def pipeline(filename, temp_dir):
"""
Convenient wrapper around the feature extraction pipeline
"""
x = plt.imread(filename)[:,:,0]
cropped = process_image(x)
to_save = filename.split('.')[0] + '.png'
plt.imsave(os.path.join(temp_dir, to_save), cropped,
cmap=plt.cm.gray)
def test_defective_images(self):
"""
Assert that the images in the defective list are indeed defective.
"""
with open(self.defects, 'r') as f:
defects_list = json.load(f)
defects = [f[0] for f in defects_list]
isError = []
for defect in defects:
path = os.path.join(ROOT, 'images_training',defect)
try:
x = plt.imread(path)
cropped = process_image(x)
save_path = os.path.join(self.processed_path, defect)
plt.imsave(save_path, cropped, cmap=plt.cm.gray)
except:
isError.append(True)
self.assertTrue(all(isError))