OpenCV开发笔记（七十八）：在ubuntu上搭建opencv+python开发环境以及匹配识别Demo_业界新闻

发布时间:2024-07-19 01:26

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前言

Python上的OpenCv开发，在linux上的基本环境搭建流程。

安装python

以python2.7为开发版本。

sudo apt-get install python2.7 sudo apt-get install python2.7-dev

安装OpenCV

多种方式，先选择最简单的方式。

sudo apt-get install python-opencv

打开摄像头

测试Demo

import cv2 import numpy cap = cv2.VideoCapture(0) while 1:   ret, frame = cap.read()   cv2.imshow("capture", frame)   if cv2.waitKey(100) & 0xff == ord('q'):     break cap.release() cv2.destroyAllWindows()

测试结果

模板匹配

测试Demo

import cv2 import numpy  # read template image template = cv2.imread("src.png") #cv2.imshow("template", template);  # read target image target = cv2.imread("dst.png") #cv2.imshow("target", target)  # get tempalte's width and height tHeight, tWidth = template.shape[:2] print tHeight, tWidth  # matches result = cv2.matchTemplate(target, template, cv2.TM_SQDIFF_NORMED)  # normalize cv2.normalize(result, result, 0, 1, cv2.NORM_MINMAX, -1)  minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(result)  strminVal = str(minVal) print strminVal  cv2.rectangle(target, minLoc, (minLoc[0] + tWidth, minLoc[1] + tHeight), (0,0,255), 2)  cv2.imshow("result", target)  cv2.waitKey()  cv2.destroyAllWindows()

测试结果

Flann特征点匹配

版本回退

在opencv3.4.x大版本后，4.x系列的sift被申请了专利，无法使用了，flann需要使用到

sift = cv2.xfeatures2d.SIFT_create()

所以需要回退版本。

sudo apt-get remove python-opencv sudo pip install opencv-python==3.4.2.16

安装模块库matplotlib

python -m pip install matplotlib sudo apt-get install python-tk pip install opencv-contrib-python==3.4.2.16

测试Demo

# FLANN based Matcher import numpy as np import cv2 from matplotlib import pyplot as plt   #min match count is 10 MIN_MATCH_COUNT = 10  # queryImage template = cv2.imread('src.png',0)  # trainImage target = cv2.imread('dst.png',0)  # initiate SIFT detector sift = cv2.xfeatures2d.SIFT_create() # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute(template,None) kp2, des2 = sift.detectAndCompute(target,None) # create FLANN match FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks = 50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1,des2,k=2) # store all the good matches as per Lowe's ratio test. good = [] # lose < 0.7 for m,n in matches:     if m.distance < 0.7*n.distance:         good.append(m) if len(good)>MIN_MATCH_COUNT:     # get key     src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)     dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)     # cal mat and mask     M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)     matchesMask = mask.ravel().tolist()     h,w = template.shape     # convert 4 corner     pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)     dst = cv2.perspectiveTransform(pts,M)     cv2.polylines(target,[np.int32(dst)],True,0,2, cv2.LINE_AA) else:     print( "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT))     matchesMask = None draw_params = dict(matchColor=(0,255,0),                     singlePointColor=None,                    matchesMask=matchesMask,                     flags=2)  result = cv2.drawMatches(template, kp1, target, kp2, good, None, **draw_params) cv2.imshow("dst", result) cv2.imshow("dst2", target) cv2.waitKey()