def test_generator(test_path, target_size, as_gray=True):
""" Image Data Generator
Function that generates batches od data for testing from specified folder
Reads images as grey, makes them square, scales them
Returns images with specified pixel size
Does preprocessing (normalization to 0-1)
"""
for test_img in os.listdir(test_path):
img = io.imread(os.path.join(test_path, test_img), as_gray=as_gray)
img = img / 255.
img = reshape_image(img, target_size)
yield img
test_image_names.append(_)
test_predictions = []
KERNEL = np.ones((15, 15), np.uint8)
#total prediction
for index, test_image_name in enumerate(test_image_names):
sys.stdout.write("Predicting: {}/{} ...\r".format(index + 1,
len(test_image_names)))
sys.stdout.flush()
img = io.imread(os.path.join(path_to_test_images, test_image_name),
as_gray=True)
img = img / 255.
img = reshape_image(img, unet_size)
result = unet.predict_on_batch(img)
result = result[0]
new_img = normalize_mask(result)
new_img = (new_img * 255).astype('uint8')
new_img = cv2.erode(new_img, KERNEL, iterations=2)
new_img = cv2.dilate(new_img, KERNEL, iterations=3)
new_img = np.expand_dims(new_img, axis=2)
new_img = cv2.bitwise_not(new_img)
img = img[0]
img = (img * 255).astype('uint8')