绘制轮廓 cv2.findContours函数及参数解释 |
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cv2 绘制轮廓
cv2.findContours()注意事项mode参数method参数offset:（可选参数）返回值
cv2.findContours()
def findContours(image, mode, method, contours=None, hierarchy=None, offset=None):
# real signature unknown; restored from __doc__
"""
findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> contours, hierarchy
. @brief Finds contours in a binary image.
.
. The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
. are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
. OpenCV sample directory.
. @note Since opencv 3.2 source image is not modified by this function.
.
. @param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
. pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
. #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
. If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
. @param contours Detected contours. Each contour is stored as a vector of points (e.g.
. std::vector).
. @param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has
. as many elements as the number of contours. For each i-th contour contours[i], the elements
. hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
. in contours of the next and previous contours at the same hierarchical level, the first child
. contour and the parent contour, respectively. If for the contour i there are no next, previous,
. parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
. @param mode Contour retrieval mode, see #RetrievalModes
. @param method Contour approximation method, see #ContourApproximationModes
. @param offset Optional offset by which every contour point is shifted. This is useful if the
. contours are extracted from the image ROI and then they should be analyzed in the whole image
. context.
"""
pass
注意事项
1.输入为二值图像，黑色为背景，白色为目标 2.该函数会修改原图像，因此若想保留原图像在，则需拷贝一份，在拷贝图里修改。 mode参数 参数名称功能cv2.RETR_EXTERNAL只检测外轮廓cv2.RETR_LIST检测的轮廓不建立等级关系，都是同级cv2.RETR_CCOMP建立两个等级的轮廓，上面一层为外边界，里面一层为内孔的边界信息cv2.RETR_TREE建立一个等级树结构的轮廓 method参数 参数名称功能cv2.CHAIN_APPROX_NONE存储所有边界点cv2.CHAIN_APPROX_SIMPLE压缩垂直、水平、对角方向，只保留端点cv2.CHAIN_APPROX_TX89_L1使用teh-Chini近似算法cv2.CHAIN_APPROX_TC89_KCOS使用teh-Chini近似算法 offset:（可选参数）offset:轮廓点的偏移量，格式为tuple,如（-10，10）表示轮廓点沿X负方向偏移10个像素点，沿Y正方向偏移10个像素点 返回值contours:轮廓点。列表格式，每一个元素为一个3维数组（其形状为（n,1,2），其中n表示轮廓点个数，2表示像素点坐标）,表示一个轮廓 hierarchy:轮廓间的层次关系,为三维数组，形状为（1,n,4），其中n表示轮廓总个数，4指的是用4个数表示各轮廓间的相互关系。第一个数表示同级轮廓的下一个轮廓编号，第二个数表示同级轮廓的上一个轮廓的编号，第三个数表示该轮廓下一级轮廓的编号，第四个数表示该轮廓的上一级轮廓的编号。 # ## -*- coding: utf-8 -*- import cv2 import imutils import numpy as np def RGB_GRAY(img): img = cv2.resize(img, (640, 480)) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) gray = cv2.bilateralFilter(gray, 13, 15, 15) return img, gray def edge(img): # cv2.Canny（source_image，thresholdValue 1，thresholdValue 2） edged = cv2.Canny(img, 30, 200) cv2.imshow('img', edged) cv2.waitKey(0) contours = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = imutils.grab_contours(contours) contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10] return contours def solve(img): img, gray = RGB_GRAY(img) contours = edge(gray) if __name__ == '__main__': img_path = './22222.jpg' img = cv2.imread(img_path) solve(img) |
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