from myHog import hog import cPickle as pickle from sklearn import svm import matplotlib.image as mpimg from skimage.color import rgb2gray from skimage import transform import numpy filename = "savedLinearSVM19999.pkl" with open(filename, 'r') as f: LinearClf1 = pickle.load(f) images = [] for i in range(61): test = mpimg.imread("test/" + "test (" + str(i + 1) + ").jpg") test = transform.resize(test, numpy.array([64, 64])) test = rgb2gray(test) test = hog(test).ravel() images.append(test) print "...Load OK!" f = open('test/lables.txt') data = f.readlines() f.close for i in range(len(data)): data[i] = int(data[i][3]) data = data[0:61] p = [] correct = 0 for i in range(len(images)): p.append(LinearClf1.predict(images[i])) if p[i] == data[i]:
from myHog import hog import cPickle as pickle from sklearn import svm import matplotlib.image as mpimg from skimage.color import rgb2gray from skimage import transform import numpy filename = "savedLinearSVM19999.pkl" with open(filename,'r') as f: LinearClf1 = pickle.load(f) images = [] for i in range(61): test = mpimg.imread("test/"+"test ("+ str(i+1) + ").jpg") test = transform.resize(test,numpy.array([64,64])) test = rgb2gray(test) test = hog(test).ravel() images.append(test) print "...Load OK!" f = open('test/lables.txt') data = f.readlines() f.close for i in range(len(data)): data[i] = int(data[i][3]) data = data[0:61] p = [] correct = 0 for i in range(len(images)): p.append(LinearClf1.predict(images[i])) if p[i] == data[i]:
imgs.append(ori_IMG) elif i > 98 and i <= 998: ori_IMG = 'images/00' + str(i+1) + '.jpg' imgs.append(ori_IMG) elif i > 998 : ori_IMG = 'images/0' + str(i+1) + '.jpg' imgs.append(ori_IMG) else: ori_IMG = 'images/0000' + str(i+1) + '.jpg' imgs.append(ori_IMG) for i in imgs: # print i img = mpimg.imread(i) img = transform.resize(img, numpy.array([64,64])) img = rgb2gray(img) result = hog(img) result = (result.ravel()).tolist() results.append(result) f = open('lables.txt') data = f.readlines() f.close for i in range(len(data)): data[i] = int(data[i][10]) data = data[0:2000] clf = svm.LinearSVC() clf.fit(results,data) filename = "savedLinearSVM2000.pkl" with open(filename,'w') as f: pname = pickle.dump(clf,f) print "DONE!"
__author__ = 'Wong Sylvia' from myHog import hog import cPickle as pickle from sklearn import svm import matplotlib.image as mpimg from skimage.color import rgb2gray from skimage import transform import numpy filename = "savedlinearSVM100.pkl" with open(filename, 'r') as f: clf = pickle.load(f) test1 = mpimg.imread("test1.jpg") test1 = transform.resize(test1, numpy.array([64, 64])) test1 = rgb2gray(test1) test1 = hog(test1).ravel() test0 = mpimg.imread("test0.jpg") test0 = transform.resize(test0, numpy.array([64, 64])) test0 = rgb2gray(test0) test0 = hog(test0).ravel() print "Test Picture : Human" print clf.predict(test1) print "Test Picture : Non-Human" print clf.predict(test0)