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from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel
import numpy as np
import cupy as cp
import cv2
class PatchMatcher:
def __init__(
self, height, width, channel, minimum_patch_size,
threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0,
random_search_steps=3, random_search_range=4,
use_mean_target_style=False, use_pairwise_patch_error=False,
tracking_window_size=0
):
self.height = height
self.width = width
self.channel = channel
self.minimum_patch_size = minimum_patch_size
self.threads_per_block = threads_per_block
self.num_iter = num_iter
self.gpu_id = gpu_id
self.guide_weight = guide_weight
self.random_search_steps = random_search_steps
self.random_search_range = random_search_range
self.use_mean_target_style = use_mean_target_style
self.use_pairwise_patch_error = use_pairwise_patch_error
self.tracking_window_size = tracking_window_size
self.patch_size_list = [minimum_patch_size + i*2 for i in range(num_iter)][::-1]
self.pad_size = self.patch_size_list[0] // 2
self.grid = (
(height + threads_per_block - 1) // threads_per_block,
(width + threads_per_block - 1) // threads_per_block
)
self.block = (threads_per_block, threads_per_block)
def pad_image(self, image):
return cp.pad(image, ((0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size), (0, 0)))
def unpad_image(self, image):
return image[:, self.pad_size: -self.pad_size, self.pad_size: -self.pad_size, :]
def apply_nnf_to_image(self, nnf, source):
batch_size = source.shape[0]
target = cp.zeros((batch_size, self.height + self.pad_size * 2, self.width + self.pad_size * 2, self.channel), dtype=cp.float32)
remapping_kernel(
self.grid + (batch_size,),
self.block,
(self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target)
)
return target
def get_patch_error(self, source, nnf, target):
batch_size = source.shape[0]
error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32)
patch_error_kernel(
self.grid + (batch_size,),
self.block,
(self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target, error)
)
return error
def get_pairwise_patch_error(self, source, nnf):
batch_size = source.shape[0]//2
error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32)
source_a, nnf_a = source[0::2].copy(), nnf[0::2].copy()
source_b, nnf_b = source[1::2].copy(), nnf[1::2].copy()
pairwise_patch_error_kernel(
self.grid + (batch_size,),
self.block,
(self.height, self.width, self.channel, self.patch_size, self.pad_size, source_a, nnf_a, source_b, nnf_b, error)
)
error = error.repeat(2, axis=0)
return error
def get_error(self, source_guide, target_guide, source_style, target_style, nnf):
error_guide = self.get_patch_error(source_guide, nnf, target_guide)
if self.use_mean_target_style:
target_style = self.apply_nnf_to_image(nnf, source_style)
target_style = target_style.mean(axis=0, keepdims=True)
target_style = target_style.repeat(source_guide.shape[0], axis=0)
if self.use_pairwise_patch_error:
error_style = self.get_pairwise_patch_error(source_style, nnf)
else:
error_style = self.get_patch_error(source_style, nnf, target_style)
error = error_guide * self.guide_weight + error_style
return error
def clamp_bound(self, nnf):
nnf[:,:,:,0] = cp.clip(nnf[:,:,:,0], 0, self.height-1)
nnf[:,:,:,1] = cp.clip(nnf[:,:,:,1], 0, self.width-1)
return nnf
def random_step(self, nnf, r):
batch_size = nnf.shape[0]
step = cp.random.randint(-r, r+1, size=(batch_size, self.height, self.width, 2), dtype=cp.int32)
upd_nnf = self.clamp_bound(nnf + step)
return upd_nnf
def neighboor_step(self, nnf, d):
if d==0:
upd_nnf = cp.concatenate([nnf[:, :1, :], nnf[:, :-1, :]], axis=1)
upd_nnf[:, :, :, 0] += 1
elif d==1:
upd_nnf = cp.concatenate([nnf[:, :, :1], nnf[:, :, :-1]], axis=2)
upd_nnf[:, :, :, 1] += 1
elif d==2:
upd_nnf = cp.concatenate([nnf[:, 1:, :], nnf[:, -1:, :]], axis=1)
upd_nnf[:, :, :, 0] -= 1
elif d==3:
upd_nnf = cp.concatenate([nnf[:, :, 1:], nnf[:, :, -1:]], axis=2)
upd_nnf[:, :, :, 1] -= 1
upd_nnf = self.clamp_bound(upd_nnf)
return upd_nnf
def shift_nnf(self, nnf, d):
if d>0:
d = min(nnf.shape[0], d)
upd_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0)
else:
d = max(-nnf.shape[0], d)
upd_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0)
return upd_nnf
def track_step(self, nnf, d):
if self.use_pairwise_patch_error:
upd_nnf = cp.zeros_like(nnf)
upd_nnf[0::2] = self.shift_nnf(nnf[0::2], d)
upd_nnf[1::2] = self.shift_nnf(nnf[1::2], d)
else:
upd_nnf = self.shift_nnf(nnf, d)
return upd_nnf
def C(self, n, m):
# not used
c = 1
for i in range(1, n+1):
c *= i
for i in range(1, m+1):
c //= i
for i in range(1, n-m+1):
c //= i
return c
def bezier_step(self, nnf, r):
# not used
n = r * 2 - 1
upd_nnf = cp.zeros(shape=nnf.shape, dtype=cp.float32)
for i, d in enumerate(list(range(-r, 0)) + list(range(1, r+1))):
if d>0:
ctl_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0)
elif d<0:
ctl_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0)
upd_nnf += ctl_nnf * (self.C(n, i) / 2**n)
upd_nnf = self.clamp_bound(upd_nnf).astype(nnf.dtype)
return upd_nnf
def update(self, source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf):
upd_err = self.get_error(source_guide, target_guide, source_style, target_style, upd_nnf)
upd_idx = (upd_err < err)
nnf[upd_idx] = upd_nnf[upd_idx]
err[upd_idx] = upd_err[upd_idx]
return nnf, err
def propagation(self, source_guide, target_guide, source_style, target_style, nnf, err):
for d in cp.random.permutation(4):
upd_nnf = self.neighboor_step(nnf, d)
nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
return nnf, err
def random_search(self, source_guide, target_guide, source_style, target_style, nnf, err):
for i in range(self.random_search_steps):
upd_nnf = self.random_step(nnf, self.random_search_range)
nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
return nnf, err
def track(self, source_guide, target_guide, source_style, target_style, nnf, err):
for d in range(1, self.tracking_window_size + 1):
upd_nnf = self.track_step(nnf, d)
nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
upd_nnf = self.track_step(nnf, -d)
nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
return nnf, err
def iteration(self, source_guide, target_guide, source_style, target_style, nnf, err):
nnf, err = self.propagation(source_guide, target_guide, source_style, target_style, nnf, err)
nnf, err = self.random_search(source_guide, target_guide, source_style, target_style, nnf, err)
nnf, err = self.track(source_guide, target_guide, source_style, target_style, nnf, err)
return nnf, err
def estimate_nnf(self, source_guide, target_guide, source_style, nnf):
with cp.cuda.Device(self.gpu_id):
source_guide = self.pad_image(source_guide)
target_guide = self.pad_image(target_guide)
source_style = self.pad_image(source_style)
for it in range(self.num_iter):
self.patch_size = self.patch_size_list[it]
target_style = self.apply_nnf_to_image(nnf, source_style)
err = self.get_error(source_guide, target_guide, source_style, target_style, nnf)
nnf, err = self.iteration(source_guide, target_guide, source_style, target_style, nnf, err)
target_style = self.unpad_image(self.apply_nnf_to_image(nnf, source_style))
return nnf, target_style
class PyramidPatchMatcher:
def __init__(
self, image_height, image_width, channel, minimum_patch_size,
threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0,
use_mean_target_style=False, use_pairwise_patch_error=False,
tracking_window_size=0,
initialize="identity"
):
maximum_patch_size = minimum_patch_size + (num_iter - 1) * 2
self.pyramid_level = int(np.log2(min(image_height, image_width) / maximum_patch_size))
self.pyramid_heights = []
self.pyramid_widths = []
self.patch_matchers = []
self.minimum_patch_size = minimum_patch_size
self.num_iter = num_iter
self.gpu_id = gpu_id
self.initialize = initialize
for level in range(self.pyramid_level):
height = image_height//(2**(self.pyramid_level - 1 - level))
width = image_width//(2**(self.pyramid_level - 1 - level))
self.pyramid_heights.append(height)
self.pyramid_widths.append(width)
self.patch_matchers.append(PatchMatcher(
height, width, channel, minimum_patch_size=minimum_patch_size,
threads_per_block=threads_per_block, num_iter=num_iter, gpu_id=gpu_id, guide_weight=guide_weight,
use_mean_target_style=use_mean_target_style, use_pairwise_patch_error=use_pairwise_patch_error,
tracking_window_size=tracking_window_size
))
def resample_image(self, images, level):
height, width = self.pyramid_heights[level], self.pyramid_widths[level]
images = images.get()
images_resample = []
for image in images:
image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA)
images_resample.append(image_resample)
images_resample = cp.array(np.stack(images_resample), dtype=cp.float32)
return images_resample
def initialize_nnf(self, batch_size):
if self.initialize == "random":
height, width = self.pyramid_heights[0], self.pyramid_widths[0]
nnf = cp.stack([
cp.random.randint(0, height, (batch_size, height, width), dtype=cp.int32),
cp.random.randint(0, width, (batch_size, height, width), dtype=cp.int32)
], axis=3)
elif self.initialize == "identity":
height, width = self.pyramid_heights[0], self.pyramid_widths[0]
nnf = cp.stack([
cp.repeat(cp.arange(height), width).reshape(height, width),
cp.tile(cp.arange(width), height).reshape(height, width)
], axis=2)
nnf = cp.stack([nnf] * batch_size)
else:
raise NotImplementedError()
return nnf
def update_nnf(self, nnf, level):
# upscale
nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2
nnf[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1
nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],1] += 1
# check if scale is 2
height, width = self.pyramid_heights[level], self.pyramid_widths[level]
if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2:
nnf = nnf.get().astype(np.float32)
nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf]
nnf = cp.array(np.stack(nnf), dtype=cp.int32)
nnf = self.patch_matchers[level].clamp_bound(nnf)
return nnf
def apply_nnf_to_image(self, nnf, image):
with cp.cuda.Device(self.gpu_id):
image = self.patch_matchers[-1].pad_image(image)
image = self.patch_matchers[-1].apply_nnf_to_image(nnf, image)
return image
def estimate_nnf(self, source_guide, target_guide, source_style):
with cp.cuda.Device(self.gpu_id):
if not isinstance(source_guide, cp.ndarray):
source_guide = cp.array(source_guide, dtype=cp.float32)
if not isinstance(target_guide, cp.ndarray):
target_guide = cp.array(target_guide, dtype=cp.float32)
if not isinstance(source_style, cp.ndarray):
source_style = cp.array(source_style, dtype=cp.float32)
for level in range(self.pyramid_level):
nnf = self.initialize_nnf(source_guide.shape[0]) if level==0 else self.update_nnf(nnf, level)
source_guide_ = self.resample_image(source_guide, level)
target_guide_ = self.resample_image(target_guide, level)
source_style_ = self.resample_image(source_style, level)
nnf, target_style = self.patch_matchers[level].estimate_nnf(
source_guide_, target_guide_, source_style_, nnf
)
return nnf.get(), target_style.get()
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