opencv face recognition python

For objects similar to faces, you may need not less than 6,000 classifiers, each of which requires a successful match (and, of course, a fault-tolerant rate) to detect a person's face. But there is a problem: for face recognition, the algorithm starts from the upper left corner to compute a block of data, and keeps asking "Is this a

The code address is as follows:Http://www.demodashi.com/demo/13071.html Objective:OpenCV is an open source cross-platform computer Vision library that provides interfaces to languages such as Python, and implements many common algorithms for image processing and computer vision.The Harr classifier based on the Viola-jones target detection framework is built into the OPENCV and only needs to be loaded i

Adjust the object recognition parameters of the AdaBoost Cascade classifier based on the Haar feature 1. Object recognition problem of AdaBoost Cascade classifier based on Haar featurePaul A. Viola and Michael J. Jones published in 2001 the article "Fast object detection using simple features to improve cascade detectors." At the same time, many bloggers in csdn from 07 to 13 have made a lot of work on the

All right, guys, I'm back. Say I drag more not to write the article can come over with your little blasted to whack my chest ....So today we are going to use python+opencv+face++ for face verification and face unlocking. The same amount of code, you can use the code in some

, limit2, color): row_num, Col_num= Card_img_hsv.shape[:2] XL=Col_num XR=0 YH=0 yl=row_num Row_num_limit= 21Col_num_limit= Col_num * 0.8ifColor! ="Green" ElseCol_num * 0.5#Green with gradient forIinchRange (row_num): Count=0 forJinchRange (col_num): H=Card_img_hsv.item (i, J, 0) S= Card_img_hsv.item (I, J, 1) V= Card_img_hsv.item (I, J, 2) ifLimit1 and andV:count+ = 1ifCount >Col_num_limit:ifYL >I:yl=IifYH I:yh=I forJinchRange (col_num): Count=0 forIinchRange (row_num): H=Card_im

],k=2) X_matches=[1]*10X_matches[0]= Bf.knnmatch (des,x_des[0],k=2) x_matches[1]= Bf.knnmatch (des,x_des[1],k=2) x_matches[2]= Bf.knnmatch (des,x_des[2],k=2) x_matches[3]= Bf.knnmatch (des,x_des[3],k=2) x_matches[4]= Bf.knnmatch (des,x_des[4],k=2) T_matches=[1]*10T_matches[0]= Bf.knnmatch (des,t_des[0],k=2) t_matches[1]= Bf.knnmatch (des,t_des[1],k=2) t_matches[2]= Bf.knnmatch (des,t_des[2],k=2) t_matches[3]= Bf.knnmatch (des,t_des[3],k=2) t_matches[4]= Bf.knnmatch (des,t_des[4],k=2) Y_matches=[

Python + opencv for Dynamic Object Recognition, pythonopencv Note: This method is very affected by light changes. Figure of the result of your mobile phone shaking at home: Source code: #-*-Coding: UTF-8-*-"Created on Wed Sep 27 15:47:54 2017 @ author: tina" import cv2 import numpy as np camera = cv2.VideoCapture (0) # parameter 0 indicates the first camera # de

Special Collection AnalysisThe dataset is Letter-recognition.data, with a total of 20,000 data, separated by commas, the data instance is shown below, the first column is the letter mark, and the remainder is a different feature. t,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8Learning methods1. Read in the data and remove the separator number2, the first column of data as a marker, the rest of the training data3. Initialize the classifier and train with training data4, the use of test data to verify the acc

(New_right_point) point_limit (Heig Th_point) Point_limit (left_point) car_img= Dst[int (left_point[1]): Int (heigth_point[1]), int (left_point[0]): Int (new_right_point[0]) car_imgs.append (car_img)elifLEFT_POINT[1] > Right_point[1]:#Negative AngleNew_left_point = [Left_point[0], heigth_point[1]] Pts2= Np.float32 ([New_left_point, Heigth_point, Right_point])#characters are only highly needed to changePts1 =Np.float32 ([Left_point, Heigth_point, Right_point]) M=Cv2.getaffinetransform (Pts1, pts

reprint Please specify: @ Xiao Wu Yi Http://www.cnblogs.com/xiaowuyi qq Group: 64770604If the robot's face rotates with the face in front of you, you will not find this interaction interesting. Years ago, learning a bit of OPENCV, through the OPENCV can be simple to achieve the fac