]
image_np = self._pallete[pred.squeeze()]
# After we apply the pallete, the image is in RGB format
image_np = rgb_to_bgr(image_np)
if self._flags.visualize_segmentation_output:
add_timestamp(msg.timestamp, image_np)
cv2.imshow(self.name, image_np)
cv2.waitKey(1)
# Get runtime in ms.
runtime = (time.time() - start_time) * 1000
self._csv_logger.info('{},{},"{}",{}'.format(time_epoch_ms(),
self.name, msg.timestamp,
runtime))
output_msg = SegmentedFrameMessage(image_np, runtime, msg.timestamp)
self.get_output_stream(self._output_stream_name).send(output_msg)
def on_right_camera_msg(self, msg):
with self._lock:
# used as bgr_to_rgb, need RGB frames
img = rgb_to_bgr(msg.frame).astype(np.uint8)
img = preprocess.crop(img)
processed = preprocess.get_transform(augment=False)
img = processed(img)
self._right_imgs.append(img)
if self._left_imgs:
self.eval_depth(msg)
def display_front_frame(self, msg):
frame = transform_to_cityscapes_palette(msg.frame)
img = Image.fromarray(np.uint8(frame))
open_cv_image = rgb_to_bgr(np.array(img))
cv2.imshow(self.name, open_cv_image)
cv2.waitKey(1)