def inpaint_double_train(img_files):
img_file_1, img_file_2, fold = img_files
img_1 = cv2.imread(join(TRAIN_DIR, 'images', img_file_1),
cv2.IMREAD_GRAYSCALE)
trg_1 = cv2.imread(join(TRAIN_DIR, 'masks', img_file_1),
cv2.IMREAD_GRAYSCALE)
img_2 = cv2.imread(join(TRAIN_DIR, 'images', img_file_2),
cv2.IMREAD_GRAYSCALE)
trg_2 = cv2.imread(join(TRAIN_DIR, 'masks', img_file_2),
cv2.IMREAD_GRAYSCALE)
if not np.sum(img_1) or not np.sum(img_2):
return
img_1 = np.pad(img_1, PAD_WIDTH, mode='constant')
trg_1 = np.pad(trg_1, PAD_WIDTH, mode='constant')
img_2 = np.pad(img_2, PAD_WIDTH, mode='constant')
trg_2 = np.pad(trg_2, PAD_WIDTH, mode='constant')
img = np.concatenate([img_1, img_2], axis=1)
trg = np.concatenate([trg_1, trg_2], axis=1)
img = (inpaint_biharmonic(img, MASK_INPAINT) * 255).astype(np.uint8)
trg = inpaint_biharmonic(trg, MASK_INPAINT)
trg = np.where(trg > TARGET_THRESHOLD, 255, 0)
img_file = img_file_1[:-4] + '_' + img_file_2[:-4] + '.png'
make_dir(join(TRAIN_DIR, 'images' + SAVE_NAME, 'fold_%d' % fold))
make_dir(join(TRAIN_DIR, 'masks' + SAVE_NAME, 'fold_%d' % fold))
cv2.imwrite(
join(TRAIN_DIR, 'images' + SAVE_NAME, 'fold_%d' % fold, img_file), img)
cv2.imwrite(
join(TRAIN_DIR, 'masks' + SAVE_NAME, 'fold_%d' % fold, img_file), trg)
return img, trg, img_file
def inpaint_train(img_file):
img = cv2.imread(join(TRAIN_DIR, 'images', img_file), cv2.IMREAD_GRAYSCALE)
trg = cv2.imread(join(TRAIN_DIR, 'masks', img_file), cv2.IMREAD_GRAYSCALE)
img = np.pad(img, PAD_WIDTH, mode='constant')
trg = np.pad(trg, PAD_WIDTH, mode='constant')
img = (inpaint_biharmonic(img, MASK_INPAINT) * 255).astype(np.uint8)
trg = inpaint_biharmonic(trg, MASK_INPAINT)
trg = np.where(trg > TARGET_THRESHOLD, 255, 0)
cv2.imwrite(join(TRAIN_DIR, 'images' + SAVE_NAME, img_file), img)
cv2.imwrite(join(TRAIN_DIR, 'masks' + SAVE_NAME, img_file), trg)
def remove_text(img):
th = threshold_otsu(img[:, :, 2])
mask = img[:, :, 2] > th
newimg = img.copy()
newimg[:, :, 2] = 0
newimg = inpaint_biharmonic(newimg, mask, multichannel=True)
return newimg
def inpaint(self):
print ' Beginning inpainting. '
mask = np.zeros(len(self.By))
for i in range(len(self.By)):
if math.isnan(self.Bx[i]) or math.isnan(self.By[i]) or math.isnan(
self.Bz[i]):
mask[i] = 1
self.Bx = skres.inpaint_biharmonic(self.Bx, mask, multichannel=False)
print ' Bx done'
self.By = skres.inpaint_biharmonic(self.By, mask, multichannel=False)
print ' By done'
self.Bz = skres.inpaint_biharmonic(self.Bz, mask, multichannel=False)
print ' Bz done'
if var.plot_comparison == True:
self.plot_comp['inpainted'] = copy.deepcopy(self.By)
print ' Inpainting done!'
return None
def inpaintBiharmonic():
img = np.tile(np.square(np.linspace(0, 1, 5)), (5, 1))
imgO = img.copy()
mask = np.zeros_like(img)
mask[2, 2:] = 1
mask[1, 3:] = 1
mask[0, 4:] = 1
imgN = mask.copy()
out = inpaint_biharmonic(img, mask)
imgR = out.copy()
return [imgO, imgN, imgR]
def inpaint_test(id):
neighbors = SALT_DATA.get_neighbors(id)
stacked_image, image_unknown_mask = SALT_DATA.get_stacked_images(neighbors, return_unknown_mask=True)
center_stacked_image = CENTER_CROP(stacked_image)
center_image_unknown_mask = CENTER_CROP(image_unknown_mask)
if np.sum(center_image_unknown_mask):
inpaint_image = inpaint_biharmonic(center_stacked_image, center_image_unknown_mask)
inpaint_image = (inpaint_image * 255).astype(np.uint8)
else:
inpaint_image = center_stacked_image
cv2.imwrite(join(TEST_DIR, 'images' + SAVE_NAME, f"{id}.png"), inpaint_image)
def inpaint_train(id):
neighbors = SALT_DATA.get_neighbors(id)
stacked_image, image_unknown_mask = SALT_DATA.get_stacked_images(neighbors, return_unknown_mask=True)
center_stacked_image = CENTER_CROP(stacked_image)
center_image_unknown_mask = CENTER_CROP(image_unknown_mask)
if np.sum(center_image_unknown_mask):
inpaint_image = inpaint_biharmonic(center_stacked_image, center_image_unknown_mask)
inpaint_image = (inpaint_image * 255).astype(np.uint8)
else:
inpaint_image = center_stacked_image
stacked_mask, mask_unknown_mask = SALT_DATA.get_stacked_masks(neighbors, return_unknown_mask=True)
center_stacked_mask = CENTER_CROP(stacked_mask)
center_mask_unknown_mask = CENTER_CROP(mask_unknown_mask)
if np.sum(center_mask_unknown_mask):
inpaint_mask = inpaint_biharmonic(center_stacked_mask, center_mask_unknown_mask)
inpaint_mask= np.where(inpaint_mask > TARGET_THRESHOLD, 255, 0)
else:
inpaint_mask = center_stacked_mask
cv2.imwrite(join(TRAIN_DIR, 'images' + SAVE_NAME, f"{id}.png"), inpaint_image)
cv2.imwrite(join(TRAIN_DIR, 'masks' + SAVE_NAME, f"{id}.png"), inpaint_mask)
def inpaint_test(img_file):
img = cv2.imread(join(TEST_DIR, 'images', img_file), cv2.IMREAD_GRAYSCALE)
img = np.pad(img, PAD_WIDTH, mode='constant')
img = (inpaint_biharmonic(img, MASK_INPAINT) * 255).astype(np.uint8)
cv2.imwrite(join(TEST_DIR, 'images' + SAVE_NAME, img_file), img)
def time_inpaint_grey(self):
restoration.inpaint_biharmonic(self.image_defect_gray, self.mask,
**_channel_kwarg(False))
def time_inpaint_rgb(self):
restoration.inpaint_biharmonic(self.image_defect, self.mask,
**_channel_kwarg(True))
def time_inpaint_grey(self):
restoration.inpaint_biharmonic(self.image_defect_gray,
self.mask,
multichannel=False)
def time_inpaint_rgb(self):
restoration.inpaint_biharmonic(self.image_defect,
self.mask,
multichannel=True)