cv2.drawContours() - 取消填充字符内的圆圈（Python，OpenCV）

Question

按照@Silencer 的建议，我使用他发布的代码在图像中的数字周围绘制轮廓。在某些时候，使用像 0,6,8,9 这样的数字，我看到它们的内部轮廓（圆圈）也被填充了。我怎样才能防止这种情况发生？是否为 cv2.drawContours() 设置了 min/max 作用区域以便我可以排除内部区域？

我尝试传递 cv2.RETR_EXTERNAL 但使用此参数时仅考虑整个外部区域。

代码是这样的（再次感谢 Silencer。我已经搜索了好几个月了..）：

import numpy as np
import cv2

im = cv2.imread('imgs\2.png')
imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

#contours.sort(key=lambda x: int(x.split('.')[0]))

for i, cnts in enumerate(contours):
    ## this contour is a 3D numpy array
    cnt = contours[i]
    res = cv2.drawContours(im, [cnt], 0, (255, 0, 0), 1)
    cv2.imwrite("contours.png", res)
    '''
    ## Method 1: crop the region
    x,y,w,h = cv2.boundingRect(cnt)
    croped = res[y:y+h, x:x+w]
    cv2.imwrite("cnts\croped{}.png".format(i), croped)
    '''
    ## Method 2: draw on blank
    # get the 0-indexed coords
    offset = cnt.min(axis=0)
    cnt = cnt - cnt.min(axis=0)
    max_xy = cnt.max(axis=0) + 1
    w, h = max_xy[0][0], max_xy[0][1]
    # draw on blank
    canvas = np.ones((h, w, 3), np.uint8) * 255
    cv2.drawContours(canvas, [cnt], -1, (0, 0, 0), -1)

    #if h > 15 and w < 60:
    cv2.imwrite("cnts\canvas{}.png".format(i), canvas)

我正在处理的主图像..

谢谢

更新

我在下面实施了 Fiver 答案，这是结果：

import cv2
import numpy as np

img = cv2.imread('img.png')
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_v = img_hsv[:, :, 2]

ret, thresh = cv2.threshold(~img_v, 127, 255, 0)
image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for i, c in enumerate(contours):
    tmp_img = np.zeros(img_v.shape, dtype=np.uint8)
    res = cv2.drawContours(tmp_img, [c], -1, 255, cv2.FILLED)

    tmp_img = np.bitwise_and(tmp_img, ~img_v)

    ret, inverted = cv2.threshold(tmp_img, 127, 255, cv2.THRESH_BINARY_INV)

    cnt = contours[i]

    x, y, w, h = cv2.boundingRect(cnt)
    cropped = inverted[y:y + h, x:x + w]

    cv2.imwrite("roi{}.png".format(i), cropped)

Answer 1

要绘制 char 不填充封闭的内部区域：

find the contours on the threshed binary image with hierarchy.

find the outer contours that don't have inner objects (by flag hierarchyi).

for each outer contour:

3.1 fill it(maybe need check whether needed);

3.2 then iterate in it's inner children contours, fill then with other color(such as inversed color).

combine with the crop code, crop them.

maybe you need sort them, resplit them, normalize them.

maybe, now you can do ocr with the trained model.

找到轮廓，重新填充内部封闭区域。

结合这个答案，多做几步，说不定你可以得到这个或者更好的：

refill内部封闭区域的核心代码如下：

#!/usr/bin/python3
# 2018.01.14 09:48:15 CST
# 2018.01.15 17:56:32 CST
# 2018.01.15 20:52:42 CST

import numpy as np
import cv2

img = cv2.imread('img02.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

## Threshold 
ret, threshed = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)

## FindContours
cnts, hiers = cv2.findContours(threshed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]

canvas = np.zeros_like(img)
n = len(cnts)
hiers = hiers[0]

for i in range(n):
    if hiers[i][3] != -1:
        ## If is inside, the continue 
        continue
    ## draw 
    cv2.drawContours(canvas, cnts, i,  (0,255,0), -1, cv2.LINE_AA)

    ## Find all inner contours and draw 
    ch = hiers[i][2]
    while ch!=-1:
        print(" {:02} {}".format(ch, hiers[ch]))
        cv2.drawContours(canvas, cnts, ch, (255,0,255), -1, cv2.LINE_AA)
        ch = hiers[ch][0]

cv2.imwrite("001_res.png", canvas)

运行此代码与此图片：

您将获得：

当然，这是针对两个层次结构的。我没有测试超过两个。有需要的可以自行测试。

更新：

注意在不同的 OpenCV 中，cv2.findContours return 不同的值。为了保持代码可执行，我们可以只获取最后两个 returned 值使用：cnts, hiers = cv2.findContours(...)[-2:]

在 OpenCV 3.4 中：

在 OpenCV 4.0 中：

Answer 2

完整代码...

这不会对图像进行排序。

import numpy as np
import cv2

im = cv2.imread('imgs\1.png')
imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

## Threshold
ret, threshed = cv2.threshold(imgray, 127, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

## FindContours
image, cnts, hiers = cv2.findContours(threshed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

canvas = np.zeros_like(im)
n = len(cnts)
hiers = hiers[0]

for i, imgs in enumerate(cnts):

    cnt = cnts[i]
    res = cv2.drawContours(im, [cnt], 0, (0, 0, 0), -1)

    x, y, w, h = cv2.boundingRect(cnt)
    croped = res[y:y + h, x:x + w]

    if h > 10:
        cv2.imwrite("out\croped{}.png".format(i), croped)
        cv2.imshow('i', croped)
        cv2.waitKey(0)

for i, value in enumerate(cnts):

    ## this contour is a 3D numpy array
    cnt = cnts[i]
    res = cv2.drawContours(im, [cnt], 0, (0, 0, 0), -1)
    # cv2.imwrite("out\contours{}.png".format(i), res)

    ## Find all inner contours and draw
    ch = hiers[i][2]
    while ch != -1:
        print(" {:02} {}".format(ch, hiers[ch]))
        res1 = cv2.drawContours(im, cnts, ch, (255, 255, 255), -1)
        ch = hiers[ch][0]

        x, y, w, h = cv2.boundingRect(cnt)
        croped = res[y:y + h, x:x + w]

        if h > 10:
            cv2.imwrite("out\croped{}.png".format(i), croped)

接受任何更正。

Answer 3

这将明确地完成工作...

import cv2
import os
import numpy as np

img = cv2.imread("image.png")

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

retval, thresholded = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

medianFiltered = cv2.medianBlur(thresholded, 3)

_, contours, hierarchy = cv2.findContours(medianFiltered, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

contour_list = []
for contour in contours:
    area = cv2.contourArea(contour)
    if area > 80:
        contour_list.append(contour)

numbers = cv2.drawContours(img, contour_list, -1, (0, 0, 0), 2)

cv2.imshow('i', numbers)
cv2.waitKey(0)

sorted_ctrs = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])

for i, cnts in enumerate(contours):

    cnt = contours[i]

    x, y, w, h = cv2.boundingRect(cnt)
    croped = numbers[y:y + h, x:x + w]

    h, w = croped.shape[:2]
    print(h, w)

    if h > 15:
        cv2.imwrite("croped{}.png".format(i), croped)

Answer 4

由于您已经从阈值步骤中获得了蒙版，因此您还可以将其用于 bitwise_and 绘制的轮廓：

import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread('drawn_chars.png')
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_v = img_hsv[:, :, 2]

ret, thresh = cv2.threshold(~img_v, 127, 255, 0)
image, contours, hierarchy = cv2.findContours(
    thresh, 
    cv2.RETR_EXTERNAL, 
    cv2.CHAIN_APPROX_SIMPLE
)

for c in contours:
    tmp_img = np.zeros(img_v.shape, dtype=np.uint8)
    cv2.drawContours(tmp_img, [c], -1, 255, cv2.FILLED)

    tmp_img = np.bitwise_and(tmp_img, ~img_v)

    plt.figure(figsize=(16, 2))
    plt.imshow(tmp_img, cmap='gray')

我已经反转了图像，所以轮廓是白色的，我省略了裁剪，因为你已经解决了这个问题。这是 "O" 个字符之一的结果：

Answer 5

这在概念上类似于 Fivers 的回答，只是 bitwise_and 发生在 for 循环之外，并且在性能方面可能更好。源代码在 C++ 中，供那些寻找此问题的 C++ 答案的人使用。

int thWin = 3;
int thOffset = 1;
cv::adaptiveThreshold(image, th, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY_INV, thWin, thOffset);

int minMoveCharCtrArea = 140;
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(th.clone(), contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);
cv::Mat filtImg = cv::Mat::zeros(img.rows, img.cols, CV_8UC1 );

for (int i = 0; i< contours.size(); ++i) {
    int ctrArea = cv::contourArea(contours[i]);
    if (ctrArea > minMoveCharCtrArea) {
        cv::drawContours(filtImg, contours, i, 255, -1);
    }
}
cv::bitwise_and(th, filtImg, filtImg);

请记住在将源图像参数传递给 findContours 时克隆图像（对于 python 它应该是副本），因为 findContours 会修改原始图像。我认为更高版本的 opencv（可能是 opencv3 +）不需要克隆。

cv2.drawContours() - 取消填充字符内的圆圈（Python，OpenCV）

cv2.drawContours() - unfill circles inside characters (Python, OpenCV)

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