Bump requirements (Version 1.4.0)

* Bump requirements

* Bump OpenCV version

* Update jpeg samples

Author: Belval

requirements-hw.txt

@@ -1,4 +1,4 @@
 .
-tensorflow>=1.13.1
+tensorflow>=1.13.1,<1.14
 matplotlib>=3.0.2
-seaborn>=0.9.0
+seaborn>=0.9.0

setup.py

@@ -12,7 +12,7 @@
 
 setup(
     name="trdg",
-    version="1.3.2",
+    version="1.4.0",
     description="TextRecognitionDataGenerator: A synthetic data generator for text recognition",
     long_description=long_description,
     long_description_content_type="text/markdown",
@@ -26,7 +26,7 @@
         #   3 - Alpha
         #   4 - Beta
         #   5 - Production/Stable
-        "Development Status :: 3 - Alpha",
+        "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",
         "License :: OSI Approved :: MIT License",
         "Programming Language :: Python :: 2",
@@ -41,10 +41,10 @@
     packages=find_packages(exclude=["contrib", "docs", "tests"]),
     include_package_data=True,
     install_requires=[
-        "pillow==5.1.0",
-        "numpy>=1.15.1,<1.17",
+        "pillow==7.0.0",
+        "numpy>=1.16.4,<1.17",
         "requests>=2.20.0",
-        "opencv-python>=4.0.0.21",
+        "opencv-python>=4.2.0.32",
         "tqdm>=4.23.0",
         "beautifulsoup4>=4.6.0"
     ],

tests/expected_results/TEST TEST TEST_13.jpg

@@ -63,7 +63,11 @@ def picture(height, width):
     pictures = os.listdir(os.path.join(script_path, "pictures"))
 
     if len(pictures) > 0:
-        pic = Image.open(os.path.join(script_path, "pictures", pictures[rnd.randint(0, len(pictures) - 1)]))
+        pic = Image.open(
+            os.path.join(
+                script_path, "pictures", pictures[rnd.randint(0, len(pictures) - 1)]
+            )
+        )
 
         if pic.size[0] < width:
             pic = pic.resize(

tests/expected_results/TEST TEST TEST_4.jpg

@@ -162,7 +162,9 @@ def generate(
             background_img = background_generator.picture(
                 background_height, background_width
             )
-        background_mask = Image.new("RGB", (background_width, background_height), (0, 0, 0))
+        background_mask = Image.new(
+            "RGB", (background_width, background_height), (0, 0, 0)
+        )
 
         #############################
         # Place text with alignment #

tests/expected_results/TEST TEST TEST_7.jpg

@@ -93,7 +93,7 @@ def _apply_func_distorsion(image, mask, vertical, horizontal, max_offset, func):
         ).convert("RGBA"),
         Image.fromarray(
             np.uint8(new_mask_arr_copy if horizontal and vertical else new_mask_arr)
-        ).convert("RGB")
+        ).convert("RGB"),
     )
 
 
@@ -139,5 +139,10 @@ def random(image, mask, vertical=False, horizontal=False):
     max_offset = int(image.height ** 0.4)
 
     return _apply_func_distorsion(
-        image, mask, vertical, horizontal, max_offset, (lambda x: rnd.randint(0, max_offset))
+        image,
+        mask,
+        vertical,
+        horizontal,
+        max_offset,
+        (lambda x: rnd.randint(0, max_offset)),
     )

trdg/background_generator.py

@@ -94,4 +94,4 @@ def next(self):
                 self.output_mask,
             ),
             self.strings[(self.generated_count - 1) % len(self.strings)],
-        )
+        )

trdg/data_generator.py

@@ -9,22 +9,35 @@
 import seaborn
 from PIL import Image, ImageColor
 from collections import namedtuple
+import warnings
+
+warnings.filterwarnings("ignore")
+
 
 def download_model_weights():
     from pathlib import Path
-    import urllib.request 
+    import urllib.request
+
     cwd = os.path.dirname(os.path.abspath(__file__))
-    for k in ['model-29.data-00000-of-00001','model-29.index','model-29.meta','translation.pkl']:
-        download_dir = Path(cwd)/'handwritten_model/'
-        download_dir.mkdir(exist_ok=True,parents=True)
-        if (download_dir/f'{k}').exists(): continue
-        print(f'file {k} not found, downloading from git repo..')
+    for k in [
+        "model-29.data-00000-of-00001",
+        "model-29.index",
+        "model-29.meta",
+        "translation.pkl",
+    ]:
+        download_dir = Path(cwd) / "handwritten_model/"
+        download_dir.mkdir(exist_ok=True, parents=True)
+        if (download_dir / f"{k}").exists():
+            continue
+        print(f"file {k} not found, downloading from git repo..")
         urllib.request.urlretrieve(
-            f'https://raw.github.com/Belval/TextRecognitionDataGenerator/master/trdg/handwritten_model/{k}', 
-            download_dir/f'{k}')
-        print(f'file {k} saved to disk')
+            f"https://raw.github.com/Belval/TextRecognitionDataGenerator/master/trdg/handwritten_model/{k}",
+            download_dir / f"{k}",
+        )
+        print(f"file {k} saved to disk")
     return cwd
 
+
 def _sample(e, mu1, mu2, std1, std2, rho):
     cov = np.array([[std1 * std1, std1 * std2 * rho], [std1 * std2 * rho, std2 * std2]])
     mean = np.array([mu1, mu2])
@@ -71,7 +84,9 @@ def _sample_text(sess, args_text, translation):
         "finish",
         "zero_states",
     ]
-    vs = namedtuple("Params", fields)(*[tf.compat.v1.get_collection(name)[0] for name in fields])
+    vs = namedtuple("Params", fields)(
+        *[tf.compat.v1.get_collection(name)[0] for name in fields]
+    )
 
     text = np.array([translation.get(c, 0) for c in args_text])
     sequence = np.eye(len(translation), dtype=np.float32)[text]
@@ -163,14 +178,20 @@ def _join_images(images):
 
 def generate(text, text_color):
     cd = download_model_weights()
-    with open(os.path.join(cd, os.path.join("handwritten_model", "translation.pkl")), "rb") as file:
+    with open(
+        os.path.join(cd, os.path.join("handwritten_model", "translation.pkl")), "rb"
+    ) as file:
         translation = pickle.load(file)
 
     config = tf.compat.v1.ConfigProto(device_count={"GPU": 0})
     tf.compat.v1.reset_default_graph()
     with tf.compat.v1.Session(config=config) as sess:
-        saver = tf.compat.v1.train.import_meta_graph(os.path.join(cd,"handwritten_model/model-29.meta"))
-        saver.restore(sess,os.path.join(cd,os.path.join("handwritten_model/model-29")))
+        saver = tf.compat.v1.train.import_meta_graph(
+            os.path.join(cd, "handwritten_model/model-29.meta")
+        )
+        saver.restore(
+            sess, os.path.join(cd, os.path.join("handwritten_model/model-29"))
+        )
         images = []
         colors = [ImageColor.getrgb(c) for c in text_color.split(",")]
         c1, c2 = colors[0], colors[-1]
@@ -203,13 +224,11 @@ def generate(text, text_color):
 
             canvas = plt.get_current_fig_manager().canvas
             canvas.draw()
-            
+
             s, (width, height) = canvas.print_to_buffer()
-            image = Image.frombytes(
-                "RGBA", (width, height), s
-            )
+            image = Image.frombytes("RGBA", (width, height), s)
             mask = Image.new("RGB", (width, height), (0, 0, 0))
-            
+
             images.append(_crop_white_borders(image))
 
             plt.close()