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adding cnn-rnn training script
This commit is contained in:
vahidrezanezhad
parent
7bf5e077d9
commit
59e5a73654
4 changed files with 961 additions and 4 deletions
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@ -758,3 +758,86 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224
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model = Model(img_input , o)
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return model
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def cnn_rnn_ocr_model(image_height, image_width, n_classes, max_seq):
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input_img = tensorflow.keras.Input(shape=(image_height, image_width, 3), name="image")
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labels = tensorflow.keras.layers.Input(name="label", shape=(None,))
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x = tensorflow.keras.layers.Conv2D(64,kernel_size=(3,3),padding="same")(input_img)
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x = tensorflow.keras.layers.BatchNormalization(name="bn1")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu1")(x)
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x = tensorflow.keras.layers.Conv2D(64,kernel_size=(3,3),padding="same")(x)
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x = tensorflow.keras.layers.BatchNormalization(name="bn2")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu2")(x)
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x = tensorflow.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x)
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x = tensorflow.keras.layers.Conv2D(128,kernel_size=(3,3),padding="same")(x)
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x = tensorflow.keras.layers.BatchNormalization(name="bn3")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu3")(x)
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x = tensorflow.keras.layers.Conv2D(128,kernel_size=(3,3),padding="same")(x)
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x = tensorflow.keras.layers.BatchNormalization(name="bn4")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu4")(x)
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x = tensorflow.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x)
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x = tensorflow.keras.layers.Conv2D(256,kernel_size=(3,3),padding="same")(x)
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x = tensorflow.keras.layers.BatchNormalization(name="bn5")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu5")(x)
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x = tensorflow.keras.layers.Conv2D(256,kernel_size=(3,3),padding="same")(x)
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x = tensorflow.keras.layers.BatchNormalization(name="bn6")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu6")(x)
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x = tensorflow.keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2))(x)
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x = tensorflow.keras.layers.Conv2D(512,kernel_size=(3,3),padding="same")(x)
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x = tensorflow.keras.layers.BatchNormalization(name="bn7")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu7")(x)
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x = tensorflow.keras.layers.Conv2D(512,kernel_size=(16,1))(x)
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x = tensorflow.keras.layers.BatchNormalization(name="bn8")(x)
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x = tensorflow.keras.layers.Activation("relu", name="relu8")(x)
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x2d = tensorflow.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x)
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x4d = tensorflow.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x2d)
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new_shape = (x.shape[2], x.shape[3])
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new_shape2 = (x2d.shape[2], x2d.shape[3])
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new_shape4 = (x4d.shape[2], x4d.shape[3])
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x = tensorflow.keras.layers.Reshape(target_shape=new_shape, name="reshape")(x)
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x2d = tensorflow.keras.layers.Reshape(target_shape=new_shape2, name="reshape2")(x2d)
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x4d = tensorflow.keras.layers.Reshape(target_shape=new_shape4, name="reshape4")(x4d)
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xrnnorg = tensorflow.keras.layers.Bidirectional(tensorflow.keras.layers.LSTM(512, return_sequences=True, dropout=0.25))(x)
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xrnn2d = tensorflow.keras.layers.Bidirectional(tensorflow.keras.layers.LSTM(512, return_sequences=True, dropout=0.25))(x2d)
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xrnn4d = tensorflow.keras.layers.Bidirectional(tensorflow.keras.layers.LSTM(512, return_sequences=True, dropout=0.25))(x4d)
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xrnn2d = tensorflow.keras.layers.Reshape(target_shape=(1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
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xrnn4d = tensorflow.keras.layers.Reshape(target_shape=(1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
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xrnn2dup = tensorflow.keras.layers.UpSampling2D(size=(1, 2), interpolation="nearest")(xrnn2d)
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xrnn4dup = tensorflow.keras.layers.UpSampling2D(size=(1, 4), interpolation="nearest")(xrnn4d)
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xrnn2dup = tensorflow.keras.layers.Reshape(target_shape=(xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup)
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xrnn4dup = tensorflow.keras.layers.Reshape(target_shape=(xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup)
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addition = tensorflow.keras.layers.Add()([xrnnorg, xrnn2dup, xrnn4dup])
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addition_rnn = tensorflow.keras.layers.Bidirectional(tensorflow.keras.layers.LSTM(512, return_sequences=True, dropout=0.25))(addition)
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out = tensorflow.keras.layers.Conv1D(max_seq, 1, data_format="channels_first")(addition_rnn)
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out = tensorflow.keras.layers.BatchNormalization(name="bn9")(out)
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out = tensorflow.keras.layers.Activation("relu", name="relu9")(out)
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#out = tensorflow.keras.layers.Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)
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out = tensorflow.keras.layers.Dense(
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n_classes, activation="softmax", name="dense2"
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)(out)
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# Add CTC layer for calculating CTC loss at each step.
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output = CTCLayer(name="ctc_loss")(labels, out)
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model = tensorflow.keras.models.Model(inputs=[input_img, labels], outputs=output, name="handwriting_recognizer")
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return model
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@ -101,6 +101,20 @@ def config_params():
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degrading = False # If true, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" in config_params.json.
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brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json.
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binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
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image_inversion = False # If true, and if the binarized images are avilable the image inevrsion will be applied.
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white_noise_strap = False # If true, white noise will be applied on some straps on the textline image.
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textline_skewing = False # If true, textline images will be skewed for augmentation.
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textline_skewing_bin = False # If true, textline image skewing augmentation for binarized images will be applied if already are available.
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textline_left_in_depth = False # If true, left side of textline image will be displayed in depth.
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textline_left_in_depth_bin = False # If true, left side of textline binarized image (if available) will be displayed in depth.
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textline_right_in_depth = False # If true, right side of textline image will be displayed in depth.
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textline_right_in_depth_bin = False # If true, right side of textline binarized image (if available) will be displayed in depth.
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textline_up_in_depth = False # If true, upper side of textline image will be displayed in depth.
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textline_up_in_depth_bin = False # If true, upper side of textline binarized image (if available) will be displayed in depth.
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textline_down_in_depth = False # If true, lower side of textline image will be displayed in depth.
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textline_down_in_depth_bin = False # If true, lower side of textline binarized image (if available) will be displayed in depth.
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pepper_bin_aug = False # If true, pepper noise will be added to textline binarized image (if available).
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pepper_aug = False # If true, pepper noise will be added to textline image.
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adding_rgb_background = False
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adding_rgb_foreground = False
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add_red_textlines = False
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@ -111,7 +125,9 @@ def config_params():
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pretraining = False # Set to true to load pretrained weights of ResNet50 encoder.
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scaling_bluring = False # If true, a combination of scaling and blurring will be applied to the image.
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scaling_binarization = False # If true, a combination of scaling and binarization will be applied to the image.
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bin_deg = False # If true, a combination of degrading and binarization will be applied to the image.
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rotation = False # If true, a 90 degree rotation will be implemeneted.
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color_padding_rotation = False # If true, rotation and padding will be implemeneted.
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rotation_not_90 = False # If true rotation based on provided angles with thetha will be implemeneted.
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scaling_brightness = False # If true, a combination of scaling and brightening will be applied to the image.
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scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image.
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@ -119,6 +135,7 @@ def config_params():
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shuffle_indexes = None
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blur_k = None # Blur image for augmentation.
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scales = None # Scale patches for augmentation.
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padd_colors = None # padding colors. A list elements can be only white and black. like ["white", "black"] or only one of them ["white"]
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degrade_scales = None # Degrade image for augmentation.
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brightness = None # Brighten image for augmentation.
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flip_index = None # Flip image for augmentation.
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@ -145,6 +162,7 @@ def config_params():
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number_of_backgrounds_per_image = 1
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dir_rgb_backgrounds = None
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dir_rgb_foregrounds = None
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characters_txt_file = None # Directory of characters text file needed for cnn_rnn_ocr model training. The file ends with .txt
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@ex.automain
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def run(_config, n_classes, n_epochs, input_height,
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@ -159,7 +177,10 @@ def run(_config, n_classes, n_epochs, input_height,
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transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
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transformer_patchsize_x, transformer_patchsize_y,
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transformer_num_patches_xy, backbone_type, save_interval, flip_index, dir_eval, dir_output,
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pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds):
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pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds,
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dir_rgb_foregrounds, characters_txt_file, color_padding_rotation, bin_deg, image_inversion, white_noise_strap, textline_skewing, textline_skewing_bin,
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textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth, textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin,
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textline_down_in_depth, textline_down_in_depth_bin, pepper_bin_aug, pepper_aug, padd_colors):
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if dir_rgb_backgrounds:
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list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
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@ -375,6 +396,34 @@ def run(_config, n_classes, n_epochs, input_height,
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#os.system('rm -rf '+dir_eval_flowing)
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#model.save(dir_output+'/'+'model'+'.h5')
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elif task=="cnn-rnn-ocr":
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dir_img, dir_lab = get_dirs_or_files(dir_train)
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with open(characters_txt_file, 'r') as char_txt_f:
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characters = json.load(char_txt_f)
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AUTOTUNE = tf.data.AUTOTUNE
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# Mapping characters to integers.
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char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
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# Mapping integers back to original characters.
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num_to_char = StringLookup(
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vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
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)
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padding_token = len(characters) + 5
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ls_files_images = os.listdir(dir_img)
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train_ds = data_gen_ocr(padding_token, batchsize=n_batch, height=input_height, width=input_width, max_len=max_len, dir_ins=dir_train, ls_files_images,
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augmentation, color_padding_rotation, rotation=rotation_not_90, bluring_aug=blurring, degrading, bin_deg, brightening, w_padding=padding_white,
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rgb_fging=adding_rgb_foreground, rgb_bkding=adding_rgb_background, binarization, image_inversion, channel_shuffling=channels_shuffling, add_red_textline=add_red_textlines, white_noise_strap,
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textline_skewing, textline_skewing_bin, textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth,
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textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin, textline_down_in_depth, textline_down_in_depth_bin,
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pepper_bin_aug, pepper_aug, deg_scales=degrade_scales, number_of_backgrounds_per_image, thethas=thetha, brightness, padd_colors,
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shuffle_indexes, pepper_indexes, skewing_amplitudes)
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elif task=='classification':
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configuration()
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model = resnet50_classifier(n_classes, input_height, input_width, weight_decay, pretraining)
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@ -10,7 +10,213 @@ from scipy.ndimage.filters import gaussian_filter
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from tqdm import tqdm
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import imutils
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from tensorflow.keras.utils import to_categorical
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from PIL import Image, ImageEnhance
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from PIL import Image, ImageFile, ImageEnhance
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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def add_salt_and_pepper_noise(img, salt_prob, pepper_prob):
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"""
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Add salt-and-pepper noise to an image.
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Parameters:
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image: ndarray
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Input image.
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salt_prob: float
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Probability of salt noise.
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pepper_prob: float
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Probability of pepper noise.
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Returns:
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noisy_image: ndarray
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Image with salt-and-pepper noise.
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"""
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# Make a copy of the image
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noisy_image = np.copy(img)
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# Generate random noise
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total_pixels = img.size
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num_salt = int(salt_prob * total_pixels)
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num_pepper = int(pepper_prob * total_pixels)
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# Add salt noise
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coords = [np.random.randint(0, i - 1, num_salt) for i in img.shape[:2]]
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noisy_image[coords[0], coords[1]] = 255 # white pixels
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# Add pepper noise
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coords = [np.random.randint(0, i - 1, num_pepper) for i in img.shape[:2]]
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noisy_image[coords[0], coords[1]] = 0 # black pixels
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return noisy_image
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def invert_image(img):
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img_inv = 255 - img
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return img_inv
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def return_image_with_strapped_white_noises(img):
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img_w_noised = np.copy(img)
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img_h, img_width = img.shape[0], img.shape[1]
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n = 9
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p = 0.3
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num_windows = np.random.binomial(n, p, 1)[0]
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if num_windows<1:
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num_windows = 1
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loc_of_windows = np.random.uniform(0,img_width,num_windows).astype(np.int64)
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width_windows = np.random.uniform(10,50,num_windows).astype(np.int64)
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for i, loc in enumerate(loc_of_windows):
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noise = np.random.normal(0, 50, (img_h, width_windows[i], 3))
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try:
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img_w_noised[:, loc:loc+width_windows[i], : ] = noise[:,:,:]
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except:
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pass
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return img_w_noised
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def do_padding_for_ocr(img, percent_height, padding_color):
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padding_size = int( img.shape[0]*percent_height/2. )
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height_new = img.shape[0] + 2*padding_size
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width_new = img.shape[1] + 2*padding_size
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h_start = padding_size
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w_start = padding_size
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if padding_color == 'white':
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img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
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if padding_color == 'black':
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img_new = np.zeros((height_new, width_new, img.shape[2])).astype(float)
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img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
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return img_new
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def do_deskewing(img, amplitude):
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height, width = img.shape[:2]
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# Generate sinusoidal wave distortion with reduced amplitude
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#amplitude = 8 # 5 # Reduce the amplitude for less curvature
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frequency = 300 # Increase frequency to stretch the curve
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x_indices = np.tile(np.arange(width), (height, 1))
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y_indices = np.arange(height).reshape(-1, 1) + amplitude * np.sin(2 * np.pi * x_indices / frequency)
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# Convert indices to float32 for remapping
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map_x = x_indices.astype(np.float32)
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map_y = y_indices.astype(np.float32)
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# Apply the remap to create the curve
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curved_image = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
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return curved_image
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def do_left_in_depth(img):
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height, width = img.shape[:2]
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# Define the original corner points of the image
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src_points = np.float32([
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[0, 0], # Top-left corner
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[width, 0], # Top-right corner
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[0, height], # Bottom-left corner
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[width, height] # Bottom-right corner
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])
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# Define the new corner points for a subtle right-to-left tilt
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dst_points = np.float32([
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[2, 13], # Slight inward shift for top-left
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[width, 0], # Slight downward shift for top-right
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[2, height-13], # Slight inward shift for bottom-left
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[width, height] # Slight upward shift for bottom-right
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])
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# Compute the perspective transformation matrix
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matrix = cv2.getPerspectiveTransform(src_points, dst_points)
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# Apply the perspective warp
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warped_image = cv2.warpPerspective(img, matrix, (width, height))
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return warped_image
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def do_right_in_depth(img):
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height, width = img.shape[:2]
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# Define the original corner points of the image
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src_points = np.float32([
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[0, 0], # Top-left corner
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[width, 0], # Top-right corner
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[0, height], # Bottom-left corner
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[width, height] # Bottom-right corner
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])
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# Define the new corner points for a subtle right-to-left tilt
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dst_points = np.float32([
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[0, 0], # Slight inward shift for top-left
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[width, 13], # Slight downward shift for top-right
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[0, height], # Slight inward shift for bottom-left
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[width, height - 13] # Slight upward shift for bottom-right
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])
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# Compute the perspective transformation matrix
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matrix = cv2.getPerspectiveTransform(src_points, dst_points)
|
||||
|
||||
# Apply the perspective warp
|
||||
warped_image = cv2.warpPerspective(img, matrix, (width, height))
|
||||
return warped_image
|
||||
|
||||
def do_up_in_depth(img):
|
||||
# Get the dimensions of the image
|
||||
height, width = img.shape[:2]
|
||||
|
||||
# Define the original corner points of the image
|
||||
src_points = np.float32([
|
||||
[0, 0], # Top-left corner
|
||||
[width, 0], # Top-right corner
|
||||
[0, height], # Bottom-left corner
|
||||
[width, height] # Bottom-right corner
|
||||
])
|
||||
|
||||
# Define the new corner points to simulate a tilted perspective
|
||||
# Make the top part appear closer and the bottom part farther
|
||||
dst_points = np.float32([
|
||||
[50, 0], # Top-left moved inward
|
||||
[width - 50, 0], # Top-right moved inward
|
||||
[0, height], # Bottom-left remains the same
|
||||
[width, height] # Bottom-right remains the same
|
||||
])
|
||||
|
||||
# Compute the perspective transformation matrix
|
||||
matrix = cv2.getPerspectiveTransform(src_points, dst_points)
|
||||
|
||||
# Apply the perspective warp
|
||||
warped_image = cv2.warpPerspective(img, matrix, (width, height))
|
||||
return warped_image
|
||||
|
||||
|
||||
def do_down_in_depth(img):
|
||||
# Get the dimensions of the image
|
||||
height, width = img.shape[:2]
|
||||
|
||||
# Define the original corner points of the image
|
||||
src_points = np.float32([
|
||||
[0, 0], # Top-left corner
|
||||
[width, 0], # Top-right corner
|
||||
[0, height], # Bottom-left corner
|
||||
[width, height] # Bottom-right corner
|
||||
])
|
||||
|
||||
# Define the new corner points to simulate a tilted perspective
|
||||
# Make the top part appear closer and the bottom part farther
|
||||
dst_points = np.float32([
|
||||
[0, 0], # Top-left moved inward
|
||||
[width, 0], # Top-right moved inward
|
||||
[50, height], # Bottom-left remains the same
|
||||
[width - 50, height] # Bottom-right remains the same
|
||||
])
|
||||
|
||||
# Compute the perspective transformation matrix
|
||||
matrix = cv2.getPerspectiveTransform(src_points, dst_points)
|
||||
|
||||
# Apply the perspective warp
|
||||
warped_image = cv2.warpPerspective(img, matrix, (width, height))
|
||||
return warped_image
|
||||
|
||||
|
||||
def return_shuffled_channels(img, channels_order):
|
||||
|
|
@ -1055,3 +1261,620 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
|
|||
cv2.flip( cv2.imread(dir_img + '/' + im), f_i),
|
||||
cv2.flip(cv2.imread(dir_of_label_file), f_i),
|
||||
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
||||
|
||||
|
||||
|
||||
def data_gen_ocr(padding_token, batchsize=None, height=None, width=None, max_len=None, dir_ins=None, ls_files_images=None,
|
||||
augmentation=False, color_padding_rotation=False, rotation=False, bluring_aug=False, degrading=False, bin_deg=False, brightening=False, w_padding=False,
|
||||
rgb_fging=False, rgb_bkding=False, binarization=False, image_inversion=False, channel_shuffling=False, add_red_textline=False, white_noise_strap=False,
|
||||
textline_skewing=False, textline_skewing_bin=False, textline_left_in_depth=False, textline_left_in_depth_bin=False, textline_right_in_depth=False,
|
||||
textline_right_in_depth_bin=False, textline_up_in_depth=False, textline_up_in_depth_bin=False, textline_down_in_depth=False, textline_down_in_depth_bin=False,
|
||||
pepper_bin_aug=False, pepper_aug=False, deg_scales=None, number_of_backgrounds_per_image=None, thethas=None, brightness=None, padd_colors=None,
|
||||
shuffle_indexes=None, ):
|
||||
|
||||
random.shuffle(ls_files_images)
|
||||
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
while True:
|
||||
for i in ls_files_images:
|
||||
f_name = i.split('.')[0]
|
||||
|
||||
txt_inp = open(os.path.join(dir_ins, "labels/"+f_name+'.txt'),'r').read().split('\n')[0]
|
||||
|
||||
img = cv2.imread(os.path.join(dir_ins, "images/"+i) )
|
||||
img_bin_corr = cv2.imread(os.path.join(dir_ins, "images_bin/"+f_name+'.png') )
|
||||
|
||||
|
||||
if augmentation:
|
||||
img_out = scale_padd_image_for_ocr(img, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if color_padding_rotation:
|
||||
for index, thetha in enumerate(thetha_padd):
|
||||
for padd_col in padd_colors:
|
||||
img_out = rotation_not_90_func(do_padding(img, 1.2, padd_col), thetha)
|
||||
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if rotation:
|
||||
for index, thetha in enumerate(thethas):
|
||||
img_out = rotation_not_90_func(img, thetha)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if bluring_aug:
|
||||
for index, blur_type in enumerate(blurs):
|
||||
img_out = bluring(img, blur_type)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if degrading:
|
||||
for index, deg_scale_ind in enumerate(deg_scales):
|
||||
try:
|
||||
img_out = do_degrading(img, deg_scale_ind)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if bin_deg:
|
||||
for index, deg_scale_ind in enumerate(deg_scales):
|
||||
try:
|
||||
img_out = do_degrading(img_bin_corr, deg_scale_ind)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img_bin_corr)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if brightening:
|
||||
for index, bright_scale_ind in enumerate(brightness):
|
||||
try:
|
||||
img_out = do_brightening(dir_img, bright_scale_ind)
|
||||
except:
|
||||
img_out = np.copy(img)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if w_padding:
|
||||
for index, padding_size in enumerate(white_padds):
|
||||
for padd_col in padd_colors:
|
||||
img_out = do_padding(img, padding_size, padd_col)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if rgb_fging:
|
||||
for i_n in range(number_of_backgrounds_per_image):
|
||||
background_image_chosen_name = random.choice(list_all_possible_background_images)
|
||||
foreground_rgb_chosen_name = random.choice(list_all_possible_foreground_rgbs)
|
||||
|
||||
img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name)
|
||||
foreground_rgb_chosen = np.load(dir_rgb_foregrounds + '/' + foreground_rgb_chosen_name)
|
||||
|
||||
img_with_overlayed_background = return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin_corr, img_rgb_background_chosen, foreground_rgb_chosen)
|
||||
|
||||
img_out = scale_padd_image_for_ocr(img_with_overlayed_background, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if rgb_bkding:
|
||||
for i_n in range(number_of_backgrounds_per_image):
|
||||
background_image_chosen_name = random.choice(list_all_possible_background_images)
|
||||
img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name)
|
||||
img_with_overlayed_background = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background_chosen)
|
||||
|
||||
img_out = scale_padd_image_for_ocr(img_with_overlayed_background, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if binarization:
|
||||
img_out = scale_padd_image_for_ocr(img_bin_corr, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if image_inversion:
|
||||
img_out = invert_image(img_bin_corr)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :, :, :] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x = np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y = np.zeros((batch_size, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if channel_shuffling:
|
||||
for shuffle_index in shuffle_indexes:
|
||||
img_out = return_shuffled_channels(img, shuffle_index)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if add_red_textline:
|
||||
img_red_context = return_image_with_red_elements(img, img_bin_corr)
|
||||
|
||||
img_out = scale_padd_image_for_ocr(img_red_context, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if white_noise_strap:
|
||||
img_out = return_image_with_strapped_white_noises(img)
|
||||
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if textline_skewing:
|
||||
for index, des_scale_ind in enumerate(skewing_amplitudes):
|
||||
try:
|
||||
img_out = do_deskewing(img, des_scale_ind)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if textline_skewing_bin:
|
||||
for index, des_scale_ind in enumerate(skewing_amplitudes):
|
||||
try:
|
||||
img_out = do_deskewing(img_bin_corr, des_scale_ind)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img_bin_corr)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_left_in_depth:
|
||||
try:
|
||||
img_out = do_left_in_depth(img)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_left_in_depth_bin:
|
||||
try:
|
||||
img_out = do_left_in_depth(img_bin_corr)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img_bin_corr)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_right_in_depth:
|
||||
try:
|
||||
img_out = do_right_in_depth(img)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_right_in_depth_bin:
|
||||
try:
|
||||
img_out = do_right_in_depth(img_bin_corr)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img_bin_corr)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_up_in_depth:
|
||||
try:
|
||||
img_out = do_up_in_depth(img)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_up_in_depth_bin:
|
||||
try:
|
||||
img_out = do_up_in_depth(img_bin_corr)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img_bin_corr)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_down_in_depth:
|
||||
try:
|
||||
img_out = do_down_in_depth(img)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if textline_down_in_depth_bin:
|
||||
try:
|
||||
img_out = do_down_in_depth(img_bin_corr)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
except:
|
||||
img_out = np.copy(img_bin_corr)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
if pepper_bin_aug:
|
||||
for index, pepper_ind in enumerate(pepper_indexes):
|
||||
img_out = add_salt_and_pepper_noise(img_bin_corr, pepper_ind, pepper_ind)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
if pepper_aug:
|
||||
for index, pepper_ind in enumerate(pepper_indexes):
|
||||
img_out = add_salt_and_pepper_noise(img, pepper_ind, pepper_ind)
|
||||
img_out = scale_padd_image_for_ocr(img_out, height, width)
|
||||
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
|
||||
else:
|
||||
|
||||
img_out = scale_padd_image_for_ocr(img, height, width)
|
||||
ret_x[batchcount, :,:,:] = img_out[:,:,:]
|
||||
|
||||
ret_y[batchcount, :] = vectorize_label(txt_inp)
|
||||
|
||||
batchcount+=1
|
||||
|
||||
if batchcount>=batchsize:
|
||||
ret_x = ret_x/255.
|
||||
yield {"image": ret_x, "label": ret_y}
|
||||
ret_x= np.zeros((batchsize, height, width, 3)).astype(np.float32)
|
||||
ret_y= np.zeros((batchsize, max_len)).astype(np.int16)+padding_token
|
||||
batchcount = 0
|
||||
|
||||
|
||||
def return_muliplier_based_on_augmnentations(augmentation=False, color_padding_rotation=False, rotation=False, bluring_aug=False,
|
||||
degrading=False, bin_deg=False, brightening=False, w_padding=False,rgb_fging=False, rgb_bkding=False, binarization=False, image_inversion=False, channel_shuffling=False, add_red_textline=False, white_noise_strap=False,
|
||||
textline_skewing=False, textline_skewing_bin=False, textline_left_in_depth=False, textline_left_in_depth_bin=False, textline_right_in_depth=False, textline_right_in_depth_bin=False, textline_up_in_depth=False, textline_up_in_depth_bin=False, textline_down_in_depth=False, textline_down_in_depth_bin=False, pepper_bin_aug=False, pepper_aug=False, deg_scales=None, number_of_backgrounds_per_image=None, thethas=None, brightness=None, padd_colors=None):
|
||||
aug_multip = 1
|
||||
|
||||
if augmentation:
|
||||
if binarization:
|
||||
aug_multip = aug_multip + 1
|
||||
if image_inversion:
|
||||
aug_multip = aug_multip + 1
|
||||
if add_red_textline:
|
||||
aug_multip = aug_multip + 1
|
||||
if white_noise_strap:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_right_in_depth:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_left_in_depth:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_up_in_depth:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_down_in_depth:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_right_in_depth_bin:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_left_in_depth_bin:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_up_in_depth_bin:
|
||||
aug_multip = aug_multip + 1
|
||||
if textline_down_in_depth_bin:
|
||||
aug_multip = aug_multip + 1
|
||||
if rgb_fging:
|
||||
aug_multip = aug_multip + number_of_backgrounds_per_image
|
||||
if rgb_bkding:
|
||||
aug_multip = aug_multip + number_of_backgrounds_per_image
|
||||
if bin_deg:
|
||||
aug_multip = aug_multip + len(deg_scales)
|
||||
if degrading:
|
||||
aug_multip = aug_multip + len(deg_scales)
|
||||
if rotation:
|
||||
aug_multip = aug_multip + len(thethas)
|
||||
if textline_skewing:
|
||||
aug_multip = aug_multip + len(skewing_amplitudes)
|
||||
if textline_skewing_bin:
|
||||
aug_multip = aug_multip + len(skewing_amplitudes)
|
||||
if color_padding_rotation:
|
||||
aug_multip = aug_multip + len(thetha_padd)*len(padd_colors)
|
||||
if channel_shuffling:
|
||||
aug_multip = aug_multip + len(shuffle_indexes)
|
||||
if bluring_aug:
|
||||
aug_multip = aug_multip + len(blurs)
|
||||
if brightening:
|
||||
aug_multip = aug_multip + len(brightness)
|
||||
if w_padding:
|
||||
aug_multip = aug_multip + len(white_padds)*len(padd_colors)
|
||||
if pepper_aug:
|
||||
aug_multip = aug_multip + len(pepper_indexes)
|
||||
if pepper_bin_aug:
|
||||
aug_multip = aug_multip + len(pepper_indexes)
|
||||
|
||||
return aug_multip
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@
|
|||
"backbone_type" : "transformer",
|
||||
"task": "segmentation",
|
||||
"n_classes" : 2,
|
||||
"max_len": 280,
|
||||
"n_epochs" : 0,
|
||||
"input_height" : 448,
|
||||
"input_width" : 448,
|
||||
|
|
@ -34,7 +35,7 @@
|
|||
"transformer_layers": 1,
|
||||
"transformer_num_heads": 1,
|
||||
"transformer_cnn_first": false,
|
||||
"blur_k" : ["blur","guass","median"],
|
||||
"blur_k" : ["blur","gauss","median"],
|
||||
"scales" : [0.6, 0.7, 0.8, 0.9],
|
||||
"brightness" : [1.3, 1.5, 1.7, 2],
|
||||
"degrade_scales" : [0.2, 0.4],
|
||||
|
|
@ -53,6 +54,7 @@
|
|||
"dir_output": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/output_new",
|
||||
"dir_rgb_backgrounds": "/home/vahid/Documents/1_2_test_eynollah/set_rgb_background",
|
||||
"dir_rgb_foregrounds": "/home/vahid/Documents/1_2_test_eynollah/out_set_rgb_foreground",
|
||||
"dir_img_bin": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin"
|
||||
"dir_img_bin": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin",
|
||||
"characters_txt_file":"dir_of_characters_txt_file_for_ocr"
|
||||
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue