目录
两图拼接使用渐进色蒙版场景过渡缝隙 还没测试
OpenCV开发笔记（八十二）：两图拼接使用渐进色蒙版场景过渡缝隙_长沙红胖子Qt-松山湖开发者村综合服务平台
图像泊松融合原理：
使用方法
需要三张图片：前景图、背景图、mask图
mask图（指明前景图中需要融合的区域，最简单的就是直接等于前景图大小的 mask，待融合区域是白色，其余位置黑色）。
center是背景图的融合中心坐标。
python opencv 泊松融合例子
不需要png格式，
# 注意修改路径！
import cv2
import numpy as np
# Read images : src image will be cloned into dst
im = cv2.imread("images/wood-texture.jpg")
obj= cv2.imread("images/iloveyouticket.jpg")
# Create an all white mask
mask = 255 * np.ones(obj.shape, obj.dtype)
# The location of the center of the src in the dst
height, width,channels = im.shape
center = (width/2,height/2)
# Seamlessly clone src into dst and put the results in output
normal_clone = cv2.seamlessClone(obj, im, mask, center, cv2.NORMAL_CLONE)
mixed_clone = cv2.seamlessClone(obj, im, mask, center, cv2.MIXED_CLONE)
# Write results
cv2.imwrite("images/opencv-normal-clone-example.jpg", normal_clone)
cv2.imwrite("images/opencv-mixed-clone-example.jpg", mixed_clone)转效果：
前景图：
背景图：
效果图：
简单总结，前景图的背景图，几乎纯色的时候，会自动被去掉。
根据mask图进行泊松融合
import cv2 as cv
import cv2
import numpy as np
import scipy.sparse as sp
from scipy.sparse.linalg import spsolve
from tqdm import trange
class PoisonBlender:
MAX_INTENSITY = 255
def __init__(self, source, target, mask, delta):
self.source = source
self.CHANNEL_SIZE = self.source.shape[2]
self.target = target
self.height, self.width, _ = self.target.shape
self.mask = mask
self.delta_x, self.delta_y = delta
@staticmethod
def get_helper_matrix(height, width):
block = sp.lil_matrix((width, width))
PoisonBlender.laplacian_taylor_approximation_block(block)
A = sp.block_diag([block] * height).tolil()
PoisonBlender.set_semi_main_diameter(A, width)
return A
def get_blended_channel(self, A, channel, f_laplacian, flatten_mask):
flatten_target = self.target[:self.height, :self.width, channel].flatten()
flatten_source = self.source[:self.height, :self.width, channel].flatten()
b = self.calculate_b_vector(flatten_mask, f_laplacian, flatten_source, flatten_target)
f = spsolve(A, b).reshape((self.height, self.width))
f = PoisonBlender.outlier_intensities_correction(f).astype('uint8')
return f
@staticmethod
def flatten2rectangular(matrix, height, width):
return matrix.reshape((height, width))
def translate(self, input, x, y):
translation_matrix = np.float32([[1, 0, x],
[0, 1, y]])
return cv.warpAffine(input, translation_matrix, (self.width, self.height))
@staticmethod
def calculate_b_vector(flatten_mask, laplacian, source_flat, target_flat):
b = laplacian.dot(source_flat)
b[flatten_mask == 0] = target_flat[flatten_mask == 0]
return b
@staticmethod
def outlier_intensities_correction(matrix):
matrix[matrix <= 0] = 0
matrix[matrix >= PoisonBlender.MAX_INTENSITY] = PoisonBlender.MAX_INTENSITY
return matrix
@staticmethod
def set_semi_main_diameter(A, width):
A.setdiag(-1, width)
A.setdiag(-1, -width)
@staticmethod
def laplacian_taylor_approximation_block(block):
# laplacian in x,y coordinate = 4f(x,y)-f(x-1,y)-f(x+1,y)-f(x,y-1)-f(x,y+1)
block.setdiag(-1, -1)
block.setdiag(-1, 1)
block.setdiag(4)
def set_out_pixel(self, coefficient_matrix, counter):
coefficient_matrix[counter, counter + self.width] = 0
coefficient_matrix[counter, counter - self.width] = 0
coefficient_matrix[counter, counter + 1] = 0
coefficient_matrix[counter, counter - 1] = 0
# set to identity (out of mask region)
coefficient_matrix[counter, counter] = 1
def blend(self):
# translate source image
self.source = self.translate(self.source, self.delta_x, self.delta_y)
# binary mask
self.mask[:self.height, :self.width][self.mask != 0] = 1
# initiate coefficient matrix in Af=b
A = self.get_helper_matrix(self.height, self.width)
f_l = A.tocsc()
self.set_out_submatrix_to_identity(A)
A = A.tocsc()
# calculate f from Af=b based on flatten matrices
flatten_mask = self.mask.flatten()
for channel in trange(self.CHANNEL_SIZE):
print('channel ' + str(channel) + ' blended')
self.target[:self.height, :self.width, channel] = self.get_blended_channel(A,
channel,
f_l,
flatten_mask)
return self.target
def set_out_submatrix_to_identity(self, coefficient_matrix):
# set to identity submatrix in out of mask region
for row in range(1, self.height - 1):
for col in range(1, self.width - 1):
if self.mask[row, col] == 0:
counter = col + row * self.width
# set to zero/one
self.set_out_pixel(coefficient_matrix, counter)
class PolygonMaker:
def __init__(self, points, mask_shape):
self.points = points
h, w, _ = mask_shape
self.mask = np.zeros((h, w), np.uint8)
class ClickHandler:
image = None
POINTS_SIZE = 0
def __init__(self, image, window_name):
self.image = image.copy()
self.window_name = window_name
cv.imshow(self.window_name, image)
h, w, _ = self.image.shape
self.counter = 0
self.points = []
print('clicked vertices of polygon:')
def get_points(self):
return np.array([[x, y] for x, y in self.points], np.int)
def click_event(self, event, clicked_x, clicked_y, flags, params):
if event == cv.EVENT_LBUTTONDOWN:
print(clicked_x, clicked_y)
point = np.array([clicked_x, clicked_y])
cv.imshow(self.window_name, self.image)
self.points.append(point)
def get_filled_polygon(self):
return cv.fillConvexPoly(self.mask, self.points, 255)
def main():
source_path = r"C:\Users\Administrator\Downloads\masks\part_3.jpg"
target_path = r"C:\Users\Administrator\Downloads\masks\part_2.jpg"
mask_path=r"C:\Users\Administrator\Downloads\masks\mask.jpg"
result_path = 'res2.jpg'
# read source and target image
source = cv.imread(source_path)
target = cv.imread(target_path)
mask_img=cv.imread(mask_path,0)
mask_img=255-mask_img
kernel = np.ones((15, 15), np.uint8)
# 进行形态学膨胀操作
dilated_image = cv2.dilate(mask_img, kernel, iterations=1)
# 展示原始图像和扩大后的图像
cv2.imshow('Dilated Image', dilated_image)
cv2.imshow('target',target)
cv2.waitKey()
cv2.destroyAllWindows()
delta=(0,0)
poisson_blend_result = PoisonBlender(source,
target,
mask_img,
delta).blend()
# save
cv.imwrite(result_path, poisson_blend_result)
if __name__ == '__main__':
main()
报错并解决：
Traceback (most recent call last):
File "F:/biadu_down/yolov5-face-master/data/ronghe/bosong.py", line 20, in <module>
mixed_clone = cv2.seamlessClone(obj, dst, mask, center, cv2.MIXED_CLONE)
cv2.error: OpenCV(4.5.4) :-1: error: (-5:Bad argument) in function 'seamlessClone'
> Overload resolution failed:
> - Can't parse 'p'. Sequence item with index 0 has a wrong type
> - Can't parse 'p'. Sequence item with index 0 has a wrong type
原因：数据类型不对，
center = (height/2, width/2)
center应该是int类型，修改方法：
center = (height//2, width//2)
修改后完整代码：
import cv2
import numpy as np
if __name__ == '__main__':
# Read images : src image will be cloned into dst
dst = cv2.imread("diban.jpg")
obj= cv2.imread("zimu.jpg")
# Create an all white mask
mask = 255 * np.ones(obj.shape, obj.dtype)
# The location of the center of the src in the dst
height,width, channels = dst.shape
center = (width//2,height//2)
# Seamlessly clone src into dst and put the results in output
normal_clone = cv2.seamlessClone(obj, dst, mask, center, cv2.NORMAL_CLONE)
mixed_clone = cv2.seamlessClone(obj, dst, mask, center, cv2.MIXED_CLONE)
# Write results
cv2.imshow("opencv-normal-clone-example.jpg", normal_clone)
cv2.imshow("opencv-mixed-clone-example.jpg", mixed_clone)
cv2.waitKey()泊松融合c++版：
https://github.com/Erkaman/poisson_blend
#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>
using namespace std;
using namespace cv;
void main()
{
Mat imgL = imread("data/apple.jpg");
Mat imgR = imread("data/orange.jpg");
int imgH = imgR.rows;
int imgW = imgR.cols;
Mat mask = Mat::zeros(imgL.size(), CV_8UC1);
mask(Rect(,, imgW*0.5, imgH)).setTo();
cv::imshow("mask", mask);
Point center(imgW*0.25, imgH*0.5);
Mat blendImg;
seamlessClone(imgL, imgR, mask, center, blendImg, NORMAL_CLONE);
cv::imshow("blendimg", blendImg);
waitKey();
}
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