OpenPano无法缝合文本，因为它无法检索足够的特征点（或关键点）来进行缝合过程。
文字拼接不需要一种对旋转具有鲁棒性的匹配方法，而仅对翻译具有鲁棒性。OpenCV方便地提供了这样的功能。它称为：模板匹配。
我将开发的解决方案基于此OpenCV的功能。
管道
现在，我将解释解决方案的主要步骤（有关更多详细信息，请参见下面提供的代码）。
匹配过程
为了匹配两个连续的图像（在matchImages函数中完成，请参见下面的代码）：
- 我们通过获取第一张图片的45％（）来创建模板图片，
H_templ_ratio如下所示：
该步骤是通过函数在我的代码中完成的genTemplate。
- 我们将黑边距添加到第二个图像（我们要在其中找到模板）。如果文本在输入图像中未对齐，则必须执行此步骤（尽管在这些示例图像中就是这种情况）。这是边距处理后图像的外观。如您所见，仅在图像下面和下面需要边距：
理论上，模板图像可以在该空白图像中的任何位置找到。该过程在addBlackMargins功能中完成。
- 我们在模板图像和我们要查找的图像上都应用了canny过滤器（在函数内部完成）。这将增加稳健性的匹配过程。这是一个例子：
Mat2Edges
- 我们 使用来将模板与图像进行
matchTemplate匹配，并使用minMaxLoc函数来检索最佳匹配位置。
计算最终图像尺寸
此步骤包括计算最终矩阵的大小，在这里我们将所有图像拼接在一起。如果所有输入图像的高度都不相同，则特别需要此设置。
此步骤在calcFinalImgSize函数内部完成。我不会在这里讨论太多细节，因为尽管它看起来有点复杂（至少对我而言），但这只是简单的数学运算（加法，减法，乘法）。如果您想了解这些公式，请用笔和纸。
拼接过程
一旦我们拥有了比赛地点为每个输入图像，我们只需要做简单的数学要复制的输入图像中的正确的位置最终图像。再次，我建议您检查代码以了解实现的详细信息（请参见stitchImages功能）。
结果
这是输入图像的结果：
如您所见，结果不是“ 完美像素 ”，但对于OCR来说应该足够好了。
这是不同高度的输入图像的另一个结果：
程式码（Python）
我的程序是用Python编写的，并且使用cv2（OpenCV）和numpy模块。但是，它可以（轻松地）以其他语言（例如C ++，Java和C＃）移植。
import numpy as np
import cv2
def genTemplate(img):
global H_templ_ratio
# we get the image's width and height
h, w = img.shape[:2]
# we compute the template's bounds
x1 = int(float(w)*(1-H_templ_ratio))
y1 = 0
x2 = w
y2 = h
return(img[y1:y2,x1:x2]) # and crop the input image
def mat2Edges(img): # applies a Canny filter to get the edges
edged = cv2.Canny(img, 100, 200)
return(edged)
def addBlackMargins(img, top, bottom, left, right): # top, bottom, left, right: margins width in pixels
h, w = img.shape[:2]
result = np.zeros((h+top+bottom, w+left+right, 3), np.uint8)
result[top:top+h,left:left+w] = img
return(result)
# return the y_offset of the first image to stitch and the final image size needed
def calcFinalImgSize(imgs, loc):
global V_templ_ratio, H_templ_ratio
y_offset = 0
max_margin_top = 0; max_margin_bottom = 0 # maximum margins that will be needed above and bellow the first image in order to stitch all the images into one mat
current_margin_top = 0; current_margin_bottom = 0
h_init, w_init = imgs[0].shape[:2]
w_final = w_init
for i in range(0,len(loc)):
h, w = imgs[i].shape[:2]
h2, w2 = imgs[i+1].shape[:2]
# we compute the max top/bottom margins that will be needed (relatively to the first input image) in order to stitch all the images
current_margin_top += loc[i][1] # here, we assume that the template top-left corner Y-coordinate is 0 (relatively to its original image)
current_margin_bottom += (h2 - loc[i][1]) - h
if(current_margin_top > max_margin_top): max_margin_top = current_margin_top
if(current_margin_bottom > max_margin_bottom): max_margin_bottom = current_margin_bottom
# we compute the width needed for the final result
x_templ = int(float(w)*H_templ_ratio) # x-coordinate of the template relatively to its original image
w_final += (w2 - x_templ - loc[i][0]) # width needed to stitch all the images into one mat
h_final = h_init + max_margin_top + max_margin_bottom
return (max_margin_top, h_final, w_final)
# match each input image with its following image (1->2, 2->3)
def matchImages(imgs, templates_loc):
for i in range(0,len(imgs)-1):
template = genTemplate(imgs[i])
template = mat2Edges(template)
h_templ, w_templ = template.shape[:2]
# Apply template Matching
margin_top = margin_bottom = h_templ; margin_left = margin_right = 0
img = addBlackMargins(imgs[i+1],margin_top, margin_bottom, margin_left, margin_right) # we need to enlarge the input image prior to call matchTemplate (template needs to be strictly smaller than the input image)
img = mat2Edges(img)
res = cv2.matchTemplate(img,template,cv2.TM_CCOEFF) # matching function
_, _, _, templ_pos = cv2.minMaxLoc(res) # minMaxLoc gets the best match position
# as we added margins to the input image we need to subtract the margins width to get the template position relatively to the initial input image (without the black margins)
rectified_templ_pos = (templ_pos[0]-margin_left, templ_pos[1]-margin_top)
templates_loc.append(rectified_templ_pos)
print("max_loc", rectified_templ_pos)
def stitchImages(imgs, templates_loc):
y_offset, h_final, w_final = calcFinalImgSize(imgs, templates_loc) # we calculate the "surface" needed to stitch all the images into one mat (and y_offset, the Y offset of the first image to be stitched)
result = np.zeros((h_final, w_final, 3), np.uint8)
#initial stitch
h_init, w_init = imgs[0].shape[:2]
result[y_offset:y_offset+h_init, 0:w_init] = imgs[0]
origin = (y_offset, 0) # top-left corner of the last stitched image (y,x)
# stitching loop
for j in range(0,len(templates_loc)):
h, w = imgs[j].shape[:2]
h2, w2 = imgs[j+1].shape[:2]
# we compute the coordinates where to stitch imgs[j+1]
y1 = origin[0] - templates_loc[j][1]
y2 = origin[0] - templates_loc[j][1] + h2
x_templ = int(float(w)*(1-H_templ_ratio)) # x-coordinate of the template relatively to its original image's right side
x1 = origin[1] + x_templ - templates_loc[j][0]
x2 = origin[1] + x_templ - templates_loc[j][0] + w2
result[y1:y2, x1:x2] = imgs[j+1] # we copy the input image into the result mat
origin = (y1,x1) # we update the origin point with the last stitched image
return(result)
if __name__ == '__main__':
# input images
part1 = cv2.imread('part1.jpg')
part2 = cv2.imread('part2.jpg')
part3 = cv2.imread('part3.jpg')
imgs = [part1, part2, part3]
H_templ_ratio = 0.45 # H_templ_ratio: horizontal ratio of the input that we will keep to create a template
templates_loc = [] # templates location
matchImages(imgs, templates_loc)
result = stitchImages(imgs, templates_loc)
cv2.imshow("result", result)
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