def crop_images(img_list, seg_list, imag_path, seg_path):
"""
Function that crops the image into N = 2000 / SLICE_LEN tiles
:param img_list: list of the images to crop
:param seg_list: list of the segmentations to crop
:param imag_path: path where to save the cropped images
:param seg_path: path where to save the cropped segmentations
:return:
"""
for idx, (img_path, mask_path) in enumerate(zip(img_list, seg_list)):
image = cv2.imread(str(img_path))
image = F.pad(image, min_height=IMG_DIM, min_width=IMG_DIM)
mask = cv2.imread(str(mask_path))
mask = F.pad(mask, min_height=IMG_DIM, min_width=IMG_DIM)
tile = 0
for i in range(0, 2000, SLICE_LEN):
for j in range(0, 2000, SLICE_LEN):
tile += 1
cv2.imwrite(os.path.join(imag_path, f"{idx:04}_{tile:02}.png"),
image[i:i + INPUT_DIM, j:j + INPUT_DIM])
cv2.imwrite(os.path.join(seg_path, f"{idx:04}_{tile:02}.png"),
mask[i:i + INPUT_DIM, j:j + INPUT_DIM])
def test_pad_float(target):
img = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
expected = np.array([[0.4, 0.3, 0.4, 0.3], [0.2, 0.1, 0.2, 0.1],
[0.4, 0.3, 0.4, 0.3], [0.2, 0.1, 0.2, 0.1]],
dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
padded_img = F.pad(img, min_height=4, min_width=4)
assert_array_almost_equal_nulp(padded_img, expected)
def test_pad(target):
img = np.array([[1, 2], [3, 4]], dtype=np.uint8)
expected = np.array(
[[4, 3, 4, 3], [2, 1, 2, 1], [4, 3, 4, 3], [2, 1, 2, 1]],
dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
padded = F.pad(img, min_height=4, min_width=4)
assert np.array_equal(padded, expected)
def pad_pil_image(img, **params):
height, width, _ = img.shape
tail = width % image_len_divisible_by
if tail == 0:
return img
# args for pad: left, top, right, bottom
# return functional.pad(img, (0, 0, image_len_divisible_by - tail, 0), padding_mode="edge")
return AF.pad(img,
min_height=height,
min_width=width + image_len_divisible_by - tail)
def test_pad(target):
img = np.array([[1, 2], [3, 4]], dtype=np.uint8)
expected = np.array(
[[4, 3, 4, 3], [2, 1, 2, 1], [4, 3, 4, 3], [2, 1, 2, 1]],
dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
expected = convert_2d_to_3d(expected)
padded = F.pad(img, min_height=4, min_width=4)
assert np.array_equal(padded, expected)
def test_pad(target):
img = np.array(
[[1, 2],
[3, 4]], dtype=np.uint8)
expected = np.array(
[[4, 3, 4, 3],
[2, 1, 2, 1],
[4, 3, 4, 3],
[2, 1, 2, 1]], dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
expected = convert_2d_to_3d(expected)
padded = F.pad(img, min_height=4, min_width=4)
assert np.array_equal(padded, expected)
def albumentations(self, img):
return albumentations.pad(img, min_height=512, min_width=512)
def albumentations(self, img):
return albumentations.pad(img, min_height=512, min_width=512)