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Fu Xianjun. All Rights Reserved

前言

一、OpenCV是什么？

OpenCV是一个用于图像处理、分析、机器视觉方面的开源函数库.

二、使用步骤

1.引入库

代码如下：

import cv2import numpy as np

2.读入数据

代码如下：

1.预处理、轮廓检测

# 正确答案ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}def cv_show(name,img):        cv2.imshow(name, img)        cv2.waitKey(0)        cv2.destroyAllWindows()  # 读取输入image = cv2.imread("test_01.png")contours_img = image.copy()gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)blurred = cv2.GaussianBlur(gray, (5, 5), 0)cv_show('blurred',blurred)edged = cv2.Canny(blurred, 75, 200)cv_show('edged',edged)# 轮廓检测cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,\                        cv2.CHAIN_APPROX_SIMPLE)[0]cv2.drawContours(contours_img,cnts,-1,(0,0,255),3) cv_show('contours_img',contours_img)

2.轮廓排序，透视变换

def order_points(pts):    # 一共4个坐标点    rect = np.zeros((4, 2), dtype = "float32")    # 按顺序找到对应坐标0123分别是 左上，右上，右下，左下    # 计算左上，右下    s = pts.sum(axis = 1)    rect[0] = pts[np.argmin(s)]    rect[2] = pts[np.argmax(s)]    # 计算右上和左下    diff = np.diff(pts, axis = 1)    rect[1] = pts[np.argmin(diff)]    rect[3] = pts[np.argmax(diff)]    return rectdef four_point_transform(image, pts):    # 获取输入坐标点    rect = order_points(pts)    (tl, tr, br, bl) = rect    # 计算输入的w和h值    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))    maxWidth = max(int(widthA), int(widthB))    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))    maxHeight = max(int(heightA), int(heightB))    # 变换后对应坐标位置    dst = np.array([        [0, 0],        [maxWidth - 1, 0],        [maxWidth - 1, maxHeight - 1],        [0, maxHeight - 1]], dtype = "float32")    # 计算变换矩阵    M = cv2.getPerspectiveTransform(rect, dst)    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))    # 返回变换后结果    return warped

# 确保检测到了docCnt = Noneif len(cnts) > 0:    # 根据轮廓大小进行排序    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)    # 遍历每一个轮廓    for c in cnts:        # 近似        peri = cv2.arcLength(c, True)        approx = cv2.approxPolyDP(c, 0.02 * peri, True)        # 准备做透视变换        if len(approx) == 4:            docCnt = approx            break# 执行透视变换warped = four_point_transform(gray, docCnt.reshape(4, 2))cv_show('warped',warped)

3.寻找圆圈轮廓

def sort_contours(cnts, method="left-to-right"):    reverse = False    i = 0    if method == "right-to-left" or method == "bottom-to-top":        reverse = True    if method == "top-to-bottom" or method == "bottom-to-top":        i = 1    boundingBoxes = [cv2.boundingRect(c) for c in cnts]    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),                                        key=lambda b: b[1][i], reverse=reverse))    return cnts, boundingBoxes

# Otsu's 阈值处理thresh = cv2.threshold(warped, 0, 255,    cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] cv_show('thresh',thresh)thresh_Contours = thresh.copy()# 找到每一个圆圈轮廓cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,    cv2.CHAIN_APPROX_SIMPLE)[0]cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) cv_show('thresh_Contours',thresh_Contours)questionCnts = []

4.输出每个轮廓，对比答案

总结

以上就是今天要讲的内容，本文仅仅简单介绍了答题卡识别的使用，而python提供了大量能使我们快速便捷地处理数据的函数和方法。

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