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import cv2
import numpy as np
import torch
def blur_image_by_mask(image, mask, kernel_size=23):
mask=mask.astype(np.uint8)
# 对图像和掩码进行位与运算，得到只有目标区域的图像
masked = image.copy()#cv2.bitwise_and(image, image, mask=mask)
# 对这个图像进行高斯模糊
blurred = cv2.GaussianBlur(masked, (kernel_size, kernel_size), 0)
# 用位或运算将模糊后的图像和原图像合并
#final = cv2.bitwise_or(image, blurred)
final = image.copy()
final[mask>0] = blurred[mask>0]
return final
def blur_image(image, center, radius):
# 创建一个和图像大小相同的掩码
mask = np.zeros(image.shape[:2], dtype="uint8")
center = center.astype(int)
# 在掩码上画出想要模糊的区域，例如一个圆形
#cv2.circle(mask, (center[0], center[1]), int(radius), 255, -1)
cv2.ellipse(mask, (center[0], center[1]), (int(radius), int(radius*1.5)), 0,0,360, 255, -1)
# 对图像和掩码进行位与运算，得到只有目标区域的图像
masked = image.copy()#cv2.bitwise_and(image, image, mask=mask)
# 对这个图像进行高斯模糊
blurred = cv2.GaussianBlur(masked, (23, 23), 0)
# 用位或运算将模糊后的图像和原图像合并
#final = cv2.bitwise_or(image, blurred)
final = image.copy()
final[mask>0] = blurred[mask>0]
return final
import torchvision.transforms as transforms
def poisson_blend_np(src:np.ndarray,dst:np.ndarray,mask:np.ndarray)->np.ndarray:
"""
* inputs:
- input (numpy array, required) shape = (H, W, 3).
- target (numpy array, required) shape = (H, W, 3).
- mask (np.bool, required) shape = (H, W).
Input mask tensor of Completion Network, whose shape = (N, 1, H, W).
* returns:
Output image numpy of shape (H, W, 3) inpainted with poisson image editing method.
"""
mask=np.expand_dims(mask,2)
mask=(np.concatenate([mask,mask,mask],2)*255).astype(np.uint8)
dstimg = dst
srcimg = src
msk=mask
# compute mask's center
xs, ys = [], []
for j in range(msk.shape[0]):
for k in range(msk.shape[1]):
if msk[j, k, 0] == 255:
ys.append(j)
xs.append(k)
xmin, xmax = min(xs), max(xs)
ymin, ymax = min(ys), max(ys)
center = ((xmax + xmin) // 2, (ymax + ymin) // 2)
dstimg = cv2.inpaint(dstimg, msk[:, :, 0], 1, cv2.INPAINT_TELEA)
out = cv2.seamlessClone(srcimg, dstimg, msk, center, cv2.NORMAL_CLONE)
return out
def poisson_blend(input, output, mask):
"""
* inputs:
- input (torch.Tensor, required)
Input tensor of Completion Network, whose shape = (N, 3, H, W).
- output (torch.Tensor, required)
Output tensor of Completion Network, whose shape = (N, 3, H, W).
- mask (torch.Tensor, required)
Input mask tensor of Completion Network, whose shape = (N, 1, H, W).
* returns:
Output image tensor of shape (N, 3, H, W) inpainted with poisson image editing method.
"""
input = input.clone().cpu()
output = output.clone().cpu()
mask = mask.clone().cpu()
mask = torch.cat((mask, mask, mask), dim=1) # convert to 3-channel format
num_samples = input.shape[0]
ret = []
for i in range(num_samples):
dstimg = transforms.functional.to_pil_image(input[i])
dstimg = np.array(dstimg)[:, :, [2, 1, 0]]
srcimg = transforms.functional.to_pil_image(output[i])
srcimg = np.array(srcimg)[:, :, [2, 1, 0]]
msk = transforms.functional.to_pil_image(mask[i])
msk = np.array(msk)[:, :, [2, 1, 0]]
# compute mask's center
xs, ys = [], []
for j in range(msk.shape[0]):
for k in range(msk.shape[1]):
if msk[j, k, 0] == 255:
ys.append(j)
xs.append(k)
xmin, xmax = min(xs), max(xs)
ymin, ymax = min(ys), max(ys)
center = ((xmax + xmin) // 2, (ymax + ymin) // 2)
dstimg = cv2.inpaint(dstimg, msk[:, :, 0], 1, cv2.INPAINT_TELEA)
out = cv2.seamlessClone(srcimg, dstimg, msk, center, cv2.NORMAL_CLONE)
out = out[:, :, [2, 1, 0]]
out = transforms.functional.to_tensor(out)
out = torch.unsqueeze(out, dim=0)
ret.append(out)
ret = torch.cat(ret, dim=0)
return ret |