从图像中裁剪掉部分(findContours？){opencv,java}

Question

我有这个包含字母的框的图像,如下所示:

我已经能够裁掉每个盒子了,像这样:

现在问我的问题.我怎样才能从每个盒子中裁剪掉字母？期望的结果如下所示

我想使用findContours,但我不确定如何实现这一点,因为它将检测噪音和周围的一切.

Answer 1

方法 我根据这个事实建议以下方法,你可以提取框.如果您按照步骤给出框,我认为这将有效:

1. 找到图像的中心
2. 找到图像中的轮廓 - 可以是候选者
3. 找到每个轮廓的边界矩形
4. 找到每个边界矩形的中心
5. 从图像中心找到每个边界矩形的距离
6. 找到最小距离 - 你的答案

注意:有一个var命名pad控制结果数字的填充!为你的所有盒子做这个.我希望这会有所帮助!

祝好运 :)

Python代码

# reading image in grayscale
image = cv2.imread('testing2.jpg',cv2.CV_LOAD_IMAGE_GRAYSCALE)
# thresholding to get a binary one
ret, image = cv2.threshold(image, 100,255,cv2.THRESH_BINARY_INV)
# finding the center of image
image_center = (image.shape[0]/2, image.shape[1]/2)

if image is None:
    print 'can not read the image data'
# finding image contours
contours, hier = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# finding distance of each contour from the center of image
d_min = 1000
for contour in contours:
    # finding bounding rect
    rect = cv2.boundingRect(contour)
    # skipping the outliers
    if rect[3] > image.shape[1]/2 and rect[2] > image.shape[0]/2:
        continue
    pt1 = (rect[0], rect[1])
    # finding the center of bounding rect-digit
    c = (rect[0]+rect[2]*1/2, rect[1]+rect[3]*1/2)
    d = np.sqrt((c[0] - image_center[0])**2 + (c[1]-image_center[1])**2)
    # finding the minimum distance from the center
    if d < d_min:
        d_min = d
        rect_min = [pt1, (rect[2],rect[3])]
# fetching the image with desired padding
pad = 5
result = image[rect_min[0][1]-pad:rect_min[0][1]+rect_min[1][1]+pad, rect_min[0][0]-pad:rect_min[0][0]+rect_min[1][0]+pad]

plt.imshow(result*255, 'gray')
plt.show()

Java代码

    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    // reading image 
    Mat image = Highgui.imread(".\\testing2.jpg", Highgui.CV_LOAD_IMAGE_GRAYSCALE);
    // clone the image 
    Mat original = image.clone();
    // thresholding the image to make a binary image
    Imgproc.threshold(image, image, 100, 128, Imgproc.THRESH_BINARY_INV);
    // find the center of the image
    double[] centers = {(double)image.width()/2, (double)image.height()/2};
    Point image_center = new Point(centers);

    // finding the contours
    ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Mat hierarchy = new Mat();
    Imgproc.findContours(image, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);

    // finding best bounding rectangle for a contour whose distance is closer to the image center that other ones
    double d_min = Double.MAX_VALUE;
    Rect rect_min = new Rect();
    for (MatOfPoint contour : contours) {
        Rect rec = Imgproc.boundingRect(contour);
        // find the best candidates
        if (rec.height > image.height()/2 & rec.width > image.width()/2)            
            continue;
        Point pt1 = new Point((double)rec.x, (double)rec.y);
        Point center = new Point(rec.x+(double)(rec.width)/2, rec.y + (double)(rec.height)/2);
        double d = Math.sqrt(Math.pow((double)(pt1.x-image_center.x),2) + Math.pow((double)(pt1.y -image_center.y), 2));            
        if (d < d_min)
        {
            d_min = d;
            rect_min = rec;
        }                   
    }
    // slicing the image for result region
    int pad = 5;        
    rect_min.x = rect_min.x - pad;
    rect_min.y = rect_min.y - pad;

    rect_min.width = rect_min.width + 2*pad;
    rect_min.height = rect_min.height + 2*pad;

    Mat result = original.submat(rect_min);     
    Highgui.imwrite("result.png", result);

编辑: 添加了Java代码!

结果