def preprocess_image(self, image_path, mask_path):
image = load_image(image_path)
mask = load_image(mask_path)
image = resize(image, self.size)
mask = resize(mask, self.size)
mask = (mask == 1) * 1.0
if self.flip:
if self.prng.choice([True, False]):
image = np.flip(image, axis=1)
mask = np.flip(mask, axis=1)
return image * mask
def preprocess_image(self, image_path):
image = load_image(image_path)
image = resize(image, self.size)
if self.flip:
if self.prng.choice([True, False]):
image = np.flip(image, axis=1)
return image
def get_example(self, i):
raw_idx = self.indices[i]
return {
"image": resize(self.raw_data.load_key("image", raw_idx),
self.size),
"identity": self.labels["identity"][i],
"frame_idx": self.labels["frame_idx"][i]
}
def preprocess_image(self, image_path: str, mask_path: str) -> np.ndarray:
image = load_image(image_path)
mask = load_image(mask_path)
mask = mask == self.mask_label
if self.invert_mask:
mask = np.logical_not(mask)
image = image * 1.0 * mask
image = resize(image, self.size)
return image
def preprocess_image(self, image_path: str, mask_path: str) -> np.ndarray:
image = load_image(image_path)
mask = load_image(mask_path)
mask = mask == self.mask_label
if self.invert_mask:
mask = np.logical_not(mask)
image = resize(image, self.size)
mask = cv2.resize(1 * mask.astype(np.uint8), self.size,
cv2.INTER_NEAREST)
return image, mask
def preprocess_image(self, image_path: str, mask_path: str) -> np.ndarray:
image = load_image(image_path)
mask = load_image(mask_path)
mask = mask == self.mask_label
if self.invert_mask:
mask = np.logical_not(mask)
image = image * 1.0 * mask
image = resize(image, self.size)
if self.flip:
if self.prng.choice([True, False]):
image = np.flip(image, axis=1)
return image
def preprocess_image(self, image_path):
image = load_image(image_path)
return resize(image, self.size)
def preprocess_image(self, image_path: str) -> np.ndarray:
image = load_image(image_path)
image = resize(image, self.size)
return image
def preprocess(self, image):
image = image.astype(np.float32)
image = image / 127.5 - 1.0
r = resize(image, self.im_shape)
return np.expand_dims(r, -1)
def preprocess_image(self, image_path):
image = load_image(image_path)
image = resize(image, self.size)
image = center_crop(image)
return image
