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#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : patch_match.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 01/09/2020
#
# Distributed under terms of the MIT license.
import ctypes
import os.path as osp
from typing import Optional, Union
import numpy as np
from PIL import Image
__all__ = ['set_random_seed', 'set_verbose', 'inpaint', 'inpaint_regularity']
class CShapeT(ctypes.Structure):
_fields_ = [
('width', ctypes.c_int),
('height', ctypes.c_int),
('channels', ctypes.c_int),
]
class CMatT(ctypes.Structure):
_fields_ = [
('data_ptr', ctypes.c_void_p),
('shape', CShapeT),
('dtype', ctypes.c_int)
]
PMLIB = ctypes.CDLL(osp.join(osp.dirname(__file__), 'libpatchmatch.so'))
PMLIB.PM_set_random_seed.argtypes = [ctypes.c_uint]
PMLIB.PM_set_verbose.argtypes = [ctypes.c_int]
PMLIB.PM_free_pymat.argtypes = [CMatT]
PMLIB.PM_inpaint.argtypes = [CMatT, CMatT, ctypes.c_int]
PMLIB.PM_inpaint.restype = CMatT
PMLIB.PM_inpaint_regularity.argtypes = [CMatT, CMatT, CMatT, ctypes.c_int, ctypes.c_float]
PMLIB.PM_inpaint_regularity.restype = CMatT
PMLIB.PM_inpaint2.argtypes = [CMatT, CMatT, CMatT, ctypes.c_int]
PMLIB.PM_inpaint2.restype = CMatT
PMLIB.PM_inpaint2_regularity.argtypes = [CMatT, CMatT, CMatT, CMatT, ctypes.c_int, ctypes.c_float]
PMLIB.PM_inpaint2_regularity.restype = CMatT
def set_random_seed(seed: int):
PMLIB.PM_set_random_seed(ctypes.c_uint(seed))
def set_verbose(verbose: bool):
PMLIB.PM_set_verbose(ctypes.c_int(verbose))
def inpaint(
image: Union[np.ndarray, Image.Image],
mask: Optional[Union[np.ndarray, Image.Image]] = None,
*,
global_mask: Optional[Union[np.ndarray, Image.Image]] = None,
patch_size: int = 15
) -> np.ndarray:
"""
PatchMatch based inpainting proposed in:
PatchMatch : A Randomized Correspondence Algorithm for Structural Image Editing
C.Barnes, E.Shechtman, A.Finkelstein and Dan B.Goldman
SIGGRAPH 2009
Args:
image (Union[np.ndarray, Image.Image]): the input image, should be 3-channel RGB/BGR.
mask (Union[np.array, Image.Image], optional): the mask of the hole(s) to be filled, should be 1-channel.
If not provided (None), the algorithm will treat all purely white pixels as the holes (255, 255, 255).
global_mask (Union[np.array, Image.Image], optional): the target mask of the output image.
patch_size (int): the patch size for the inpainting algorithm.
Return:
result (np.ndarray): the repaired image, of the same size as the input image.
"""
if isinstance(image, Image.Image):
image = np.array(image)
image = np.ascontiguousarray(image)
assert image.ndim == 3 and image.shape[2] == 3 and image.dtype == 'uint8'
if mask is None:
mask = (image == (255, 255, 255)).all(axis=2, keepdims=True).astype('uint8')
mask = np.ascontiguousarray(mask)
else:
mask = _canonize_mask_array(mask)
if global_mask is None:
ret_pymat = PMLIB.PM_inpaint(np_to_pymat(image), np_to_pymat(mask), ctypes.c_int(patch_size))
else:
global_mask = _canonize_mask_array(global_mask)
ret_pymat = PMLIB.PM_inpaint2(np_to_pymat(image), np_to_pymat(mask), np_to_pymat(global_mask), ctypes.c_int(patch_size))
ret_npmat = pymat_to_np(ret_pymat)
PMLIB.PM_free_pymat(ret_pymat)
return ret_npmat
def inpaint_regularity(
image: Union[np.ndarray, Image.Image],
mask: Optional[Union[np.ndarray, Image.Image]],
ijmap: np.ndarray,
*,
global_mask: Optional[Union[np.ndarray, Image.Image]] = None,
patch_size: int = 15, guide_weight: float = 0.25
) -> np.ndarray:
if isinstance(image, Image.Image):
image = np.array(image)
image = np.ascontiguousarray(image)
assert isinstance(ijmap, np.ndarray) and ijmap.ndim == 3 and ijmap.shape[2] == 3 and ijmap.dtype == 'float32'
ijmap = np.ascontiguousarray(ijmap)
assert image.ndim == 3 and image.shape[2] == 3 and image.dtype == 'uint8'
if mask is None:
mask = (image == (255, 255, 255)).all(axis=2, keepdims=True).astype('uint8')
mask = np.ascontiguousarray(mask)
else:
mask = _canonize_mask_array(mask)
if global_mask is None:
ret_pymat = PMLIB.PM_inpaint_regularity(np_to_pymat(image), np_to_pymat(mask), np_to_pymat(ijmap), ctypes.c_int(patch_size), ctypes.c_float(guide_weight))
else:
global_mask = _canonize_mask_array(global_mask)
ret_pymat = PMLIB.PM_inpaint2_regularity(np_to_pymat(image), np_to_pymat(mask), np_to_pymat(global_mask), np_to_pymat(ijmap), ctypes.c_int(patch_size), ctypes.c_float(guide_weight))
ret_npmat = pymat_to_np(ret_pymat)
PMLIB.PM_free_pymat(ret_pymat)
return ret_npmat
def _canonize_mask_array(mask):
if isinstance(mask, Image.Image):
mask = np.array(mask)
if mask.ndim == 2 and mask.dtype == 'uint8':
mask = mask[..., np.newaxis]
assert mask.ndim == 3 and mask.shape[2] == 1 and mask.dtype == 'uint8'
return np.ascontiguousarray(mask)
dtype_pymat_to_ctypes = [
ctypes.c_uint8,
ctypes.c_int8,
ctypes.c_uint16,
ctypes.c_int16,
ctypes.c_int32,
ctypes.c_float,
ctypes.c_double,
]
dtype_np_to_pymat = {
'uint8': 0,
'int8': 1,
'uint16': 2,
'int16': 3,
'int32': 4,
'float32': 5,
'float64': 6,
}
def np_to_pymat(npmat):
assert npmat.ndim == 3
return CMatT(
ctypes.cast(npmat.ctypes.data, ctypes.c_void_p),
CShapeT(npmat.shape[1], npmat.shape[0], npmat.shape[2]),
dtype_np_to_pymat[str(npmat.dtype)]
)
def pymat_to_np(pymat):
npmat = np.ctypeslib.as_array(
ctypes.cast(pymat.data_ptr, ctypes.POINTER(dtype_pymat_to_ctypes[pymat.dtype])),
(pymat.shape.height, pymat.shape.width, pymat.shape.channels)
)
ret = np.empty(npmat.shape, npmat.dtype)
ret[:] = npmat
return ret
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