![]() ![]() ctc_batch_cost def call ( self, y_true, y_pred ): batch_len = tf. Layer ): def _init_ ( self, name = None ): super (). For a detailed understanding of theĬlass CTCLayer ( keras. Our model will use the CTC loss as an endpoint layer. You will notice that the content of original image is kept as faithful as possible and has reduce_join ( num_to_char ( indices )) label = label. not_equal ( label, padding_token ))) # Convert to string. uint8 ) img = img # Gather indices where label!= padding_token. transpose ( img, perm = ) img = ( img * 255.0 ). subplots ( 4, 4, figsize = ( 15, 8 )) for i in range ( 16 ): img = images img = tf. take ( 1 ): images, labels = data, data _, ax = plt. prefetch ( AUTOTUNE )įor data in train_ds. map ( process_images_labels, num_parallel_calls = AUTOTUNE ) return dataset. from_tensor_slices (( image_paths, labels )). pad ( label, paddings = ], constant_values = padding_token ) return label def process_images_labels ( image_path, label ): image = preprocess_image ( image_path ) label = vectorize_label ( label ) return def prepare_dataset ( image_paths, labels ): dataset = tf. shape ( label ) pad_amount = max_len - length label = tf. unicode_split ( label, input_encoding = "UTF-8" )) length = tf. float32 ) / 255.0 return image def vectorize_label ( label ): label = char_to_num ( tf. decode_png ( image, 1 ) image = distortion_free_resize ( image, img_size ) image = tf. Putting the utilities togetherīatch_size = 64 padding_token = 99 image_width = 128 image_height = 32 def preprocess_image ( image_path, img_size = ( image_width, image_height )): image = tf. Notice how this resizing would have introduced unnecessary stretching. If we just go with the plain resizing then the images would look like so: pad ( image, paddings =, ,, ], ) image = tf. if pad_height % 2 != 0 : height = pad_height // 2 pad_height_top = height 1 pad_height_bottom = height else : pad_height_top = pad_height_bottom = pad_height // 2 if pad_width % 2 != 0 : width = pad_width // 2 pad_width_left = width 1 pad_width_right = width else : pad_width_left = pad_width_right = pad_width // 2 image = tf. ![]() shape ( image ) # Only necessary if you want to do same amount of padding on both sides. resize ( image, size = ( h, w ), preserve_aspect_ratio = True ) # Check tha amount of padding needed to be done. split ( " \n " )) return paths, corrected_samples train_img_paths, train_labels = get_image_paths_and_labels ( train_samples ) validation_img_paths, validation_labels = get_image_paths_and_labels ( validation_samples ) test_img_paths, test_labels = get_image_paths_and_labels ( test_samples )ĭef distortion_free_resize ( image, img_size ): w, h = img_size image = tf. join ( base_image_path, partI, partI "-" partII, image_name ".png" ) if os. split ( " " ) # Each line split will have this format for the corresponding image: # part1/part1-part2/part1-part2-part3.png image_name = line_split partI = image_name. ![]() join ( base_path, "words" ) def get_image_paths_and_labels ( samples ): paths = corrected_samples = for ( i, file_line ) in enumerate ( samples ): line_split = file_line. ![]()
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