def test_shuffle_shreds(self):
im_path = os.path.join(self.__class__.shredder_tests_root, 'fish_orig.JPEG')
im_grayscaled = cv2.cvtColor(cv2.imread(im_path), cv2.COLOR_RGB2GRAY)
t = 5
shuffled = Shredder.shred(im_grayscaled, t, shuffle_shreds=True)
reconstructed = Shredder.reconstruct(shuffled)
trimmed = im_grayscaled[0:reconstructed.shape[0], 0:reconstructed.shape[1]]
assert np.linalg.norm(trimmed - reconstructed) > 1000 # just some magic number
def shred_shuffle_and_reconstruct(x, t):
f = lambda v: Shredder.reconstruct(Shredder.shred(v, t, shuffle_shreds=True))
if type(x) == np.ndarray:
if x.ndim == 2:
return f(x)
assert x.ndim == 3, 'Invalid dimensions'
return list_of_images_to_numpy([f(im) for im in x[:]])
else: # Here, we got a list of images with different size
return [f(v) for v in x]
def test_reconstruction(self):
for im_path in [os.path.join(self.__class__.shredder_tests_root, 'fish_orig.JPEG'),
os.path.join(self.__class__.shredder_tests_root, 'doc_orig.jpg')]:
im = cv2.imread(im_path)
im_grayscaled = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
for t in (2, 4, 5):
shredded = Shredder.shred(im, t)
reconstructed = Shredder.reconstruct(shredded)
trimmed = im_grayscaled[0:reconstructed.shape[0], 0:reconstructed.shape[1]]
np.testing.assert_equal(reconstructed, trimmed)
def predict(images):
reconstructed_image = Shredder.reconstruct(images)
print('It is ', end='')
if fish_or_doc_classifier.is_fish([reconstructed_image])[0]:
print('image')
solver_with_comparator = image_type_to_solver_with_comparator[
ImageType.IMAGES]
else:
print('document')
solver_with_comparator = image_type_to_solver_with_comparator[
ImageType.DOCUMENTS]
labels = solver_with_comparator.predict(images)
return labels
def visualize_crops(crops_list, show=False, save_path=None):
t = round(math.sqrt(len(crops_list)))
grid_color = 255
img = Shredder.reconstruct(crops_list)
if t > 1:
dx = img.shape[0] // t
dy = img.shape[1] // t
img[:, ::dy] = grid_color
img[::dx, :] = grid_color
plt.imshow(img, cmap='gray')
plt.axis('off')
if show:
plt.show()
if save_path is not None:
plt.savefig(save_path)
plt.clf()