import cv2
import numpy as np
from matplotlib import pyplot as plt
from os import listdir
# local modules
from common import clock, mosaic
#Parameter
SIZE = 32
CLASS_NUMBER = 13
def load_traffic_dataset():
dataset = []
labels = []
for sign_type in range(CLASS_NUMBER):
sign_list = listdir("./dataset/{}".format(sign_type))
for sign_file in sign_list:
if '.png' in sign_file:
path = "./dataset/{}/{}".format(sign_type,sign_file)
print(path)
img = cv2.imread(path,0)
img = cv2.resize(img, (SIZE, SIZE))
img = np.reshape(img, [SIZE, SIZE])
dataset.append(img)
labels.append(sign_type)
return np.array(dataset), np.array(labels)
def deskew(img):
m = cv2.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SIZE*skew], [0, 1, 0]])
img = cv2.warpAffine(img, M, (SIZE, SIZE), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
return img
class StatModel(object):
def load(self, fn):
self.model.load(fn) # Known bug: https://github.com/opencv/opencv/issues/4969
def save(self, fn):
self.model.save(fn)
class SVM(StatModel):
def __init__(self, C = 12.5, gamma = 0.50625):
self.model = cv2.ml.SVM_create()
self.model.setGamma(gamma)
self.model.setC(C)
self.model.setKernel(cv2.ml.SVM_RBF)
self.model.setType(cv2.ml.SVM_C_SVC)
def train(self, samples, responses):
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
def predict(self, samples):
return self.model.predict(samples)[1].ravel()
def evaluate_model(model, data, samples, labels):
resp = model.predict(samples)
print(resp)
err = (labels != resp).mean()
print('Accuracy: %.2f %%' % ((1 - err)*100))
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp):
confusion[int(i), int(j)] += 1
print('confusion matrix:')
print(confusion)
vis = []
for img, flag in zip(data, resp == labels):
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if not flag:
img[...,:2] = 0
vis.append(img)
return mosaic(16, vis)
def preprocess_simple(data):
return np.float32(data).reshape(-1, SIZE*SIZE) / 255.0
def get_hog() :
winSize = (20,20)
blockSize = (10,10)
blockStride = (5,5)
cellSize = (10,10)
nbins = 9
derivAperture = 1
winSigma = -1.
histogramNormType = 0
L2HysThreshold = 0.2
gammaCorrection = 1
nlevels = 64
signedGradient = True
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,histogramNormType,L2HysThreshold,gammaCorrection,nlevels, signedGradient)
return hog
affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR
def training():
print('Loading data from data.png ... ')
# Load data.
#data, labels = load_data('data.png')
data, labels = load_traffic_dataset()
print(data.shape)
print('Shuffle data ... ')
# Shuffle data
rand = np.random.RandomState(10)
shuffle = rand.permutation(len(data))
data, labels = data[shuffle], labels[shuffle]
print('Deskew images ... ')
data_deskewed = list(map(deskew, data))
print('Defining HoG parameters ...')
# HoG feature descriptor
hog = get_hog()
print('Calculating HoG descriptor for every image ... ')
hog_descriptors = []
for img in data_deskewed:
hog_descriptors.append(hog.compute(img))
hog_descriptors = np.squeeze(hog_descriptors)
print('Spliting data into training (90%) and test set (10%)... ')
train_n=int(0.9*len(hog_descriptors))
data_train, data_test = np.split(data_deskewed, [train_n])
hog_descriptors_train, hog_descriptors_test = np.split(hog_descriptors, [train_n])
labels_train, labels_test = np.split(labels, [train_n])
print('Training SVM model ...')
model = SVM()
model.train(hog_descriptors_train, labels_train)
print('Saving SVM model ...')
model.save('data_svm.dat')
return model
def getLabel(model, data):
gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
img = [cv2.resize(gray,(SIZE,SIZE))]
#print(np.array(img).shape)
img_deskewed = list(map(deskew, img))
hog = get_hog()
hog_descriptors = np.array([hog.compute(img_deskewed[0])])
hog_descriptors = np.reshape(hog_descriptors, [-1, hog_descriptors.shape[1]])
return int(model.predict(hog_descriptors)[0])
