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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
Python上的OpenCv开发,在linux上的基本环境搭建流程。
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本文章博客地址:https://hpzwl.blog.csdn.net/article/details/140435870
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上一篇:《OpenCV开发笔记(七十七):相机标定(二):通过棋盘标定计算相机内参矩阵矫正畸变摄像头图像》
下一篇:《OpenCV开发笔记(七十九):基于Stitcher类实现全景图片拼接》
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()
测试结果
上一篇:《OpenCV开发笔记(七十七):相机标定(二):通过棋盘标定计算相机内参矩阵矫正畸变摄像头图像》
下一篇:《OpenCV开发笔记(七十九):基于Stitcher类实现全景图片拼接》
本文章博客地址:https://hpzwl.blog.csdn.net/article/details/140435870
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