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python - 轮廓旋转后OpenCV将不规则轮廓区域复制到另一张图像

问题描述

我正在处理带有扭曲/旋转文本的图像。在对它们运行 OCR 之前，我需要将这些文本块旋转回水平水平（0 度）。我设法解决了旋转问题，但现在我需要找到一种方法将原始轮廓的内容复制到旋转矩阵。

以下是我为提取和修复轮换问题所做的一些事情：

  1. 寻找轮廓
  2. 重度膨胀和去除非文本行
  3. 在极地空间中找到轮廓角并进行角度校正。

我曾尝试使用仿射变换来旋转矩形文本块，但由于某些文本块是不规则的，因此最终会裁剪掉一些文本。结果在这里

轮廓中的蓝点是质心，数字是轮廓角度。如何复制未旋转轮廓的内容，旋转它们并复制到新图像？ 在此处输入图像描述

代码

def getContourCenter(contour):
    M = cv2.moments(contour)
    if M["m00"] != 0:
        cx = int(M['m10']/M['m00'])
        cy = int(M['m01']/M['m00'])
    else:
        return 0, 0
    return int(cx), int(cy)

def rotateContour(contour, center: tuple, angle: float):

    def cart2pol(x, y):
        theta = np.arctan2(y, x)
        rho = np.hypot(x, y)
        return theta, rho

    def pol2cart(theta, rho):
        x = rho * np.cos(theta)
        y = rho * np.sin(theta)
        return x, y

    # Translating the contour by subtracting the center with all the points
    norm = contour - [center[0], center[1]]

    # Convert the points to polar co-ordinates, add the rotation, and convert it back to Cartesian co-ordinates.
    coordinates = norm[:, 0, :]
    xs, ys = coordinates[:, 0], coordinates[:, 1]
    thetas, rhos = cart2pol(xs, ys)

    thetas = np.rad2deg(thetas)
    thetas = (thetas + angle) % 360
    thetas = np.deg2rad(thetas)

    # Convert the new polar coordinates to cartesian co-ordinates
    xs, ys = pol2cart(thetas, rhos)
    norm[:, 0, 0] = xs
    norm[:, 0, 1] = ys

    rotated = norm + [center[0], center[1]]
    rotated = rotated.astype(np.int32)

    return rotated


def straightenText(image, vis):

    # create a new mat
    mask = 0*np.ones([image.shape[0], image.shape[1], 3], dtype=np.uint8)

    # invert pixel index arrangement and dilate aggressively
    dilate = cv2.dilate(~image, ImageUtils.box(33, 1))

    # find contours
    _, contours, hierarchy = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    for contour in contours:
        [x, y, w, h] = cv2.boundingRect(contour)
        if w > h:

            # find contour angle and centers
            (x, y), (w, h), angle = cv2.minAreaRect(contour)
            cx, cy = getContourCenter(contour)

            # fix angle returned
            if w < h:
                angle = 90 + angle

            # fix contour angle
            rotatedContour = rotateContour(contour, (cx, cy), 0-angle)

            cv2.drawContours(vis, contour, -1, (0, 255, 0), 2)
            cv2.drawContours(mask, rotatedContour, -1, (255, 0, 0), 2)
            cv2.circle(vis, (cx, cy), 2, (0, 0, 255), 2, 8) # centroid
            cv2.putText(vis, str(round(angle, 2)), (cx, cy), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,0,0), 2)

标签： pythonopencvcontour

解决方案

要仅提取单个轮廓的内容，而不是其较大的边界框，您可以通过绘制填充轮廓然后将其应用于原始图像来创建蒙版。在你的情况下，你需要这样的东西：

# prepare the target image
resX,resY = image.shape[1],image.shape[0]
target = np.zeros((resY, resX , 3), dtype=np.uint8)  
target.fill(255)  # make it entirely white

# find the contours
allContours,hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

# then perform rotation, etc, per contour
for contour in allContours:
  # create empty mask
  mask = np.zeros((resY, resX , 1), dtype=np.uint8)  

  # draw the contour filled into the mask
  cv2.drawContours(mask, [contour], -1, (255),  thickness=cv2.FILLED) 
  
  # copy the relevant part into a new image 
  # (you might want to use bounding box here for more efficiency)
  single = cv2.bitwise_and(image, image, mask=mask)   

  # then apply your rotation operations both on the mask and the result
  single = doContourSpecificOperation(single)
  mask = doContourSpecificOperation(mask)

  # then, put the result into your target image (which was originally white)
  target = cv2.bitwise_and(target, single, mask=mask)

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