python+opencv学习（二）--直线拟合、轮廓检测

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


# 直线拟合
def zhixian():
    img = cv2.imread("go3.jpg")
    pary = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 灰度图

    # findContours接受的参数为二值图，即黑白的（不是灰度图），所以读取的图像要先转成灰度的，再转成二值图
    ret, thresh = cv2.threshold(pary, 127, 255, cv2.THRESH_BINARY)  # 转化黑白图
    # python3为三个参数，加上aa ,返回的aa为你处理的图像，contours轮廓的点集，hierarchy各层轮廓的索引
    aa, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    cnt = contours[0]
    M = cv2.moments(cnt)
    print(M)

    rows, cols = img.shape[:2]
    [vx, vy, x, y] = cv2.fitLine(cnt, cv2.DIST_L2, 0, 0.01, 0.01)
    lefty = int((-x * vy / vx) + y)
    righty = int(((cols - x) * vy / vx) + y)
    img = cv2.line(pary, (cols - 1, righty), (0, lefty), (0, 255, 0), 2)
    cv2.imshow("test", pary)
    cv2.waitKey(0)


# 轮廓检测
def lunkuo():
    img = cv2.imread("go.jpg")
    pary = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    edges = cv2.Canny(pary, 100, 200)
    cv2.imshow("test", edges)
    cv2.waitKey(0)
    cv2.imwrite("a.jpg",edges)


def shibie():
    # 设置边缘检测参数
    minval = 200
    maxval = 400
    img = cv2.imread("go.jpg")
    pary = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    edges = cv2.Canny(pary, minval, maxval)

    cv2.imshow("test", edges)
    cv2.waitKey(0)


shibie()