def main():
args = parse_args()
image = imread(args.input_image) # 读取图像，存储顺序是RGB
mask = imread(args.mask, as_grey=True) # 读取掩膜，存储顺序是RGB
output_image = Inpainter(image,
mask,
patch_size=args.patch_size,
plot_progress=args.plot_progress).inpaint()
imsave(args.output, output_image, quality=100)
def inpaint_image(image, mode, p1, p2):
print mode
print image.shape
if mode == 'ss':
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
size = 15
print 'blobbing'
blob_array = segment_image(image)
pixel_val_1 = np.array([p1[1], p1[0]])
pixel_val_2 = np.array([p2[1], p2[0]])
obs = []
min_dist = [10000000000000] * 5
target = [None] * 5
print 'selecting'
for blob in blob_array:
x1, y1, x2, y2 = blob.bbox
dist = abs(x1 - pixel_val_1[0]) + abs(y1 - pixel_val_1[1]) + abs(
x2 - pixel_val_2[0]) + abs(y2 - pixel_val_2[1])
for i, m in enumerate(min_dist):
if m > dist and blob_check(blob, pixel_val_1, pixel_val_2):
min_dist[i] = dist
target[i] = blob
image_copy, mask = blob_plot(image, target[-1])
#dst = cv2.inpaint(image_copy,mask,3,cv2.INPAINT_TELEA)
inp = Inpainter(image, mask * 255, 4)
inp.inpaint()
dst = inp.result
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
return dst
elif mode == 'gc':
img = image
mask = np.zeros(img.shape[:2], np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (p1[0], p1[1], p2[0], p2[1])
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5,
cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img2 = img * mask2[:, :, np.newaxis]
img = img - img2
# dst = inpaint.inpaint_biharmonic(img,mask2,multichannel =True)
#dst = cv2.inpaint(img,mask2,3,cv2.INPAINT_TELEA)
inp = Inpainter(image, mask * 255, 4)
inp.inpaint()
dst = inp.result
return dst
def main():
args = parse_args()
image = imread(args.input_image)
mask = imread(args.mask, as_gray=True)
output_image = Inpainter(
image,
mask,
patch_size=args.patch_size,
plot_progress=args.plot_progress,
include_target_in_dict=args.include_target_in_dict).inpaint()
imsave(args.output, output_image, quality=100)
def main():
args = parse_args()
image = imread(args.input_image)
#image = cv2.imread(args.input_image)
#image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask = imread(args.mask, as_grey=True)
print('start inpaint', image.shape, mask.shape)
output_image = Inpainter(image,
mask,
patch_size=args.patch_size,
plot_progress=args.plot_progress).inpaint()
print('finish!', 'save to', args.output)
imsave(args.output, output_image, quality=100)
def Image_Inpainting(Org_Image, Target_Image, filename, org_dims):
image = imread(Org_Image)
mask = imread(Target_Image, as_grey=True)
output_image = Inpainter(
image,
mask,
filename,
patch_size=9,
plot_progress=True,
).inpaint()
imsave(os.path.join(app.config['DOWNLOAD_FOLDER'], filename),
output_image,
quality=100)
path = os.path.dirname(
os.path.abspath(__file__)) + '/Gif/' + filename.split('.')[0] + '.gif'
folder_Name = 'downloaded_files/'
my_bucket.upload_file(
os.path.join(app.config['DOWNLOAD_FOLDER'], filename),
folder_Name + filename)
folder_Name = 'GIF/'
print(path)
my_bucket.upload_file(path, folder_Name + filename.split('.')[0] + '.gif')
crop_inpaintMask = inpaintMask[yl:yr, xr:xl]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.axis('off')
plt.imshow(crop_inpaintMask, vmin=vmin, vmax=vmax)
plt.close('all')
time.sleep(1)
crop_gray_originalImage = np.uint16(np.round(((2**16 - 1)*(crop_originalImage-np.nanmin(originalImage)))/(np.nanmax(originalImage)-np.nanmin(originalImage))))
else:
crop_gray_originalImage = None
crop_inpaintMask = None
yl, xr = 0, 0
gray_originalImage = np.uint16(np.round(((2**16 - 1)*(originalImage-np.nanmin(originalImage)))/(np.nanmax(originalImage)-np.nanmin(originalImage))))
model = Inpainter(gray_originalImage, inpaintMask, crop_gray_originalImage, crop_inpaintMask, halfPatchWidth, select)
model.inpaint(file_name, directory_2 , yl, xr)
result = model.result
float_result = (result*(np.nanmax(originalImage)-np.nanmin(originalImage)))/(2**16 - 1)+np.nanmin(originalImage)
for x in range(originalImage.shape[0]):
for y in range(originalImage.shape[1]):
if math.isnan(originalImage[x, y]):
if inpaintMask[x, y] == 0:
float_result[x, y] = 'nan'
[xcoord, ycoord] = np.load(coord_path)
cx = int(np.nanmean(xcoord))
cy = int(np.nanmean(ycoord))
f = plt.figure()
from skimage.io import imread, imsave
from inpainter import Inpainter
image = imread('resources/image6.jpg')
mask = imread('resources/mask6.jpg', as_grey=True)
output_image = Inpainter(image, mask, patch_size=16,
plot_progress=True).inpaint()
imsave('output1.jpg', output_image, quality=100)