OpenCV Python강좌 - 컨투어(Contour) 검출 및 특성 사용하기

컨투어 검출하는 방법과 컨투어 특성을 사용하는 방법을 다룹니다. 

사용하는 OpenCV 버전에 따라 findContours 함수의 사용 방법이 다음처럼 차이가 있습니다. 

OpenCV 4.x

contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)

OpenCV 3.x

_, contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)

Contour Features

영역 크기

import cv2 as cv img_color = cv.imread('test.png') img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY) ret, img_binary = cv.threshold(img_gray, 127, 255, 0) contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE) for cnt in contours:    cv.drawContours(img_color, [cnt], 0, (255, 0, 0), 3)  # blue cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    area = cv.contourArea(cnt)    print(area) cv.imshow("result", img_color) cv.waitKey(0)

근사화

 

import cv2 as cv import numpy as np img_color = cv.imread('square.png') img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY) ret, img_binary = cv.threshold(img_gray, 127, 255, 0) contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE) for cnt in contours:    cv.drawContours(img_color, [cnt], 0, (255, 0, 0), 3)  # blue cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    epsilon = 0.02 * cv.arcLength(cnt, True)    approx = cv.approxPolyDP(cnt, epsilon, True)    print( len(approx))    cv.drawContours(img_color,[approx],0,(0,255,255),5) cv.imshow("result", img_color) cv.waitKey(0)

무게 중심

import cv2 as cv img_color = cv.imread('test.png') img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY) ret, img_binary = cv.threshold(img_gray, 127, 255, 0) contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE) for cnt in contours:    cv.drawContours(img_color, [cnt], 0, (255, 0, 0), 3)  # blue cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    M = cv.moments(cnt)    cx = int(M['m10']/M['m00'])    cy = int(M['m01']/M['m00'])    cv.circle(img_color, (cx, cy), 10, (0,0,255), -1) cv.imshow("result", img_color) cv.waitKey(0)

..

..

경계 사각형 (Bounding Rectangle)

import cv2 as cv import numpy as np img_color = cv.imread('test.png') img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY) ret, img_binary = cv.threshold(img_gray, 127, 255, 0) contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE) for cnt in contours:    cv.drawContours(img_color, [cnt], 0, (255, 0, 0), 3)  # blue cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    x, y, w, h = cv.boundingRect(cnt)    cv.rectangle(img_color, (x, y), (x + w, y + h), (0, 255, 0), 2) cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    rect = cv.minAreaRect(cnt)    box = cv.boxPoints(rect)    box = np.int0(box)    cv.drawContours(img_color,[box],0,(0,0,255),2) cv.imshow("result", img_color) cv.waitKey(0)

Convex Hull

import cv2 as cv import numpy as np img_color = cv.imread('hand.png') img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY) ret, img_binary = cv.threshold(img_gray, 127, 255, 0) contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE) for cnt in contours:    cv.drawContours(img_color, [cnt], 0, (255, 0, 0), 3)  # blue cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    hull = cv.convexHull(cnt)    cv.drawContours(img_color, [hull], 0, (255, 0, 255), 5) cv.imshow("result", img_color) cv.waitKey(0)

Convexity Defects

import cv2 as cv import numpy as np img_color = cv.imread('hand.png') img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY) ret, img_binary = cv.threshold(img_gray, 127, 255, 0) contours, hierarchy = cv.findContours(img_binary, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE) for cnt in contours:    cv.drawContours(img_color, [cnt], 0, (255, 0, 0), 3)  # blue cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    hull = cv.convexHull(cnt)    cv.drawContours(img_color, [hull], 0, (255, 0, 255), 5) cv.imshow("result", img_color) cv.waitKey(0) for cnt in contours:    hull = cv.convexHull(cnt, returnPoints = False)    defects = cv.convexityDefects(cnt, hull)    for i in range(defects.shape[0]):        s,e,f,d = defects[i,0]        start = tuple(cnt[s][0])        end = tuple(cnt[e][0])        far = tuple(cnt[f][0])        print(d)        if d > 500:            cv.line(img_color, start, end, [0, 255, 0], 5)            cv.circle(img_color, far, 5, [0,0,255], -1)        cv.imshow("result", img_color)        cv.waitKey(0)

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