make relative wildcard imports explicit

pyproject.toml

@@ -33,7 +33,6 @@ classifiers = [
 eynollah = "eynollah.cli:main"
 ocrd-eynollah-segment = "eynollah.ocrd_cli:main"
 ocrd-sbb-binarize = "eynollah.ocrd_cli_binarization:main"
-eynollah-training = "eynollah.training.cli:main"
 
 [project.urls]
 Homepage = "https://github.com/qurator-spk/eynollah"

src/eynollah/training/build_model_load_pretrained_weights_and_save.py

@@ -1,12 +1,7 @@
-import os
-import sys
 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():

src/eynollah/training/generate_gt_for_training.py

@@ -1,10 +1,28 @@
 import click
 import json
+import os
 from tqdm import tqdm
 from pathlib import Path
 from PIL import Image, ImageDraw, ImageFont
+import cv2
+import numpy as np
 
-from .gt_gen_utils import *
+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():

src/eynollah/training/inference.py

@@ -13,9 +13,14 @@ import click
 from tensorflow.python.keras import backend as tensorflow_backend
 import xml.etree.ElementTree as ET
 
-from .models import *
-from .gt_gen_utils import *
-
+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")

src/eynollah/training/train.py

@@ -2,20 +2,39 @@ import os
 import sys
 import json
 
+from eynollah.training.metrics import soft_dice_loss, weighted_categorical_crossentropy
+
+from .models import (
+    PatchEncoder,
+    Patches,
+    machine_based_reading_order_model,
+    resnet50_classifier,
+    resnet50_unet,
+    vit_resnet50_unet,
+    vit_resnet50_unet_transformer_before_cnn
+)
+from .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 tensorflow.keras.models import load_model
 from tqdm import tqdm
 from sklearn.metrics import f1_score
 from tensorflow.keras.callbacks import Callback
 
-from .models import *
-from .utils import *
-from .metrics import *
+import numpy as np
+import cv2
 
 class SaveWeightsAfterSteps(Callback):
     def __init__(self, save_interval, save_path, _config):
@@ -47,8 +66,8 @@ 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)
@@ -425,7 +444,7 @@ 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'])