python opencv实现图像矫正功能_python

import cv2
import numpy as np

img = cv2.imread('/home/pzs/图片/1.jpeg')

result3 = img.copy()

img = cv2.GaussianBlur(img,(3,3),0)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

edges = cv2.Canny(gray,50,150,apertureSize = 3)
cv2.imwrite("canny.jpg", edges)

src = np.float32([[207, 151], [517, 285], [17, 601], [343, 731]])
dst = np.float32([[0, 0], [337, 0], [0, 488], [337, 488]])
m = cv2.getPerspectiveTransform(src, dst)
result = cv2.warpPerspective(result3, m, (337, 488))
cv2.imshow("result", result)
cv2.waitKey(0)

import cv2
import imutils

img = cv2.imread('1.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
dilate = cv2.dilate(blurred, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)))
edged = cv2.Canny(dilate, 30, 120, 3)            # 边缘检测

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # 轮廓检测
cnts = cnts[0] if imutils.is_cv2() else cnts[1]  # 判断是opencv2还是opencv3
docCnt = None

if len(cnts) > 0:
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True) # 根据轮廓面积从大到小排序
    for c in cnts:
        peri = cv2.arcLength(c, True)                                       # 计算轮廓周长
        approx = cv2.approxPolyDP(c, 0.02*peri, True)           # 轮廓多边形拟合
        # 轮廓为4个点表示找到纸张
        if len(approx) == 4:
            docCnt = approx
            break

for peak in docCnt:
    peak = peak[0]
    cv2.circle(img, tuple(peak), 10, (255, 0, 0))

cv2.imshow('img', img)
cv2.waitKey(0)