Java opencv和python-激光曲线检测 我试图得到一组在这个曲线中间的点。 我找到了这个脚本，但我的激光图像不起作用 im_gray = cv2.imread(img, cv2.CV_LOAD_IMAGE_GRAYSCALE) im_gray = cv2.Canny(im_gray,50,150,apertureSize = 3) ret, im_bw = cv2.threshold(im_gray, 0, 255, cv2.THRESH_BINARY) #(thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) #thresh = 127 #im_bw = cv2.threshold(im_gray, thresh, 255, cv2.THRESH_BINARY)[1] #ret, bw = cv2.threshold(im_bw, 0, 255, cv2.THRESH_BINARY) cv2.imwrite('resultpoint_bw.png',im_bw) # find contours of the binarized image contours, heirarchy = cv2.findContours(im_bw, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # curves curves = np.zeros((im_bw.shape[0], im_bw.shape[1], 3), np.uint8) cv2.imwrite('resultpoint_bw_2.png',im_bw) for i in range(len(contours)): # for each contour, draw the filled contour draw = np.zeros((im_bw.shape[0], im_bw.shape[1]), np.uint8) cv2.drawContours(draw, contours, i, (255,255,255), -1) # for each column, calculate the centroid for col in range(draw.shape[0]): M = cv2.moments(draw[:, col]) if M['m00'] != 0: x = col y = int(M['m01']/M['m00']) curves[y, x, :] = (0, 0, 255) cv2.imwrite('resultpoint_0.png',curves) 在结果图像中，POIT是一个轮廓，不需要等高线，而中间需要Sigle点……/P>

有没有可能做到这一点？

您可以应用这些简单的步骤来获得这条中心线

阈值二进制反转

适用于减少厚度

在二值图像中查找非零像素

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我在python中找到了解决方案：

    import cv2
    import numpy as np
    import glob
    import json, io
    from matplotlib import pyplot as plt
    from PIL import Image

    img = cv2.imread(fname, 0);

    i = Image.fromarray(self.__imgremapped_bw)

    pixels = i.load() # this is not a list

    self.__pointsData = [];

    find = 0

    for y in range(self.__top,self.__bottom):
        row_averages = []
        for x in range(self.__top,self.__bottom):
            cur_pixel = pixels[x, y]
            if cur_pixel >= self.__thresholdColor:
                row_averages.append(x)
                find = 1
            elif find == 1:
                pointSum = 0
                for idx, val in enumerate(row_averages):
                    pointSum += row_averages[idx];

                xf = pointSum/len(row_averages)
                # 0.5 correzione pixel al centro
                self.__pointsData.append([[y+0.5,xf+0.5]])
                row_averages = []
                find = 0

    #self.__drawPoint(self.__imgremapped_bw)

    return self.__pointsData

self.\u顶部、self.\u底部和self.\u顶部、self.\u底部是用于优化提取点的裁剪区域

self.\uu pointsData.append（[[y+0.5，xf+0.5]]

+0.5是具有中心像素的固定值

在这种情况下，可能会有更多的线路，因为这些线路：

            if cur_pixel >= self.__thresholdColor:
                row_averages.append(x)
                find = 1
            elif find == 1:
                pointSum = 0
                for idx, val in enumerate(row_averages):
                    pointSum += row_averages[idx];

                xf = pointSum/len(row_averages)
                # 0.5 correzione pixel al centro
                self.__pointsData.append([[y+0.5,xf+0.5]])
                row_averages = []
                find = 0

有一个具有颜色范围的媒体点计算

我希望这会有帮助

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谢谢

为什么这个标签是代码> java < /C>和<代码> C++ >代码>？因为我错了：我需要Pyton、C++和OpenCV TAG你可以添加你的图像样本吗？在这里你可以找到IMG你解决了你的问题吗？如果这对您有帮助，请标记为答案！非常感谢你！现在我需要用python转换您的脚本。有可能提取坐标吗？我把它调高了，但要注意，激光散斑通常意味着最亮的像素不会在中心。您需要首先进行大量的模糊/平滑处理，或者使用加权平均值来获得精确的激光线中心。

    }

    import cv2
    import numpy as np
    import glob
    import json, io
    from matplotlib import pyplot as plt
    from PIL import Image

    img = cv2.imread(fname, 0);

    i = Image.fromarray(self.__imgremapped_bw)

    pixels = i.load() # this is not a list

    self.__pointsData = [];

    find = 0

    for y in range(self.__top,self.__bottom):
        row_averages = []
        for x in range(self.__top,self.__bottom):
            cur_pixel = pixels[x, y]
            if cur_pixel >= self.__thresholdColor:
                row_averages.append(x)
                find = 1
            elif find == 1:
                pointSum = 0
                for idx, val in enumerate(row_averages):
                    pointSum += row_averages[idx];

                xf = pointSum/len(row_averages)
                # 0.5 correzione pixel al centro
                self.__pointsData.append([[y+0.5,xf+0.5]])
                row_averages = []
                find = 0

    #self.__drawPoint(self.__imgremapped_bw)

    return self.__pointsData

            if cur_pixel >= self.__thresholdColor:
                row_averages.append(x)
                find = 1
            elif find == 1:
                pointSum = 0
                for idx, val in enumerate(row_averages):
                    pointSum += row_averages[idx];

                xf = pointSum/len(row_averages)
                # 0.5 correzione pixel al centro
                self.__pointsData.append([[y+0.5,xf+0.5]])
                row_averages = []
                find = 0