utils_ocr: avoid module-level import of TF

src/eynollah/utils/utils_ocr.py

@@ -4,7 +4,9 @@ from itertools import islice
 
 import numpy as np
 import cv2
-import tensorflow as tf
+# avoid module-level import:
+# import tensorflow as tf
+# (wait for tf-keras and logging setup in ModelZoo.load_model)
 from scipy.signal import find_peaks
 from scipy.ndimage import gaussian_filter1d
 from PIL import Image, ImageDraw, ImageFont
@@ -13,6 +15,8 @@ from .resize import resize_image
 
 
 def decode_batch_predictions(pred, num_to_char, max_len = 128):
+    import tensorflow as tf
+
     # input_len is the product of the batch size and the
     # number of time steps.
     input_len = np.ones(pred.shape[0]) * pred.shape[1]
@@ -40,6 +44,8 @@ def decode_batch_predictions(pred, num_to_char, max_len = 128):
     
     
 def distortion_free_resize(image, img_size):
+    import tensorflow as tf
+
     w, h = img_size
     image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)
 

tests/cli_tests/test_ocr.py

@@ -30,7 +30,7 @@ def test_run_eynollah_ocr_filename(
             '-o', str(outfile.parent),
         ] + options,
         [
-            # FIXME: ocr has no logging!
+            'output filename:'
         ]
     )
     assert outfile.exists()
@@ -57,7 +57,7 @@ def test_run_eynollah_ocr_directory(
             '-o', str(outdir),
         ],
         [
-            # FIXME: ocr has no logging!
+            'output filename:'
         ]
     )
     assert len(list(outdir.iterdir())) == 2