def _setup_image(model_input, batch_id=None):
# Handle torch Variable instances
if batch_id is None:
if isinstance(model_input, torch.autograd.Variable):
img = model_input.data
else:
img = model_input
else:
if isinstance(model_input, torch.autograd.Variable):
img = model_input.data[batch_id]
else:
img = model_input[batch_id]
# Copy to CPU if needed
if isinstance(img, torch.cuda.FloatTensor):
img = img.cpu()
# NumPy-ify and change from CHW to HWC
img = img.numpy().transpose((1, 2, 0))
# Undo image normalization
img = img * (-255) + 255
if img.shape[2] == 1:
# matplotlib plots grayscale images correctly only if you get rid of channel dimension
img = img[:, :, 0]
cmap = plt.cm.gray
else:
# OpenCV images are BGR whereas matplotlib assumes RGB
img = cv2.convertColor(img, cv.COLOR_BGR2RGB)
cmap = None # fallback to default
return img, cmap
def grab_frame(dev, width, height, col_from, col_to):
valid, raw = dev.read()
img = resize(convertColor(raw, COLOR_BGR2GRAY).astype(int16),
(width, height),
interpolation=CV_INTER_NN)[:, col_from:col_to]
return img
def grab_first(dev, res):
valid, raw = dev.read()
height, width, depth = raw.shape
new_height = res
new_width = int( float(new_height*width)/float(height) )
col_from = (new_width - res)//2
col_to = col_from + res
img = resize(convertColor(raw, COLOR_BGR2GRAY).astype(int16),
(new_width, new_height), interpolation=CV_INTER_NN)[:, col_from:col_to]
return img, new_width, new_height, col_from, col_to
def grab_first(dev, res):
valid, raw = dev.read()
height, width, depth = raw.shape
new_height = res
new_width = int(float(new_height * width) / float(height))
col_from = (new_width - res) // 2
col_to = col_from + res
img = resize(convertColor(raw, COLOR_BGR2GRAY).astype(int16),
(new_width, new_height),
interpolation=CV_INTER_NN)[:, col_from:col_to]
return img, new_width, new_height, col_from, col_to
def run(fps, vid_src, num_frames, width, height, dtype):
vid_src.set(cv2.CAP_PROP_FPS, fps)
stats = []
ref_frame = dtype(128)*numpy.ones((height, width), dtype=dtype)
curr_frame = numpy.zeros((height, width), dtype=dtype)
abs_diff = numpy.zeros((height, width), dtype=dtype)
diff = numpy.zeros((height, width), dtype=dtype)
valid = False
for i in range(num_frames):
curr_frame[:] = convertColor(vid_src.read()[1], COLOR_BGR2GRAY).astype(dtype)
cv2.imshow ("", convertColor(curr_frame.astype(numpy.uint8), COLOR_GRAY2RGB))
if cv2.waitKey(1) & 0xFF == ord('q'):#\
break
diff = curr_frame - ref_frame
ref_frame[:] = curr_frame
abs_diff = numpy.abs(diff).astype(dtype)
stats.append(get_stats(abs_diff))
return stats
def grab_frame(self):
#~ start_time = time.time()
if self._is_virtual_cam:
valid_frame, self._curr_frame[:] = self._video_source.read(
self._ref_frame)
return True
else:
if self._raw_frame is None or self._scale_changed:
valid_frame, self._raw_frame = self._video_source.read()
else:
valid_frame, self._raw_frame[:] = self._video_source.read()
#~ end_time = time.time()
#~ print("Time to capture frame = ", end_time - start_time)
if not valid_frame:
return False
#~ start_time = time.time()
if self._gray_frame is None or self._scale_changed:
self._gray_frame = cv2.convertColor(
self._raw_frame, cv2.COLOR_BGR2GRAY).astype(int16)
else:
self._gray_frame[:] = cv2.convertColor(self._raw_frame,
cv2.COLOR_BGR2GRAY)
#~ end_time = time.time()
#~ print("Time to convert to grayscale = ", end_time - start_time)
#~ start_time = time.time()
if self._get_sizes or self._scale_changed:
self._get_sizes = False
self._scale_changed = False
self._img_height, self._img_width = self._gray_frame.shape
self._img_ratio = float(self._img_width) / float(
self._img_height)
self._img_scaled_width = int(
float(self._out_res) * self._img_ratio)
if self._scale_img:
diff = self._img_scaled_width - self._out_res
self._img_width_crop_l = diff // 2
self._img_width_crop_r = self._img_width_crop_l + self._out_res
else:
diff = self._img_width - self._out_res
self._img_width_crop_l = diff // 2
self._img_width_crop_r = self._img_width_crop_l + self._out_res
diff = self._img_height - self._out_res
self._img_height_crop_u = diff // 2
self._img_height_crop_b = self._img_height_crop_u + self._out_res
self._tmp_frame = np.zeros(
(self._out_res, self._img_scaled_width))
#~ end_time = time.time()
#~ print("Time to calculate sizes = ", end_time - start_time)
#~ start_time = time.time()
if self._scale_img:
self._tmp_frame[:] = cv2.resize(
self._gray_frame, (self._img_scaled_width, self._out_res),
interpolation=cv2.INTER_NN)
self._curr_frame[:] = self._tmp_frame[:,
self._img_width_crop_l:
self._img_width_crop_r]
else:
self._curr_frame[:] = self._gray_frame[
self._img_height_crop_u:self._img_height_crop_b,
self._img_width_crop_l:self._img_width_crop_r]
#~ end_time = time.time()
#~ print("Time to scale frame = ", end_time - start_time)
return True
def grab_frame(self):
#~ start_time = time.time()
if self._is_virtual_cam:
valid_frame, self._curr_frame[:] = self._video_source.read(self._ref_frame)
return True
else:
if self._raw_frame is None or self._scale_changed:
valid_frame, self._raw_frame = self._video_source.read()
else:
valid_frame, self._raw_frame[:] = self._video_source.read()
#~ end_time = time.time()
#~ print("Time to capture frame = ", end_time - start_time)
if not valid_frame:
return False
#~ start_time = time.time()
if self._gray_frame is None or self._scale_changed:
self._gray_frame = cv2.convertColor(self._raw_frame, cv2.COLOR_BGR2GRAY).astype(int16)
else:
self._gray_frame[:] = cv2.convertColor(self._raw_frame, cv2.COLOR_BGR2GRAY)
#~ end_time = time.time()
#~ print("Time to convert to grayscale = ", end_time - start_time)
#~ start_time = time.time()
if self._get_sizes or self._scale_changed:
self._get_sizes = False
self._scale_changed = False
self._img_height, self._img_width = self._gray_frame.shape
self._img_ratio = float(self._img_width)/float(self._img_height)
self._img_scaled_width = int(float(self._out_res)*self._img_ratio)
if self._scale_img:
diff = self._img_scaled_width - self._out_res
self._img_width_crop_l = diff//2
self._img_width_crop_r = self._img_width_crop_l + self._out_res
else:
diff = self._img_width - self._out_res
self._img_width_crop_l = diff//2
self._img_width_crop_r = self._img_width_crop_l + self._out_res
diff = self._img_height - self._out_res
self._img_height_crop_u = diff//2
self._img_height_crop_b = self._img_height_crop_u + self._out_res
self._tmp_frame = np.zeros((self._out_res, self._img_scaled_width))
#~ end_time = time.time()
#~ print("Time to calculate sizes = ", end_time - start_time)
#~ start_time = time.time()
if self._scale_img:
self._tmp_frame[:] = cv2.resize(self._gray_frame, (self._img_scaled_width, self._out_res),
interpolation=cv2.INTER_NN)
self._curr_frame[:] = self._tmp_frame[:, self._img_width_crop_l: self._img_width_crop_r]
else:
self._curr_frame[:] = self._gray_frame[self._img_height_crop_u: self._img_height_crop_b,
self._img_width_crop_l: self._img_width_crop_r]
#~ end_time = time.time()
#~ print("Time to scale frame = ", end_time - start_time)
return True
def grab_frame(dev, width, height, col_from, col_to):
valid, raw = dev.read()
img = resize(convertColor(raw, COLOR_BGR2GRAY).astype(int16),
(width, height), interpolation=CV_INTER_NN)[:, col_from:col_to]
return img