├── .idea ├── deployment.xml └── vcs.xml └── HOG+SVM classifer ├── classifier.py ├── config.py ├── data └── config │ └── config.cfg ├── extractFeat.py └── readme.txt /.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /HOG+SVM classifer/classifier.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | #encoding:utf-8 3 | """ 4 | @author: 5 | @time:2017/3/19 11:08 6 | """ 7 | from sklearn.svm import LinearSVC 8 | from sklearn.externals import joblib 9 | import numpy as np 10 | import glob 11 | import os 12 | import time 13 | from config import * 14 | 15 | if __name__ == "__main__": 16 | t0 = time.time() 17 | clf_type = 'LIN_SVM' 18 | fds = [] 19 | labels = [] 20 | num = 0 21 | total = 0 22 | for feat_path in glob.glob(os.path.join(train_feat_path, '*.feat')): 23 | data = joblib.load(feat_path) 24 | fds.append(data[:-1]) 25 | labels.append(data[-1]) 26 | if clf_type is 'LIN_SVM': 27 | clf = LinearSVC() 28 | print("Training a Linear SVM Classifier.") 29 | clf.fit(fds, labels) 30 | # If feature directories don't exist, create them 31 | # if not os.path.isdir(os.path.split(model_path)[0]): 32 | # os.makedirs(os.path.split(model_path)[0]) 33 | # joblib.dump(clf, model_path) 34 | # clf = joblib.load(model_path) 35 | print("Classifier saved to {}".format(model_path)) 36 | for feat_path in glob.glob(os.path.join(test_feat_path, '*.feat')): 37 | total += 1 38 | data_test = joblib.load(feat_path) 39 | temp = data_test[:-1] 40 | data_test_feat = temp.reshape((1, -1)) 41 | result = clf.predict(data_test_feat) 42 | if int(result) == int(data_test[-1]): 43 | num += 1 44 | rate = float(num)/total 45 | t1 = time.time() 46 | print('The classification accuracy is %f'%rate) 47 | print('The cast of time is :%f'%(t1-t0)) 48 | 49 | 50 | 51 | 52 | -------------------------------------------------------------------------------- /HOG+SVM classifer/config.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | #encoding:utf-8 3 | """ 4 | @author: 5 | @time:2017/3/18 21:03 6 | Set the config variable. 7 | """ 8 | import configparser as cp 9 | import json 10 | 11 | config = cp.RawConfigParser() 12 | config.read('./data/config/config.cfg') 13 | 14 | orientations = json.loads(config.get("hog", "orientations")) 15 | pixels_per_cell = json.loads(config.get("hog", "pixels_per_cell")) 16 | cells_per_block = json.loads(config.get("hog", "cells_per_block")) 17 | visualize = config.getboolean("hog", "visualize") 18 | normalize = config.getboolean("hog", "normalize") 19 | train_feat_path = config.get("path", "train_feat_path") 20 | test_feat_path = config.get("path", "test_feat_path") 21 | model_path = config.get("path", "model_path") 22 | -------------------------------------------------------------------------------- /HOG+SVM classifer/data/config/config.cfg: -------------------------------------------------------------------------------- 1 | [hog] 2 | orientations: 9 3 | pixels_per_cell: [8, 8] 4 | cells_per_block: [2, 2] 5 | visualize: False 6 | normalize: True 7 | 8 | 9 | [path] 10 | train_feat_path : ./data/features/train 11 | test_feat_path : ./data/features/test 12 | model_path : ./data/models/svm.model 13 | 14 | -------------------------------------------------------------------------------- /HOG+SVM classifer/extractFeat.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | #encoding:utf-8 3 | """ 4 | @author: 5 | @time:2017/3/18 14:33 6 | """ 7 | # Import the functions to calculate feature descriptions 8 | from skimage.feature import hog 9 | import numpy as np 10 | from sklearn.externals import joblib 11 | # To read image file and save image feature descriptions 12 | import os 13 | import time 14 | # import glob 15 | import pickle as pk 16 | from config import * 17 | # import matplotlib.pyplot as plt 18 | 19 | 20 | def unpickle(file): 21 | import pickle 22 | with open(file, 'rb') as fo: 23 | dict = pickle.load(fo, encoding='bytes') 24 | return dict 25 | 26 | def getData(filePath): 27 | TrainData = [] 28 | for childDir in os.listdir(filePath): 29 | if 'data_batch' in childDir: 30 | f = os.path.join(filePath, childDir) 31 | data = unpickle(f) 32 | # train = np.reshape(data[str.encode('data')], (10000, 3, 32 * 32)) 33 | # If your python version do not support to use this way to transport str to bytes. 34 | # Think another way and you can. 35 | train = np.reshape(data[b'data'], (10000, 3, 32 * 32)) 36 | labels = np.reshape(data[b'labels'], (10000, 1)) 37 | fileNames = np.reshape(data[b'filenames'], (10000, 1)) 38 | datalebels = zip(train, labels, fileNames) 39 | TrainData.extend(datalebels) 40 | if childDir == "test_batch": 41 | f = os.path.join(filePath, childDir) 42 | data = unpickle(f) 43 | test = np.reshape(data[b'data'], (10000, 3, 32 * 32)) 44 | labels = np.reshape(data[b'labels'], (10000, 1)) 45 | fileNames = np.reshape(data[b'filenames'], (10000, 1)) 46 | TestData = zip(test, labels, fileNames) 47 | return TrainData, TestData 48 | 49 | 50 | def getFeat(TrainData, TestData): 51 | for data in TestData: 52 | image = np.reshape(data[0].T, (32, 32, 3)) 53 | gray = rgb2gray(image)/255.0 54 | fd = hog(gray, orientations, pixels_per_cell, cells_per_block) 55 | fd = np.concatenate((fd, data[1])) 56 | filename = list(data[2]) 57 | fd_name = str(filename[0], encoding = "utf-8") .split('.')[0]+'.feat' 58 | fd_path = os.path.join('./data/features/test/', fd_name) 59 | joblib.dump(fd, fd_path) 60 | print("Test features are extracted and saved.") 61 | for data in TrainData: 62 | image = np.reshape(data[0].T, (32, 32, 3)) 63 | gray = rgb2gray(image)/255.0 64 | fd = hog(gray, orientations, pixels_per_cell, cells_per_block) 65 | fd = np.concatenate((fd, data[1])) 66 | filename = list(data[2]) 67 | fd_name = str(filename[0], encoding="utf-8") .split('.')[0]+'.feat' 68 | fd_path = os.path.join('./data/features/train/', fd_name) 69 | joblib.dump(fd, fd_path) 70 | print("Train features are extracted and saved.") 71 | def rgb2gray(im): 72 | gray = im[:, :, 0]*0.2989+im[:, :, 1]*0.5870+im[:, :, 2]*0.1140 73 | return gray 74 | if __name__ == '__main__': 75 | t0 = time.time() 76 | filePath = r'E:\dataset\cifar-10\cifar-10-batches-py' 77 | TrainData, TestData = getData(filePath) 78 | getFeat(TrainData, TestData) 79 | t1 = time.time() 80 | print("Features are extracted and saved.") 81 | print('The cast of time is:%f'%(t1-t0)) 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | -------------------------------------------------------------------------------- /HOG+SVM classifer/readme.txt: -------------------------------------------------------------------------------- 1 | step1:添加需要的包，比较简单。 2 | step2:下载训练图片和测试图片数据。该程序使用的是The CIFAR-10 dataset 3 | http://www.cs.toronto.edu/~kriz/cifar.html 4 | step3:将filePath = r'D:\ObjectClassify\cifar-10-batches-py'，改成你的数据路径就好。 5 | ###################### 6 | 之前的版本是python2.7写的，最近上传的是python3.5的。如果还有版本错误请issues。 --------------------------------------------------------------------------------