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。
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