-
Notifications
You must be signed in to change notification settings - Fork 0
/
traffic_sign_classifier.py
203 lines (147 loc) · 5.39 KB
/
traffic_sign_classifier.py
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
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
import pickle
import time
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import explore_dataset
from preprocess.img.normalize import normalize_luminance_srgb
from output_sign_imgs import output_grid_imgs, output_compared_imgs
from models.lenet import Lenet
from dataset import ImageDataset
from visualize.img import combine_in_one_img
def load_files():
print('>>> Loading files ...')
with open('./data/train.p', 'rb') as f:
train = pickle.load(f)
with open('./data/valid.p', 'rb') as f:
valid = pickle.load(f)
with open('./data/test.p', 'rb') as f:
test = pickle.load(f)
sign_names = pd.read_csv('./data/signnames.csv')
return train, valid, test, sign_names
def preprocess(imgs):
print('>>> Preprocessing ...')
preprocessed = []
def _grayscale(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def _normalize(img):
return img.astype(np.float32)/255.0
def _reshape(img):
img = img.reshape(32, 32, 1)
return np.transpose(img, (2, 0, 1))
def _pipeline(img):
img = normalize_luminance_srgb(img)
img = _grayscale(img)
img = _normalize(img)
img = _reshape(img)
return img
for img in imgs:
pipelined = _pipeline(img)
preprocessed.append(pipelined)
preprocessed = np.array(preprocessed)
print(type(preprocessed))
print(preprocessed.shape)
return preprocessed
def train_model(model, train):
print('>>> Train model ...')
dataset = ImageDataset(train['features'], train['labels'])
dataloader = DataLoader(dataset, batch_size=4,
shuffle=True, num_workers=2)
n_epoch = 10
lr = 0.0005
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
for epoch in range(n_epoch):
model.train()
running_loss = 0.0
for i, (features, labels) in enumerate(dataloader):
optimizer.zero_grad()
outputs = model(features)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch+1, i+1, running_loss/2000))
running_loss = 0.0
return model
def test_model(model, test):
print('>>> Test model ...')
model.eval()
dataset = ImageDataset(test['features'], test['labels'])
dataloader = DataLoader(dataset, batch_size=4,
shuffle=False, num_workers=2)
correct = 0
total = 0
for i, (features, labels) in enumerate(dataloader):
outputs = model(features)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = correct / total
print('Accuracy: %.3f' % accuracy)
return accuracy
def test_web_imgs(model, features):
print('>>> Test web images ...')
dataset = ImageDataset(features, np.array([13, 22, 15, 4, 38]))
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
model.eval()
for i, (features, labels) in enumerate(dataloader):
outputs = model(features)
print('label', labels)
print('outputs', outputs)
_, predicted = torch.max(outputs, 1)
m = torch.nn.Softmax()
softmax = m(outputs)
print('softmax', softmax)
print('predicted', predicted)
sorts = []
for i, output in enumerate(softmax[0]):
sorts.append((output.item(), i))
sorts = sorted(sorts, key=lambda x: x[0], reverse=True)
print(sorts)
for i in range(5):
print('%.4f' % sorts[i][0])
def load_web_imgs(paths):
print('>>> Loading web images ...')
imgs = []
for path in paths:
img = cv2.imread(str(path))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
imgs.append(img)
imgs = np.array(imgs)
print(type(imgs))
print(imgs.shape)
fig = combine_in_one_img(imgs, ["", "", "", "", ""], [None, None, None, None, None], '15')
fig.savefig('./figures/web_images.png', dpi=300, transparent=True, bbox_inches='tight', pad_inches=0)
return imgs
def main():
train, valid, test, sign_names = load_files()
# explore_dataset.output_data_summery(train, valid, test)
# explore_dataset.output_histogram(train, valid, test)
# train['features'] = preprocess(train['features'])
# output_grid_imgs('./outputs/grayscaled.png',
# 5, 5, train['features'], train['labels'], sign_names)
# with open('./data/train_preprocessed.p', 'wb') as f:
# pickle.dump(train, f)
# with open('./data/train_preprocessed.p', 'rb') as f:
# train = pickle.load(f)
model = Lenet()
# model = train_model(model, train)
#
# torch.save(model.state_dict(), './outputs/lenet.p')
model.load_state_dict(torch.load('./outputs/lenet.p'))
# test['features'] = preprocess(test['features'])
# valid['features'] = preprocess(valid['features'])
imgs = load_web_imgs(Path('./data/web').glob('*'))
features = preprocess(imgs)
test_web_imgs(model, features)
# test_model(model, test)
if __name__ == "__main__":
main()