打开简历快照指向特定尺寸的矩形

Question

我试图在具有旋转和翻译方差的质量下降质量页面上检测某种类型的图像。我需要从页面上“裁剪”检测到的图像，因此我需要旋转和检测到的图像的坐标。例如，已在A4页面上拍摄的图像。

我正在使用SIFT检测对象扫描页面。这些图像可以旋转和翻译，但没有她看上去或有任何视角失真。我正在使用经典（SIFT，SURF，ORB等）方法，但是它假设透视转换是为了创建边界多边形的4个点。此处的问题是因为关键点不能完美地排列（由于图像质量的变化，投影假定空间失真，并且多边形被正确扭曲。

• 我想尝试的方法是“捕捉”检测到的多边形点/尺寸/ 的旋转角度

输入

• 在页面上确定图像
• 图像的区域应允许我 但是，他们假设样品的点是等距的，并且没有近似值
• 。

def detect(img, frame, detector):
    frame = frame.copy()
    kp1, desc1 = detector.detectAndCompute(img, None)
    kp2, desc2 = detector.detectAndCompute(frame, None)

    index_params = dict(algorithm=0, trees=5)
    search_params = dict()
    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(desc1, desc2, k=2)
    good_points = []
    for m, n in matches:
        if m.distance < 0.5 * n.distance:
            good_points.append(m)
            if(len(good_points) == 20):
                break

    # out_img=cv2.drawMatches(img, kp1, frame, kp2, good_points, flags=2, outImg=None)
    # plt.figure(figsize = (6*4, 8*4))
    # plt.imshow(out_img)        
    
    if len(good_points) > 10: # at least 6 matches are required
        # Get the matching points
        query_pts = np.float32([kp1[m.queryIdx].pt for m in good_points]).reshape(-1, 1, 2)
        train_pts = np.float32([kp2[m.trainIdx].pt for m in good_points]).reshape(-1, 1, 2)
        
        matrix, mask = cv2.findHomography(query_pts, train_pts, cv2.RANSAC, 5.0)
        matches_mask = mask.ravel().tolist()
        h, w = img.shape
        pts = np.float32([[0, 0], [0, h], [w, h], [w, 0]]).reshape(-1, 1, 2)
        dst = cv2.perspectiveTransform(pts, matrix)
        
        
        overlayImage = cv2.polylines(frame, [np.int32(dst)], True, (0, 0, 0), 3)
        plt.figure(figsize = (6*2, 8*2))
        plt.imshow(overlayImage)

orb = cv2.SIFT_create()
for frame in frames:
    detect(img, frame, orb)

• 我们试图在其上检测到的图像的页面的示例。
• 这
• 是

Answer 1

我偶然发现了一个帖子，该帖子向您展示了如何从一组点中提取最小边界框。这也非常有效，因为它也披露了旋转。

def detect_ICP(img, frame, detector):
frame = frame.copy()
kp1, desc1 = detector.detectAndCompute(img, None)
kp2, desc2 = detector.detectAndCompute(frame, None)

index_params = dict(algorithm=0, trees=5)
search_params = dict()
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(desc1, desc2, k=2)
matches = sorted(matches, key = lambda x:x[0].distance + 0.5 * x[1].distance)
good_points = []
for m, n in matches:
    if m.distance < 0.5 * n.distance:
        good_points.append(m)
    
out_img=cv2.drawMatches(img, kp1, frame, kp2, good_points, flags=2, outImg=None)
plt.figure(figsize = (6*4, 8*4))
plt.imshow(out_img)        

if len(good_points) > 10: # at least 6 matches are required
    # Get the matching points
    query_pts = np.float32([kp1[m.queryIdx].pt for m in good_points]).reshape(-1, 1, 2)
    train_pts = np.float32([kp2[m.trainIdx].pt for m in good_points]).reshape(-1, 1, 2)
    
    matrix, mask = cv2.findHomography(query_pts, train_pts, cv2.RANSAC, 5.0)
    # matches_mask = mask.ravel().tolist()
    h, w = img.shape
    pts = np.float32([[0, 0], [0, h], [w, h], [w, 0]]).reshape(-1, 1, 2)
    dst = cv2.perspectiveTransform(pts, matrix)
    
    # determine the minimum bounding box
    minAreaRect = cv2.minAreaRect(dst)    # This will have size and rotation information
    rotatedBox = cv2.boxPoints(minAreaRect)
    rotatedBox = np.float32(rotatedBox).reshape(-1, 1, 2)
    
    overlayImage = cv2.polylines(frame, [np.int32(rotatedBox)], True, (0, 0, 0), 3)
    plt.figure(figsize = (6*2, 8*2))
    plt.imshow(overlayImage)