def get_img(img_path): img = read_file(img_path) img = decode_jpeg(img, channels=3) img = resize(img, [image_size, image_size]) img = 1 - img/255. # We would rather have the whole white void area be full of zeros than ones img = tf.convert_to_tensor([img]) return img
def checkDecodingTF_Image_Decode(filename): can_load = True try: im = io.read_file(filename) im = image.decode_image(im, channels=3, expand_animations=False).numpy() except Exception as e: can_load = False return can_load
def process_path(file_path): print(file_path) file = read_file(file_path) file = image.decode_jpeg(file, channels=3) file = cast(file, float32) file = preprocess_input(file) file = image.resize(file, [ROW, COL]) return file
def load_image(image_path): """ Convert all image into array of shape (1, width, height, 3) """ image = io.read_file(image_path) image = img.decode_image(image, channels=3) image = img.convert_image_dtype(image, float32) image = img.resize(image, (width, height)) image = image[newaxis, :] return image
def image_generator(test_size=0.2, testing=False): path = '/content/drive/My Drive/Flickr_Faces' no_of_images = len(os.listdir(path)) if testing: list_of_images = os.listdir(path)[int(no_of_images * test_size):] else: list_of_images = os.listdir(path)[:int(no_of_images * test_size)] for filename in list_of_images: img = os.path.join(path, filename) img = read_file(img) img = decode_png(img, channels=3) yield img / 255
def load_img(img_path): img = io.read_file(img_path) img = tf.image.decode_image(img, channels=3) img = tf.image.convert_image_dtype(img, tf.float32) shape = tf.cast(tf.shape(img)[:-1], tf.float32) long_dim = max(shape) scale = FLAGS.max_dim / long_dim new_shape = tf.cast(shape * scale, tf.int32) img = tf.image.resize(img, new_shape, antialias=True) img = img[tf.newaxis, :] return img
def image_import(all_paths, img_size, ch=3): """Function to convert two images into tensors for putting it into the network Arguments: all_path - list of paths to images, img_size - image size ch - kind of image color channels Return: list of tensors""" images = [ image.resize(image.decode_image(io.read_file(path)), [img_size, img_size, ch]) for path in all_paths ] images /= 255.0 print('Images are ready') return images
def song_vectors(song_dir): dir_name = '../tensors/' with open('song_vectors.txt', 'w') as f: f_index = 0 for file_name in os.listdir(song_dir): raw_audio = io.read_file(song_dir + file_name) song_vector, sample_rate = audio.decode_wav(raw_audio, desired_samples=100000) song_pickle = open(dir_name + 'song_tensor' + str(f_index), 'wb') rate_pickle = open(dir_name + 'rate_tensor' + str(f_index), 'wb') pickle.dump(song_vector, song_pickle) pickle.dump(sample_rate, rate_pickle) song_pickle.close() rate_pickle.close() f.write(str(sample_rate) + ':') for tensor in song_vector: f.write(str(tensor)) f.write('\n') f_index += 1
def load_and_preprocess_image(self, path): image = tf_io.read_file(path) return self.preprocess_image(image)
def load_image(file_path): img = read_file(file_path) # decode and resize image img = decode_png(img, channels=3) img = resize(img, [32, 32]) return img
def handle_image_path(img_path): img = read_file(img_path) img = decode_jpeg(img, channels=3) img = resize_images(img, [image_size, image_size]) img = img / 255.0 return img
def load_image(image_path, img_width=299, img_height=299): img = io.read_file(image_path) img = image.decode_jpeg(img, channels=3) img = image.resize(img, (img_width, img_height)) return img
def from_file_system(cls, path: str): image = read_file(path) return cls.from_raw(image)