使用OpenCV和圆角卡进行更好的边缘检测

Question

我一直在PyImageSearch.com使用精彩的教程来获得Pi（v3）来识别一些扑克牌。到目前为止，它一直在努力，但教程中描述的方法更多的是对于尖角矩形，当然扑克牌是圆角的。这意味着轮廓角最终被绘制为略微偏移到实际卡片，因此我得到的裁剪和去扭曲图像略微旋转，这略微抛出了镜头识别。 绿色轮廓是由OpenCV提供的，您可以看到与我绘制的红线相比，它标记了它偏移/旋转的实际边界。我的问题是;如何让它跟随那些红线，即检测边缘？

这是当前为获得该结果而运行的代码：

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frame = vs.read()
    frame = cv2.flip(frame, 1)
    frame = imutils.resize(frame, width=640)
    image = frame.copy() #copy frame so that we don't get funky contour problems when drawing contours directly onto the frame.
    
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    gray = cv2.bilateralFilter(gray, 11, 17, 17) 
    edges = imutils.auto_canny(gray)

    cv2.imshow("Edge map", edges)

    #find contours in the edged image, keep only the largest
    # ones, and initialize our screen contour
    _, cnts, _ = cv2.findContours(edges.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:3]
    screenCnt = None

    # loop over our contours
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.05 * peri, True)
     
        # if our approximated contour has four points, then
        # we can assume that we have found our card
        if len(approx) == 4:
            screenCnt = approx
            break

    cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 3)

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Answer 1

原来我只需要阅读OpenCV contour docs一点。我基本上寻找的是我的轮廓周围的最小区域框：

rect = cv2.minAreaRect(cnt) # get a rectangle rotated to have minimal area
box = cv2.boxPoints(rect) # get the box from the rectangle
box = np.int0(box) # the box is now the new contour.

就我而言，screenCnt的所有实例现在都变为box变量，其余代码将继续正常运行。