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

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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()

在这里插入图片描述

测试结果

在这里插入图片描述

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 strminValcv2.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

在这里插入图片描述

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 keysrc_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 maskM, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)matchesMask = mask.ravel().tolist()h,w = template.shape# convert 4 cornerpts = 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()