Testing Class Material

extra_slides/packaging/noiser_project_final/noiser/main.py

@@ -0,0 +1,22 @@
+import os.path
+
+import matplotlib.pyplot as plt
+from scipy.ndimage import imread
+from pkg_resources import resource_filename
+
+from noiser.noise import white_noise
+from noiser.utils import copy_image
+
+
+def main():
+    path = resource_filename('noiser', os.path.join('images', 'baboon_kandinsky.png'))
+    print(path)
+    img = imread(path)
+    noisy = copy_image(white_noise(img, 20))
+    plt.imshow(noisy)
+    plt.draw()
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()

extra_slides/packaging/noiser_project_final/noiser/noise.py

@@ -0,0 +1,7 @@
+import numpy as np
+
+
+def white_noise(image, std):
+    noise = np.random.normal(scale=std, size=image.shape).astype(image.dtype)
+    noisy = image + noise
+    return noisy

extra_slides/packaging/noiser_project_final/noiser/setup.py

@@ -0,0 +1,8 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='Noiser',
+    version='1.0',
+    packages=find_packages(),
+)
+

extra_slides/packaging/noiser_project_final/noiser/tests/test_noise.py

@@ -0,0 +1,24 @@
+import numpy as np
+from numpy.testing import assert_allclose
+
+from noiser.noise import white_noise
+
+
+def test_white_noise():
+    n_images, height, width = 201, 101, 102
+    dtype = np.float32
+
+    # Create ``n_images`` identical image.
+    base_image = np.random.rand(1, height, width, 3).astype(dtype) - 0.5
+    images = np.repeat(base_image, n_images, axis=0)
+
+    std = 0.13
+    noisy = white_noise(images, std=std)
+
+    # dtype and shape are preserved.
+    assert noisy.dtype == dtype
+    assert noisy.shape == images.shape
+
+    # Mean and std of noisy image match expectations.
+    assert_allclose(images.mean(0), base_image[0], atol=1e-4)
+    assert np.isclose((noisy - images).std(), std, atol=1e-4)

extra_slides/packaging/noiser_project_final/noiser/tests/test_utils.py

@@ -0,0 +1,14 @@
+import numpy as np
+from numpy.testing import assert_array_equal
+
+from noiser.utils import copy_image
+
+
+def test_copy_image():
+    height, width = 101, 102
+    dtype = np.float32
+
+    image = np.random.rand(height, width, 3).astype(dtype)
+    copy = copy_image(image)
+    assert_array_equal(copy, image)
+

extra_slides/packaging/noiser_project_final/noiser/utils.pyx

@@ -0,0 +1,15 @@
+import numpy as np
+cimport numpy as np
+
+
+def copy_image(np.ndarray img):
+    cdef int h = img.shape[0]
+    cdef int w = img.shape[1]
+    cdef int c = img.shape[2]
+    cdef np.ndarray copy = np.empty([h, w, c], dtype=img.dtype)
+
+    for i in range(h):
+        for j in range(w):
+            for k in range(c):
+                copy[i, j, k] = img[i, j, k]
+    return copy