Merge pull request #18 from prakass1/master

improving glass blur using numba

.gitignore

@@ -1,6 +1,9 @@
 build/*
 *egg-info/
+*.egg-info/*
 dist/*
 *__pycache__*
 *.ipynb_checkpoints*
 *.pyc
+.vscode
+.venv

imagecorruptions/corruptions.py

@@ -15,6 +15,7 @@
 import warnings
 import os
 from pkg_resources import resource_filename
+from numba import njit, prange
 
 
 
@@ -149,6 +150,20 @@ def _motion_blur(x, radius, sigma, angle):
         blurred = blurred + kernel[i] * shifted
     return blurred
 
+# Numba nopython compilation to shuffle_pixles
+@njit()
+def _shuffle_pixels_njit_glass_blur(d0,d1,x,c):
+
+    # locally shuffle pixels
+    for i in range(c[2]):
+        for h in range(d0 - c[1], c[1], -1):
+            for w in range(d1 - c[1], c[1], -1):
+                dx, dy = np.random.randint(-c[1], c[1], size=(2,))
+                h_prime, w_prime = h + dy, w + dx
+                # swap
+                x[h, w], x[h_prime, w_prime] = x[h_prime, w_prime], x[h, w]
+    return x
+
 # /////////////// End Corruption Helpers ///////////////
 
 
@@ -196,21 +211,12 @@ def glass_blur(x, severity=1):
 
     x = np.uint8(
         gaussian(np.array(x) / 255., sigma=c[0], multichannel=True) * 255)
-    x_shape = np.array(x).shape
 
-    # locally shuffle pixels
-    for i in range(c[2]):
-        for h in range(x_shape[0] - c[1], c[1], -1):
-            for w in range(x_shape[1] - c[1], c[1], -1):
-                dx, dy = np.random.randint(-c[1], c[1], size=(2,))
-                h_prime, w_prime = h + dy, w + dx
-                # swap
-                x[h, w], x[h_prime, w_prime] = x[h_prime, w_prime], x[h, w]
+    x = _shuffle_pixels_njit_glass_blur(np.array(x).shape[0],np.array(x).shape[1],x,c)
 
     return np.clip(gaussian(x / 255., sigma=c[0], multichannel=True), 0,
                    1) * 255
 
-
 def defocus_blur(x, severity=1):
     c = [(3, 0.1), (4, 0.5), (6, 0.5), (8, 0.5), (10, 0.5)][severity - 1]
 

requirements.txt

@@ -0,0 +1,6 @@
+numpy >= 1.16
+Pillow >= 5.4.1
+scikit-image >= 0.15
+opencv-python >= 3.4.5
+scipy >= 1.2.1
+numba >= 0.53.0

setup.py

@@ -3,6 +3,10 @@
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
+# Can be used to load install_require
+# with open('requirements.txt', 'r') as freq:
+#     required = freq.read().splitlines()
+
 
 setuptools.setup(
     name="imagecorruptions",
@@ -20,6 +24,7 @@
           'scikit-image >= 0.15',
           'opencv-python >= 3.4.5',
           'scipy >= 1.2.1',
+          'numba >= 0.53.0'
       ],
       include_package_data=True,
     classifiers=[

test_demo.py

@@ -0,0 +1,17 @@
+from imagecorruptions import corrupt, get_corruption_names
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+image = np.asarray(Image.open('test_image.jpg'))
+#image = np.ones((427, 640, 3), dtype=np.uint8)
+
+# corrupted_image = corrupt(img, corruption_name='gaussian_blur', severity=1)
+
+for corruption in get_corruption_names('blur'):
+    tic = time.time()
+    for severity in range(5):
+        corrupted = corrupt(image, corruption_name=corruption, severity=severity+1)
+        plt.imshow(corrupted)
+        plt.show()
+    print(corruption, time.time() - tic)