python - 使用opencv python连接/合并图像

Question

我正在尝试从手写手稿创建基于纹理的图像。对输入图像(文本行形式的IAM数据库的二进制图像)进行一些预处理之后，使用垂直轮廓投影将这些行分割为单词/字符。分割的单词/字符的大小不同，我想对其进行合并/合并以形成所需的基于纹理的图像。输出图像的大小使叠加变得不可能。我正在将openCV与python结合使用，我想要一些想法或方法来完成此类任务。此方法的灵感来自R. K. Hanusiak the article link在第219-220页中的“使用基于纹理的特征进行作家验证”。

Concatenated images alined with it's center of mass

Text sample on the left, and it's texture-base images on the right

Answer 1

这是一个可能的解决方案。当然，您必须调整一些参数...

我的示例代码做什么:

应用threshold并将图像反转(bitwise_not)以获取具有黑色背景和白色字母的二进制图像

应用一个小的dilate合并一些小元素并减少检测次数

使用findContours来...找到轮廓:)

为每个轮廓计算boundingRect和area，返回检测到文字的矩形(可用于过滤不想要的小元素的区域)

准备一个与源图像重叠的轮廓和矩形图像(此部分仅用于调试)

检测到之后，代码继续创建所需的新“纹理图像”:

total_width是所有矩形宽度

的总和

mean_height是所有爬行高度的平均值

total_lines是新图像中的行数；根据total_width和mean_height计算得出，因此生成的图像近似为方形

循环内的

，我们将每个矩形从src图像复制到newImg

curr_line和curr_width跟踪将src矩形粘贴到

的位置

我已经使用cv.min()将每个新矩形混合到newImg中了；这类似于Photoshop

中的“暗化”混合模式

显示检测结果的图像:



生成的纹理图像:



代码...

import cv2 as cv
import numpy as np
import math

src = cv.imread("handwriting.jpg")
src_gray = cv.cvtColor(src,cv.COLOR_BGR2GRAY)

# apply threshold
threshold = 230
_, img_thresh = cv.threshold(src_gray, threshold, 255, 0)
img_thresh = cv.bitwise_not(img_thresh)

# apply dilate
dilatation_size = 1
dilatation_type = cv.MORPH_ELLIPSE
element = cv.getStructuringElement(dilatation_type, (2*dilatation_size + 1, 2*dilatation_size+1), (dilatation_size, dilatation_size))
img_dilate = cv.dilate(img_thresh, element)

# find contours
contours = cv.findContours(img_dilate, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

# calculate rectangles and areas
boundRect = [None]*len(contours[1])
areas = [None]*len(contours[1])
for i, c in enumerate(contours[1]):
    boundRect[i] = cv.boundingRect(c)
    areas[i] = cv.contourArea(c)

# set drawing 
drawing = np.zeros((src.shape[0], src.shape[1], 3), dtype=np.uint8)

# you can use only contours bigger than some area
for i in range(len(contours[1])):
    if areas[i] > 1:
        color = (50,50,0)
        cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
          (int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)

# set newImg
newImg = np.ones((src.shape[0], src.shape[1], 3), dtype=np.uint8)*255
total_width = 0
mean_height = 0.0
n = len(boundRect)
for r in (boundRect):
    total_width += r[2]
    mean_height += r[3]/n

total_lines = math.ceil(math.sqrt(total_width/mean_height))
max_line_width = math.floor(total_width/total_lines)

# loop through rectangles and perform a kind of copy paste
curr_line = 0
curr_width = 0
for r in (boundRect):
    if curr_width > max_line_width:
        curr_line += 1
        curr_width = 0
    # this is the position in newImg, where to insert source rectangle
    pos = [curr_width, \
           curr_width + r[2], \
           math.floor(curr_line*mean_height), \
           math.floor(curr_line*mean_height) + r[3] ]
    s = src[r[1]:r[1]+r[3], r[0]:r[0]+r[2], :]
    d = newImg[pos[2]:pos[3], pos[0]:pos[1], :]
    newImg[pos[2]:pos[3], pos[0]:pos[1], :] = cv.min(d,s)
    curr_width += r[2]

cv.imwrite('detection.png',cv.subtract(src,drawing))
cv.imshow('blend',cv.subtract(src,drawing))

crop = int(max_line_width*1.1)
cv.imwrite('texture.png',newImg[:crop, :crop, :])
cv.imshow('newImg',newImg[:crop, :crop, :])

cv.waitKey()