Question

我正在处理文本变形/旋转的图像。我需要先将这些文本Blob旋转回水平位置（0度），然后才能对它们运行OCR。我设法解决了旋转问题，但是现在我需要找到一种将原始轮廓的内容复制到旋转矩阵的方法。

以下是我提取并解决轮换问题的一些操作：

找到轮廓
大量扩张并删除非文本行
找到轮廓角并在极空间中进行角度校正。

我曾尝试使用仿射变换来旋转矩形文本Blob，但由于某些文本Blob不规则，最终导致裁剪出一些文本。 Result here

轮廓中的蓝点是质心，数字是轮廓角。如何复制未旋转轮廓的内容，将其旋转并复制到新图像？

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，这是我认为可以在Python / OpenCV中完成的最简单的方法，尽管在速度上可能并非最佳。

为所需的输出创建白色的空白图像。（因此，如果您需要进行OCR，我们会在白色背景上显示黑色文字）
在输入中获取轮廓的旋转边界矩形。
在输出中获取轮廓的法线边界矩形。
获取每个边界框的四个角。
计算两组4个角之间的仿射变换矩阵点。
将（整个）输入图像扭曲为相同大小（非最佳）。
将输出边界框尺寸和左上角与 numpy切片将扭曲图像中的区域转移到相同白色输出图像中的区域。
使用结果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。

这是一个模拟，向您展示如何操作。

源文本图像：

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：

将文本转换为白色图像并转换为所需的矩形区域：

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，而不提取其较大的边界框，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。在您的情况下，您需要这样的东西：

# 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)

OpenCV在轮廓旋转后将不规则轮廓区域复制到另一个图像

2 个答案: