organize imports, use relative imports

src/eynollah/training/generate_gt_for_training.py

@@ -1,10 +1,11 @@
 import click
 import json
-from gt_gen_utils import *
 from tqdm import tqdm
 from pathlib import Path
 from PIL import Image, ImageDraw, ImageFont
 
+from .gt_gen_utils import *
+
 @click.group()
 def main():
     pass

src/eynollah/training/gt_gen_utils.py

@@ -6,7 +6,6 @@ from tqdm import tqdm
 import cv2
 from shapely import geometry
 from pathlib import Path
-import matplotlib.pyplot as plt
 from PIL import ImageFont
 
 

src/eynollah/training/inference.py

@@ -1,19 +1,21 @@
 import sys
 import os
-import numpy as np
 import warnings
+import json
+
+import numpy as np
 import cv2
 from tensorflow.keras.models import load_model
 import tensorflow as tf
 from tensorflow.keras import backend as K
 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
 
+from .models import *
+from .gt_gen_utils import *
+
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")

src/eynollah/training/train.py

@@ -1,20 +1,22 @@
 import os
 import sys
+import json
+
 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 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
 
+from .models import *
+from .utils import *
+from .metrics import *
+
 class SaveWeightsAfterSteps(Callback):
     def __init__(self, save_interval, save_path, _config):
         super(SaveWeightsAfterSteps, self).__init__()

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