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