轮廓旋转后OpenCV将不规则轮廓区域复制到另一张图像

Question

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. 大幅扩张并删除非文本行
3. 在极地空间求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。 Result here

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？

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)

Answer 1

这是一种方法，这是我能想到的在 Python/OpenCV 中最简单的方法，尽管速度可能不是最优的。

• 为所需的输出创建一个白色的空白图像。 （所以我们在白色背景上有黑色文本，以防您需要进行 OCR）

• 在输入中获取轮廓的旋转边界矩形。

• 在输出中获取轮廓的正常边界矩形。

• 获取每个边界框的 4 个角。

• 计算两组 4 个角之间的仿射变换矩阵 分。

• 将（整个）输入图像变形为相同大小（非最佳）。

• 使用输出边界框尺寸和左上角 numpy 切片将扭曲图像中的区域转移到相同的区域 白色输出图像中的区域。

• 使用生成的图像代替每个文本轮廓重复 原始的白色图像作为新的目标图像。

所以这里有一个模拟来告诉你如何做。

源文字图片：

带有红色旋转矩形的源文本图像：

白色目标图像中所需的边界矩形：

文本转换为白色图像到所需的矩形区域：

代码：

import cv2
import numpy as np

# Read source text image.
src = cv2.imread('text_on_white.png')
hs, ws, cs = src.shape

# Read same text image with red rotated bounding box drawn.
src2 = cv2.imread('text2_on_white.png')

# Read white image showing desired output bounding box.
src2 = cv2.imread('text2_on_white.png')

# create white destination image
dst = np.full((hs,ws,cs), (255,255,255), dtype=np.uint8)

# define coordinates of bounding box in src
src_pts = np.float32([[51,123], [298,102], [300,135], [54,157]])

# size and placement of text in dst is (i.e. bounding box):
xd = 50
yd = 200
wd = 249
hd = 123
dst_pts = np.float32([[50,200], [298,200], [298,234], [50,234]])

# get rigid affine transform (no skew)
# use estimateRigidTransform rather than getAffineTransform so can use all 4 points
matrix = cv2.estimateRigidTransform(src_pts, dst_pts, 0)

# warp the source image
src_warped = cv2.warpAffine(src, matrix, (ws,hs), cv2.INTER_AREA, borderValue=(255,255,255))

# do numpy slicing on warped source and place in white destination
dst[yd:yd+hd, xd:xd+wd] = src_warped[yd:yd+hd, xd:xd+wd]

# show results
cv2.imshow('SRC', src)
cv2.imshow('SRC2', src2)
cv2.imshow('SRC_WARPED', src_warped)
cv2.imshow('DST', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

# save results
cv2.imwrite('text_on_white_transferred.png', dst)

Answer 2

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)