Merge f60e0543ab into 8a9b4f8f55

docs/train.md

@@ -13,7 +13,7 @@ The following three tasks can all be accomplished using the code in the
 * train a model
 * inference with the trained model
 
-## Training , evaluation and output 
+## Training, evaluation and output 
 
 The train and evaluation folders should contain subfolders of `images` and `labels`.
 
@@ -22,11 +22,13 @@ The output folder should be an empty folder where the output model will be writt
 ## Generate training dataset
 
 The script `generate_gt_for_training.py` is used for generating training datasets. As the results of the following
-command demonstrates, the dataset generator provides three different commands:
+command demonstrates, the dataset generator provides several subcommands:
 
-`python generate_gt_for_training.py --help`
+```sh
+eynollah-training generate-gt --help
+```
 
-These three commands are:
+The three most important subcommands are:
 
 * image-enhancement
 * machine-based-reading-order
@@ -38,7 +40,7 @@ Generating a training dataset for image enhancement is quite straightforward. Al
 high-resolution images. The training dataset can then be generated using the following command:
 
 ```sh
-python generate_gt_for_training.py image-enhancement \
+eynollah-training image-enhancement \
   -dis "dir of high resolution images" \
   -dois "dir where degraded images will be written" \
   -dols "dir where the corresponding high resolution image will be written as label" \
@@ -69,7 +71,7 @@ to filter out regions smaller than this minimum size. This minimum size is defin
 to the image area, with a default value of zero. To run the dataset generator, use the following command:
 
 ```shell
-python generate_gt_for_training.py machine-based-reading-order \
+eynollah-training generate-gt machine-based-reading-order \
   -dx "dir of GT xml files" \
   -domi "dir where output images will be written" \
 "" -docl "dir where the labels will be written" \
@@ -144,7 +146,7 @@ region" are also present in the label. However, other regions like "noise region
 included in the label PNG file, even if they have information in the page XML files, as we chose not to include them.
 
 ```sh
-python generate_gt_for_training.py pagexml2label \
+eynollah-training generate-gt pagexml2label \
   -dx "dir of GT xml files" \
   -do "dir where output label png files will be written" \
   -cfg "custom config json file" \
@@ -198,7 +200,7 @@ provided to ensure that they are cropped in sync with the labels. This ensures t
 required for training are obtained. The command should resemble the following:
 
 ```sh
-python generate_gt_for_training.py pagexml2label \
+eynollah-training generate-gt pagexml2label \
   -dx "dir of GT xml files" \
   -do "dir where output label png files will be written" \
   -cfg "custom config json file" \
@@ -261,7 +263,7 @@ And the "dir_eval" the same structure as train directory:
 The classification model can be trained using the following command line:
 
 ```sh
-python train.py with config_classification.json
+eynollah-training train with config_classification.json
 ```
 
 As evident in the example JSON file above, for classification, we utilize a "f1_threshold_classification" parameter.
@@ -395,7 +397,9 @@ And the "dir_eval" the same structure as train directory:
 After configuring the JSON file for segmentation or enhancement, training can be initiated by running the following
 command, similar to the process for classification and reading order:
 
-`python train.py with config_classification.json`
+```
+eynollah-training train with config_classification.json`
+```
 
 #### Binarization
 
@@ -679,7 +683,7 @@ For conducting inference with a trained model, you simply need to execute the fo
 directory of the model and the image on which to perform inference:
 
 ```sh
-python inference.py -m "model dir" -i "image"
+eynollah-training inference -m "model dir" -i "image"
 ```
 
 This will straightforwardly return the class of the image.
@@ -691,7 +695,7 @@ without the reading order. We simply need to provide the model directory, the XM
 new XML file with the added reading order will be written to the output directory with the same name. We need to run:
 
 ```sh
-python inference.py \
+eynollah-training inference \
   -m "model dir" \
   -xml "page xml file" \
   -o "output dir to write new xml with reading order"
@@ -702,7 +706,7 @@ python inference.py \
 For conducting inference with a trained model for segmentation and enhancement you need to run the following command line:
 
 ```sh
-python inference.py \
+eynollah-training inference \
   -m "model dir" \
   -i "image" \
   -p \

pyproject.toml

@@ -31,6 +31,7 @@ classifiers = [
 
 [project.scripts]
 eynollah = "eynollah.cli:main"
+eynollah-training = "eynollah.training.cli:main"
 ocrd-eynollah-segment = "eynollah.ocrd_cli:main"
 ocrd-sbb-binarize = "eynollah.ocrd_cli_binarization:main"
 

src/eynollah/training/build_model_load_pretrained_weights_and_save.py

@@ -1,20 +1,15 @@
-import os
-import sys
+import click
 import tensorflow as tf
-import warnings
-from tensorflow.keras.optimizers import *
-from sacred import Experiment
-from models import *
-from utils import *
-from metrics import *
+
+from .models import resnet50_unet
 
 
 def configuration():
     gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
     session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
 
-
-if __name__ == '__main__':
+@click.command()
+def build_model_load_pretrained_weights_and_save():
     n_classes = 2
     input_height = 224
     input_width = 448

src/eynollah/training/cli.py

@@ -0,0 +1,26 @@
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
+
+import click
+import sys
+
+from .build_model_load_pretrained_weights_and_save import build_model_load_pretrained_weights_and_save
+from .generate_gt_for_training import main as generate_gt_cli
+from .inference import main as inference_cli
+from .train import ex
+
+@click.command(context_settings=dict(
+        ignore_unknown_options=True,
+))
+@click.argument('SACRED_ARGS', nargs=-1, type=click.UNPROCESSED)
+def train_cli(sacred_args):
+    ex.run_commandline([sys.argv[0]] + list(sacred_args))
+
+@click.group('training')
+def main():
+    pass
+
+main.add_command(build_model_load_pretrained_weights_and_save)
+main.add_command(generate_gt_cli, 'generate-gt')
+main.add_command(inference_cli, 'inference')
+main.add_command(train_cli, 'train')

src/eynollah/training/generate_gt_for_training.py

@@ -1,9 +1,28 @@
 import click
 import json
-from gt_gen_utils import *
+import os
 from tqdm import tqdm
 from pathlib import Path
 from PIL import Image, ImageDraw, ImageFont
+import cv2
+import numpy as np
+
+from eynollah.training.gt_gen_utils import (
+    filter_contours_area_of_image,
+    find_format_of_given_filename_in_dir,
+    find_new_features_of_contours,
+    fit_text_single_line,
+    get_content_of_dir,
+    get_images_of_ground_truth,
+    get_layout_contours_for_visualization,
+    get_textline_contours_and_ocr_text,
+    get_textline_contours_for_visualization,
+    overlay_layout_on_image,
+    read_xml,
+    resize_image,
+    visualize_image_from_contours,
+    visualize_image_from_contours_layout
+)
 
 @click.group()
 def main():
@@ -562,6 +581,3 @@ def visualize_ocr_text(xml_file, dir_xml, dir_out):
                     # Draw the text
                     draw.text((text_x, text_y), ocr_texts[index], fill="black", font=font)
         image_text.save(os.path.join(dir_out, f_name+'.png'))
-    
-if __name__ == "__main__":
-    main()

src/eynollah/training/gt_gen_utils.py

@@ -1,5 +1,3 @@
-import click
-import sys
 import os
 import numpy as np
 import warnings
@@ -8,8 +6,7 @@ from tqdm import tqdm
 import cv2
 from shapely import geometry
 from pathlib import Path
-import matplotlib.pyplot as plt
-from PIL import Image, ImageDraw, ImageFont
+from PIL import ImageFont
 
 
 KERNEL = np.ones((5, 5), np.uint8)

src/eynollah/training/inference.py

@@ -1,23 +1,29 @@
 import sys
 import os
-import numpy as np
 import warnings
+import json
+
+import numpy as np
 import cv2
-import seaborn as sns
 from tensorflow.keras.models import load_model
 import tensorflow as tf
 from tensorflow.keras import backend as K
-from tensorflow.keras import layers
-import tensorflow.keras.losses
 from tensorflow.keras.layers import *
-from models import *
-from gt_gen_utils import *
 import click
-import json
 from tensorflow.python.keras import backend as tensorflow_backend
 import xml.etree.ElementTree as ET
-import matplotlib.pyplot as plt
 
+from .gt_gen_utils import (
+    filter_contours_area_of_image,
+    find_new_features_of_contours,
+    read_xml,
+    resize_image,
+    update_list_and_return_first_with_length_bigger_than_one
+)
+from .models import (
+    PatchEncoder,
+    Patches
+)
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")
@@ -55,11 +61,9 @@ class sbb_predict:
             seg=seg[:,:,0]
             
         seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(np.uint8)
-        colors=sns.color_palette("hls", self.n_classes)
         
         for c in ann_u:
             c=int(c)
-            segl=(seg==c)
             seg_img[:,:,0][seg==c]=c
             seg_img[:,:,1][seg==c]=c
             seg_img[:,:,2][seg==c]=c
@@ -674,9 +678,3 @@ def main(image, dir_in, model, patches, save, save_layout, ground_truth, xml_fil
     x=sbb_predict(image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, out, min_area)
     x.run()
 
-if __name__=="__main__":
-    main()
-
-    
-    
-    

src/eynollah/training/train.py

@@ -1,20 +1,45 @@
 import os
 import sys
+import json
+
+import click
+
+from eynollah.training.metrics import (
+    soft_dice_loss,
+    weighted_categorical_crossentropy
+)
+from eynollah.training.models import (
+    PatchEncoder,
+    Patches,
+    machine_based_reading_order_model,
+    resnet50_classifier,
+    resnet50_unet,
+    vit_resnet50_unet,
+    vit_resnet50_unet_transformer_before_cnn
+)
+from eynollah.training.utils import (
+    data_gen,
+    generate_arrays_from_folder_reading_order,
+    generate_data_from_folder_evaluation,
+    generate_data_from_folder_training,
+    get_one_hot,
+    provide_patches,
+    return_number_of_total_training_data
+)
+
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
 import tensorflow as tf
 from tensorflow.compat.v1.keras.backend import set_session
-import warnings
-from tensorflow.keras.optimizers import *
+from tensorflow.keras.optimizers import SGD, Adam
 from sacred import Experiment
-from models import *
-from utils import *
-from metrics import *
 from tensorflow.keras.models import load_model
 from tqdm import tqdm
-import json
 from sklearn.metrics import f1_score
 from tensorflow.keras.callbacks import Callback
 
+import numpy as np
+import cv2
+
 class SaveWeightsAfterSteps(Callback):
     def __init__(self, save_interval, save_path, _config):
         super(SaveWeightsAfterSteps, self).__init__()
@@ -45,8 +70,8 @@ def configuration():
 
 
 def get_dirs_or_files(input_data):
-    if os.path.isdir(input_data):
     image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
+    if os.path.isdir(input_data):
         # Check if training dir exists
         assert os.path.isdir(image_input), "{} is not a directory".format(image_input)
         assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
@@ -121,7 +146,6 @@ def config_params():
     dir_rgb_backgrounds = None
     dir_rgb_foregrounds = None
 
-
 @ex.automain
 def run(_config, n_classes, n_epochs, input_height,
         input_width, weight_decay, weighted_loss,
@@ -423,7 +447,7 @@ def run(_config, n_classes, n_epochs, input_height,
 
         #f1score_tot = [0]
         indexer_start = 0
-        opt = SGD(learning_rate=0.01, momentum=0.9)
+        # opt = SGD(learning_rate=0.01, momentum=0.9)
         opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
         model.compile(loss="binary_crossentropy",
                             optimizer = opt_adam,metrics=['accuracy'])

src/eynollah/training/utils.py

@@ -1,13 +1,14 @@
 import os
+import math
+import random
+
 import cv2
 import numpy as np
 import seaborn as sns
 from scipy.ndimage.interpolation import map_coordinates
 from scipy.ndimage.filters import gaussian_filter
-import random
 from tqdm import tqdm
 import imutils
-import math
 from tensorflow.keras.utils import to_categorical
 from PIL import Image, ImageEnhance