PythonOpenCV实现基于模板的图像拼接
之前基于特征点的图像拼接如果是多张图，每次计算变换矩阵，都有误差，最后可以图像拼完就变形很大，基于模板的方法可以很好的解决这一问题。
import cv2
import numpy as np
def matchStitch(imageLeft, imageRight):
ImageLeft_gray = cv2.cvtColor(imageLeft,cv2.COLOR_BGR2GRAY)
ImageRight_gray = cv2.cvtColor(imageRight,cv2.COLOR_BGR2GRAY)
# cv2.imshow("gray", ImageLeft_gray)
# cv2.waitKey()
# 获取图像长宽
height_Left, width_left = ImageLeft_gray.shape[:2]
height_Right, width_Right = ImageRight_gray.shape[:2]
# 模板区域
left_width_begin = int(3*width_left/4)
left_height_begin = 0
template_left = imageLeft[left_height_begin:int(height_Left/2), left_width_begin: width_left]
drawLeftRect = imageLeft.copy()
cv2.rectangle(drawLeftRect, (left_width_begin, left_height_begin), (width_left, int(height_Left/2) ), (0, 0, 255), 1)
cv2.imshow("template_left", drawLeftRect)
# cv2.waitKey()
# 右边匹配区域
match_right = imageRight[0:height_Right, 0: int(2*width_Right/3)]
# cv2.imshow("match_right", match_right)
# cv2.waitKey()
# 执行模板匹配，采用的匹配方式cv2.TM_CCOEFF_NORMED
matchResult = cv2.matchTemplate(match_right, template_left, cv2.TM_CCOEFF_NORMED)
# 归一化处理
cv2.normalize( matchResult, matchResult, 0, 1, cv2.NORM_MINMAX, -1 )
# 寻找矩阵（一维数组当做向量，用Mat定义）中的最大值和最小值的匹配结果及其位置
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(matchResult)
# 设置最终图片大小
dstStitch = np.zeros((height_Left, width_Right + left_width_begin - max_loc[0] , 3), imageLeft.dtype)
# imageLeft.dtype
# print(imageLeft.dtype)
height_dst, width_dst = dstStitch.shape[:2]
# copy left image
dstStitch[0:height_Left, 0:width_left] = imageLeft.copy()
# cv2.imshow("src", dstStitch)
# 匹配右图的高要能和目标区域一样
matchRight_H = height_Right - max_loc[1] + left_height_begin
dst_y_start = 0
if height_dst == matchRight_H:
matchRight = imageRight[max_loc[1] - left_height_begin: height_Right, max_loc[0]:width_Right]
elif height_dst < matchRight_H:
matchRight = imageRight[max_loc[1] - left_height_begin: height_Right - 1, max_loc[0]:width_Right]
else:
matchRight = imageRight[max_loc[1] - left_height_begin: height_Right, max_loc[0]:width_Right]
dst_y_start = height_dst - matchRight_H
# copy right image
# matchRight = imageRight[max_loc[1] - left_height_begin: height_Right, max_loc[0]:width_Right]
drawRightRect = imageRight.copy()
h, w = template_left.shape[:2]
cv2.rectangle(drawRightRect, (max_loc[0],max_loc[1]), (max_loc[0] + w, max_loc[1] + h ), (0, 0, 255), 1)
#
cv2.imshow("drawRightRect", drawRightRect)
# cv2.imshow("matchRight", matchRight)
# print("height_Right " + str(height_Right - max_loc[1] + left_height_begin))
# print("matchRight" + str(matchRight.shape))
height_mr, width_mr = matchRight.shape[:2]
# print("dstStitch" + str(dstStitch.shape))
dstStitch[dst_y_start:height_dst, left_width_begin:width_mr + left_width_begin] = matchRight.copy()
# # 图像融合处理相图相交的地方 效果不好
# for i in range(0, height_dst):
# # if i + winHeight > height:
# # i_heiht = True
# for j in range(0, width_dst):
# if j == left_width_begin:
#
# j += 1
# (b1, g1, r1) = dstStitch[i, j]
# j -= 1
#
# dstStitch[i, j] = (b1, g1, r1)
# cv2.imwrite("fineFlower04.jpg", dstStitch)
cv2.imshow("dstStitch", dstStitch)
cv2.waitKey()
if __name__ == "__main__":
# imageLeft = cv2.imread("Images/Scan/2.jpg")
# imageRight = cv2.imread("Images/Scan/3.jpg")
imageLeft = cv2.imread("Images/Scan/flower05.jpg")
imageRight = cv2.imread("Images/Scan/flower06.jpg")
if imageLeft is None or imageRight is None:
print("NOTICE: No images")
else:
# cv2.imshow("image", imageLeft)
# cv2.waitKey()
matchStitch(imageLeft, imageRight)
计算时需要注意的是模板区域一定要在拼接的左右两张图中都有，如果疏忽导致左图中模板较大，而右较中选的区域没有完整的模型就接错了。
# 右边匹配区域
match_right = imageRight[0:height_Right, 0: int(width_Right/2)]
右边先一半，一部分模板的不在里面了，就会拼的效果不好
边缘的区域还有改进的地方，后面有空再写。
到此这篇关于Python OpenCV实现基于模板的图像拼接的文章就介绍到这了,更多相关Python OpenCV图像拼接内容请搜索软件开发网以前的文章或继续浏览下面的相关文章希望大家以后多多支持软件开发网！
相关文章
Floria
2020-11-08
Calandra
2022-11-04
Maha
2022-11-04
Hazel
2022-11-06
Peony
2022-11-06
Kitty
2022-11-06
Hazel
2022-11-06
Viveka
2022-11-06
Psyche
2022-11-09
Olivia
2022-11-10
Nyako
2023-02-25
Jamina
2023-02-26
Malinda
2023-03-02
Nissa
2023-04-18
Viridis
2023-05-18
Pandora
2023-07-07
Jayne
2023-07-21