def preprocess(self, frame, rois, landmarks, headposes):
assert len(frame.shape) == 4, "Frame shape should be [1, c, h, w]"
inputs = cut_rois(frame, rois)
output = []
for input, roi, landmark, headpose in zip(inputs, rois, landmarks,
headposes):
boxLeft = self.createEyeBoundingBox(landmark.left_eye[0],
landmark.left_eye[1])
boxRight = self.createEyeBoundingBox(landmark.right_eye[0],
landmark.right_eye[1])
midLeft = (landmark.left_eye[0] + landmark.left_eye[1]) / 2
midRight = (landmark.right_eye[0] + landmark.right_eye[1]) / 2
scaledBoxLeft = self.BoundingBox(boxLeft.position * roi.size,
boxLeft.size * roi.size)
scaledBoxRight = self.BoundingBox(boxRight.position * roi.size,
boxRight.size * roi.size)
imgLeft = resize_input(cut_roi(input, scaledBoxLeft),
self.input_shape_left)
imgRight = resize_input(cut_roi(input, scaledBoxRight),
self.input_shape_right)
output.append(
self.PreProcessResult(imgLeft, imgRight, midLeft, midRight,
headpose))
return output
def preprocess(self, frame, rois):
inputs = cut_rois(frame, rois)
inputs = [
resize_input(input, self.input_shape, self.nchw_layout)
for input in inputs
]
return inputs
def preprocess(self, frame):
# prepare the frames to fit openvino's requirement of nchw
assert len(frame.shape) == 4, "Frame shape should be [1, c, h, w]"
assert frame.shape[0] == 1
assert frame.shape[1] == 3
input = resize_input(frame, self.input_shape)
return input
def preprocess(self, frame, rois, landmarks):
image = frame.copy()
inputs = cut_rois(image, rois)
self._align_rois(inputs, landmarks)
inputs = [
resize_input(input, self.input_shape, self.nchw_layout)
for input in inputs
]
return inputs
def preprocess(self, frame, rois, landmarks):
assert len(frame.shape) == 4, "Frame shape should be [1, c, h, w]"
inputs = cut_rois(frame, rois)
self._align_rois(inputs, landmarks)
inputs = [resize_input(input, self.input_shape) for input in inputs]
return inputs
def preprocess(self, frame):
assert len(frame.shape) == 4, "Frame shape should be [1, c, h, w]"
assert frame.shape[0] == 1
assert frame.shape[1] == 3
input = resize_input(frame, self.input_shape)
return input
def preprocess(self, frame):
self.input_size = frame.shape
return resize_input(frame, self.input_shape)
def main(args):
if not os.path.isfile(args.input):
print("Error : {} file doesn't exist".format(args.input),
file=sys.stderr)
exit(1)
start = time.time()
gpu_ids = args.gpu
prefs = {
"titsize": args.bsize,
"aursize": args.asize,
"nipsize": args.nsize,
"vagsize": args.vsize,
"hairsize": args.hsize
}
if args.cpu:
gpu_ids = None
elif gpu_ids is None:
gpu_ids = [0]
if not args.gif:
# Read image
file = open(args.input, "rb")
image_bytes = bytearray(file.read())
np_image = np.asarray(image_bytes, dtype=np.uint8)
image = cv2.imdecode(np_image, cv2.IMREAD_COLOR)
# See if image loaded correctly
if image is None:
print("Error : {} file is not valid".format(args.input),
file=sys.stderr)
exit(1)
# Preprocess
if args.overlay:
original_image = image.copy()
image = utils.crop_input(image, args.overlay[0], args.overlay[1],
args.overlay[2], args.overlay[3])
elif args.auto_resize:
image = utils.resize_input(image)
elif args.auto_resize_crop:
image = utils.resize_crop_input(image)
elif args.auto_rescale:
image = utils.rescale_input(image)
# See if image has the correct shape after preprocessing
if image.shape != (512, 512, 3):
print("Error : image is not 512 x 512, got shape: {}".format(
image.shape),
file=sys.stderr)
exit(1)
# Process
if args.n_runs is None or args.n_runs == 1:
result = process(image, gpu_ids, prefs)
if args.overlay:
result = utils.overlay_original_img(original_image, result,
args.overlay[0],
args.overlay[1],
args.overlay[2],
args.overlay[3])
cv2.imwrite(args.output, result)
else:
base_output_filename = utils.strip_file_extension(
args.output, ".png")
def process_one_image(i):
result = process(image, gpu_ids, prefs)
if args.overlay:
result = utils.overlay_original_img(
original_image, result, args.overlay[0],
args.overlay[1], args.overlay[2], args.overlay[3])
cv2.imwrite(base_output_filename + "%03d.png" % i, result)
if args.cpu:
pool = ThreadPool(args.n_cores)
pool.map(process_one_image, range(args.n_runs))
pool.close()
pool.join()
else:
for i in range(args.n_runs):
process_one_image(i)
else:
# Read images
gif_imgs = imageio.mimread(args.input)
print("Total {} frames in the gif!".format(len(gif_imgs)))
# Preprocess
if args.auto_resize:
gif_imgs = [utils.resize_input(img) for img in gif_imgs]
elif args.auto_resize_crop:
gif_imgs = [utils.resize_crop_input(img) for img in gif_imgs]
elif args.auto_rescale:
gif_imgs = [utils.rescale_input(img) for img in gif_imgs]
# Process
if args.n_runs is None or args.n_runs == 1:
process_gif_wrapper(
gif_imgs,
args.output if args.output != "output.png" else "output.gif",
gpu_ids, prefs, args.n_cores)
else:
base_output_filename = utils.strip_file_extension(
args.output,
".gif") if args.output != "output.png" else "output"
for i in range(args.n_runs):
process_gif_wrapper(gif_imgs,
base_output_filename + "%03d.gif" % i,
gpu_ids, prefs, args.n_cores)
end = time.time()
duration = end - start
# Done
print("Done! We have taken", round(duration, 2), "seconds")
# Exit
sys.exit()
