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classify_foreground.py
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classify_foreground.py
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#!/usr/bin/python
import sys, os, time
from sklearn.svm import LinearSVC
from sklearn import decomposition
import numpy as np
from sklearn import datasets
from skimage.feature import hog
from sklearn.utils import shuffle
from sklearn import metrics
import sys
from numpy import genfromtxt
import warnings
import cv2
Rsol = 30
def vectorize_test(Y):
ht, wd = np.shape(Y)
if (ht > wd):
y = Rsol
x = int(round((Rsol*wd)/float(ht)))
else:
x = Rsol
y = int(round((Rsol*ht)/float(wd)))
# print Y
# print np.shape(Y), x, y
Y = cv2.resize(Y, (x,y))
v = np.zeros((Rsol, Rsol), dtype=int)
v[0:y,0:x] = Y
v = np.reshape(v, Rsol*Rsol)
v = map(int, v)
return np.array(v)
def get_HoG(xs, size_cell=4, size_block=4, orientation=7):
hog_xs = []
for x in xs:
fd = hog(x.reshape((Rsol, Rsol)),
orientations=orientation,
pixels_per_cell=(size_cell, size_cell),
cells_per_block=(size_block, size_block), visualise=False)
hog_xs.append(fd)
return hog_xs
def get_class(clf, im):
lblMap = {0: 'Person', 1: 'Bicycle', 2: 'Motorcycle',3: 'Rickshaw',4: 'Autorickshaw',5: 'Car'}
X_test = vectorize_test(im)
X_test = [X_test]
y_pred = clf.predict(get_HoG(X_test))
return lblMap[y_pred[0]]
def get_clf():
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=DeprecationWarning)
extnsn = "_grey_ftr.csv"
train_dir = "CSVs/train_dir"
X_train_o = genfromtxt(train_dir + str(Rsol) + extnsn, delimiter=',', dtype=int)
y_train = genfromtxt(train_dir + str(Rsol) +"_lbl.csv", delimiter=',', dtype=int)
X_train = get_HoG(X_train_o)
clf = LinearSVC()
clf.fit(X_train, y_train)
return clf