如何对轮廓进行分组并绘制单个边界矩形

Question

我需要分组 contours 并绘制一个包含所有轮廓的单个 bounding rectangle，像这样

from matplotlib import pyplot as plt
import cv2 as cv

img = cv.imread('shapes1.png', 0)
imgRGB = cv.cvtColor(img.copy(), cv.COLOR_GRAY2RGB)

_, ctrs, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

boxes = []
for ctr in ctrs:
    x, y, w, h = cv.boundingRect(ctr)
    boxes.append([x, y, w, h])

for box in boxes:
    top_left     = (box[0], box[1])
    bottom_right = (box[0] + box[2], box[1] + box[3])
    cv.rectangle(imgRGB, top_left, bottom_right, (0,255,0), 2)

fig = plt.figure(figsize = (10, 10))
ax  = fig.add_subplot(111)
ax.imshow(imgRGB, cmap='gray')

有什么直接的方法可以做到这一点，而不是以编程方式合并所有边界矩形

Answer 1

您可以通过迭代所有轮廓并从每个轮廓创建边界框然后计算 minX、minY、maxX、maxY 来实现。

int minX=MAX_INTEGER, minY=MAX_INTEGER, maxX=0, maxY=0;

for each contour in contours:
    Rect rect = boundRecFromContour(contour)
    if rect.x < minX:
        minx = rect.x
    if rect.y < minY:
        minY = rect.y
    if rect.x+rect.width > maxX:
        maxX = rect.x+rect.width
    if rect.y+rect.height > maxY:
        maxY = rect.y+rect.height
Rect groupRect = (minX, minY, minX+maxX, minY+maxY)

现在您可以绘制 groupRect。

另外，你也可以使用技术来解决这个问题：

cv::Mat idx;
cv::findNonZero(binaryImg, idx);
// find min max points
double maxX = 0, minX = MAX_INTEGER;
double maxY = 0, minY = MAX_INTEGER;
for (int i=0; i<idx.rows; ++i) {
    cv::Point pnt = idx.at<cv::Point>(i);
    if (pnt.x > maxX) {
        maxX = pnt.x;
    }
    if (pnt.x < minX) {
        minX = pnt.x;
    }
    if (pnt.y > maxY) {
        maxY = pnt.y;
    }
    if (pnt.y < minY) {
        minY = pnt.y;
    }
}
Rect groupRect = cv::Rect(cv::Point(int(minX), int(minY)), cv::Point(int(maxX), int(maxY)));

Answer 2

如果您不介意使用numpy，您可以简单地使用concatenate function from there, see the following code. Attention: I use OpenCV 4.0.0 where the order of the return values of findContours，不同的是

import cv2
import numpy as np

# Input image
input = cv2.imread('images/kchZb.png', cv2.IMREAD_GRAYSCALE)

# Modify input image to extract "original" image
_, input = cv2.threshold(input[10:400, 40:580], 224, 255, cv2.THRESH_BINARY)

# Find contours
cnts, _ = cv2.findContours(input, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Concatenate all contours
cnts = np.concatenate(cnts)

# Determine and draw bounding rectangle
x, y, w, h = cv2.boundingRect(cnts)
cv2.rectangle(input, (x, y), (x + w - 1, y + h - 1), 255, 2)

# Output image
cv2.imwrite('images/output.png', input)
cv2.imshow('Input', input)
cv2.waitKey(0)

免责声明：一般来说，我是 Python 的新手，特别是 OpenCV 的 Python API（C++ 为赢）。非常欢迎提出意见、改进和强调 Python 不可行的问题！

Answer 3

@HansHirse 解决方案非常优雅。这是另一种解决方案，使用包围矩形。这可以直接使用 C++ 中 OpenCV 中的 Rect class 和 OpenCV Wrapper library.

或(|)运算符用于给出最小外接矩形。

import cv2 as cv
import opencv_wrapper as cvw

img = cv.imread("shapes1.png", 0)
imgRGB = cvw.gray2bgr(img)

contours = cvw.find_external_contours(img)

enclosing_rect = contours[0].bounding_rect | contours[1].bounding_rect
enclosing_rect = enclosing_rect | contours[2].bounding_rect

cvw.rectangle(imgRGB, enclosing_rect, cvw.Color.GREEN)

cv.imshow("Image", imgRGB)
cvw.wait_key(0)

披露：我是 OpenCV Wrapper 的作者

如何对轮廓进行分组并绘制单个边界矩形

How to group contours and draw a single bounding rectangle

python

python-3.x

opencv

opencv-contour