def get_data(self, img_path): """ 1. load image from img_path. 2. resize or oversampling. 3. transformer data: transpose, channel swap, sub mean. return K x H x W ndarray. img_path: image path. """ image = image_util.load_image(img_path, self.is_color) # Another way to extract oversampled features is that # cropping and averaging from large feature map which is # calculated by large size of image. # This way reduces the computation. if self.oversample: # image_util.resize_image: short side is self.resize_dim image = image_util.resize_image(image, self.resize_dim) image = np.array(image) input = np.zeros((1, image.shape[0], image.shape[1], 3), dtype=np.float32) input[0] = image.astype(np.float32) input = image_util.oversample(input, self.crop_dims) else: image = image.resize(self.crop_dims, Image.ANTIALIAS) input = np.zeros((1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32) input[0] = np.array(image).astype(np.float32) data_in = [] for img in input: img = self.transformer.transformer(img).flatten() data_in.append([img.tolist()]) # paddle input: [[[]],[[]],...], [[]] is one sample. return data_in
def get_data(self, img_path): """ 1. load image from img_path. 2. resize or oversampling. 3. transformer data: transpose, channel swap, sub mean. return K x H x W ndarray. img_path: image path. """ image = image_util.load_image(img_path, self.is_color) # Another way to extract oversampled features is that # cropping and averaging from large feature map which is # calculated by large size of image. # This way reduces the computation. if self.oversample: # image_util.resize_image: short side is self.resize_dim image = image_util.resize_image(image, self.resize_dim) image = np.array(image) input = np.zeros( (1, image.shape[0], image.shape[1], 3), dtype=np.float32) input[0] = image.astype(np.float32) input = image_util.oversample(input, self.crop_dims) else: image = image.resize(self.crop_dims, Image.ANTIALIAS) input = np.zeros( (1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32) input[0] = np.array(image).astype(np.float32) data_in = [] for img in input: img = self.transformer.transformer(img).flatten() data_in.append([img.tolist()]) # paddle input: [[[]],[[]],...], [[]] is one sample. return data_in
def get_data(self, img_path): """ 1. load image from img_path. 2. resize or oversampling. 3. transformer data: transpose, sub mean. return K x H x W ndarray. img_path: image path. """ image = image_util.load_image(img_path, self.is_color) if self.oversample: # image_util.resize_image: short side is self.resize_dim image = image_util.resize_image(image, self.resize_dim) image = np.array(image) input = np.zeros((1, image.shape[0], image.shape[1], 3), dtype=np.float32) input[0] = image.astype(np.float32) input = image_util.oversample(input, self.crop_dims) else: image = image.resize(self.crop_dims, Image.ANTIALIAS) input = np.zeros((1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32) input[0] = np.array(image).astype(np.float32) data_in = [] for img in input: img = self.transformer.transformer(img).flatten() data_in.append([img.tolist()]) return data_in
def get_data(self, img_path): """ 1. load image from img_path. 2. resize or oversampling. 3. transformer data: transpose, sub mean. return K x H x W ndarray. img_path: image path. """ image = image_util.load_image(img_path, self.is_color) if self.oversample: # image_util.resize_image: short side is self.resize_dim image = image_util.resize_image(image, self.resize_dim) image = np.array(image) input = np.zeros( (1, image.shape[0], image.shape[1], 3), dtype=np.float32) input[0] = image.astype(np.float32) input = image_util.oversample(input, self.crop_dims) else: image = image.resize(self.crop_dims, Image.ANTIALIAS) input = np.zeros( (1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32) input[0] = np.array(image).astype(np.float32) data_in = [] for img in input: img = self.transformer.transformer(img).flatten() data_in.append([img.tolist()]) return data_in