svm.py

import os
import cv2
import numpy as np
from os import listdir
import sklearn
from sklearn import svm
from os.path import isfile, join
from sklearn.externals import joblib

path = 'train/'
training_set = []
training_labels = []

def get_hog() : 
    '''winSize = (20,20)
    blockSize = (8,8)
    blockStride = (4,4)
    cellSize = (8,8)
    nbins = 9
    derivAperture = 1
    winSigma = -1.
    histogramNormType = 0
    L2HysThreshold = 0.2
    gammaCorrection = 1
    nlevels = 64
    signedGradient = True'''

    hog = cv2.HOGDescriptor("hog.xml")

    return hog
    affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR

hog = get_hog();
print 'training...'
for root, dirs, files in os.walk(path):
	print 'processing.. ',root
	for name in files:
		if name.endswith((".png")):
			if (os.path.getsize(root + str('/') + name)) != 0 :
				label = root.split('/')[1]
				img = cv2.imread(root +str('/')+ name)
				#res = cv2.resize(img,(250,250))
				res = img
				#gray_image = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
				gray_image = hog.compute(res)
				xarr = np.squeeze(np.array(gray_image).astype(np.float32))
				m,v = cv2.PCACompute(xarr, mean = np.array([]))
				arr = np.array(v)
				flat_arr = arr.ravel()
				training_set.append(flat_arr)
				training_labels.append(label)
	print 'done ',root

trainData = np.float32(training_set)
responses = training_labels
#svm = cv2.SVM()
svm = sklearn.svm.LinearSVC(C = 1.0,  random_state = 0)
svm.fit(trainData,responses)
#svm.save('svm_data.dat')
print 'training done!'

print 'testing...'

path = 'test/'
testing_set = []
testing_labels = []

for root, dirs, files in os.walk(path):
    for name in files:
        if name.endswith((".png")):
        	if (os.path.getsize(root + str('/') + name)) != 0 :
        		label = root.split('/')[1]
        		img = cv2.imread(root + str('/') + name)
        		#res=cv2.resize(img,(250,250))
        		res = img
        		gray_image = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
        		xarr=np.squeeze(np.array(gray_image).astype(np.float32))
        		m,v=cv2.PCACompute(xarr, mean = np.array([]))
        		arr= np.array(v)
        		flat_arr= arr.ravel()
        		testing_set.append(flat_arr)
        		testing_labels.append(label)
t = 0
f = 0
testData = np.float32(testing_set)
responses = svm.predict(testData)

for i in range(len(responses)):
	if responses[i] == testing_labels[i]:
		t += 1
	else:
		f += 1

print responses
print set(responses)
print len(responses),' ', len(testing_labels)
print 'True ',t
print 'False ',f

file = open('results_svm.txt','w')
for i in range(len(responses)):
	file.write(testing_labels[i]+"  "+responses[i]+'\n')
file.close()

joblib.dump(svm, 'svm.pkl'+ '.gz', compress=('gzip', 3))