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
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
|
---
date: 2020-01-16 10:36
description: Tutorial on creating a custom Image Classifier using Turicreate and a dataset from Kaggle
tags: Tutorial, Colab, Turicreate, AI
---
# Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire
*For setting up Kaggle with Google Colab, please refer to <a href="/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab"> my previous post</a>*
## Dataset
### Mounting Google Drive
```python
import os
from google.colab import drive
drive.mount('/content/drive')
```
### Downloading Dataset from Kaggle
```python
os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"
!kaggle datasets download ashutosh69/fire-and-smoke-dataset
!unzip "fire-and-smoke-dataset.zip"
```
## Pre-Processing
```Termcap
!mkdir default smoke fire
```
\
```Termcap
!ls data/data/img_data/train/default/*.jpg
```
\
```Termcap
img_1002.jpg img_20.jpg img_519.jpg img_604.jpg img_80.jpg
img_1003.jpg img_21.jpg img_51.jpg img_60.jpg img_8.jpg
img_1007.jpg img_22.jpg img_520.jpg img_61.jpg img_900.jpg
img_100.jpg img_23.jpg img_521.jpg 'img_62 (2).jpg' img_920.jpg
img_1014.jpg img_24.jpg 'img_52 (2).jpg' img_62.jpg img_921.jpg
img_1018.jpg img_29.jpg img_522.jpg 'img_63 (2).jpg' img_922.jpg
img_101.jpg img_3000.jpg img_523.jpg img_63.jpg img_923.jpg
img_1027.jpg img_335.jpg img_524.jpg img_66.jpg img_924.jpg
img_102.jpg img_336.jpg img_52.jpg img_67.jpg img_925.jpg
img_1042.jpg img_337.jpg img_530.jpg img_68.jpg img_926.jpg
img_1043.jpg img_338.jpg img_531.jpg img_700.jpg img_927.jpg
img_1046.jpg img_339.jpg 'img_53 (2).jpg' img_701.jpg img_928.jpg
img_1052.jpg img_340.jpg img_532.jpg img_702.jpg img_929.jpg
img_107.jpg img_341.jpg img_533.jpg img_703.jpg img_930.jpg
img_108.jpg img_3.jpg img_537.jpg img_704.jpg img_931.jpg
img_109.jpg img_400.jpg img_538.jpg img_705.jpg img_932.jpg
img_10.jpg img_471.jpg img_539.jpg img_706.jpg img_933.jpg
img_118.jpg img_472.jpg img_53.jpg img_707.jpg img_934.jpg
img_12.jpg img_473.jpg img_540.jpg img_708.jpg img_935.jpg
img_14.jpg img_488.jpg img_541.jpg img_709.jpg img_938.jpg
img_15.jpg img_489.jpg 'img_54 (2).jpg' img_70.jpg img_958.jpg
img_16.jpg img_490.jpg img_542.jpg img_710.jpg img_971.jpg
img_17.jpg img_491.jpg img_543.jpg 'img_71 (2).jpg' img_972.jpg
img_18.jpg img_492.jpg img_54.jpg img_71.jpg img_973.jpg
img_19.jpg img_493.jpg 'img_55 (2).jpg' img_72.jpg img_974.jpg
img_1.jpg img_494.jpg img_55.jpg img_73.jpg img_975.jpg
img_200.jpg img_495.jpg img_56.jpg img_74.jpg img_980.jpg
img_201.jpg img_496.jpg img_57.jpg img_75.jpg img_988.jpg
img_202.jpg img_497.jpg img_58.jpg img_76.jpg img_9.jpg
img_203.jpg img_4.jpg img_59.jpg img_77.jpg
img_204.jpg img_501.jpg img_601.jpg img_78.jpg
img_205.jpg img_502.jpg img_602.jpg img_79.jpg
img_206.jpg img_50.jpg img_603.jpg img_7.jpg
```
The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate
```python
from PIL import Image
import glob
folders = ["default","smoke","fire"]
for folder in folders:
n = 1
for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
im = Image.open(file)
rgb_im = im.convert('RGB')
rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
n +=1
for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
im = Image.open(file)
rgb_im = im.convert('RGB')
rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
n +=1
```
\
```Termcap
!mkdir train
!mv default ./train
!mv smoke ./train
!mv fire ./train
```
## Making the Image Classifier
### Making an SFrame
```Termcap
!pip install turicreate
```
\
```python
import turicreate as tc
import os
data = tc.image_analysis.load_images("./train", with_path=True)
data["label"] = data["path"].apply(lambda path: os.path.basename(os.path.dirname(path)))
print(data)
data.save('fire-smoke.sframe')
```
\
```Termcap
+-------------------------+------------------------+
| path | image |
+-------------------------+------------------------+
| ./train/default/1.jpg | Height: 224 Width: 224 |
| ./train/default/10.jpg | Height: 224 Width: 224 |
| ./train/default/100.jpg | Height: 224 Width: 224 |
| ./train/default/101.jpg | Height: 224 Width: 224 |
| ./train/default/102.jpg | Height: 224 Width: 224 |
| ./train/default/103.jpg | Height: 224 Width: 224 |
| ./train/default/104.jpg | Height: 224 Width: 224 |
| ./train/default/105.jpg | Height: 224 Width: 224 |
| ./train/default/106.jpg | Height: 224 Width: 224 |
| ./train/default/107.jpg | Height: 224 Width: 224 |
+-------------------------+------------------------+
[2028 rows x 2 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
+-------------------------+------------------------+---------+
| path | image | label |
+-------------------------+------------------------+---------+
| ./train/default/1.jpg | Height: 224 Width: 224 | default |
| ./train/default/10.jpg | Height: 224 Width: 224 | default |
| ./train/default/100.jpg | Height: 224 Width: 224 | default |
| ./train/default/101.jpg | Height: 224 Width: 224 | default |
| ./train/default/102.jpg | Height: 224 Width: 224 | default |
| ./train/default/103.jpg | Height: 224 Width: 224 | default |
| ./train/default/104.jpg | Height: 224 Width: 224 | default |
| ./train/default/105.jpg | Height: 224 Width: 224 | default |
| ./train/default/106.jpg | Height: 224 Width: 224 | default |
| ./train/default/107.jpg | Height: 224 Width: 224 | default |
+-------------------------+------------------------+---------+
[2028 rows x 3 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
```
### Making the Model
```python
import turicreate as tc
# Load the data
data = tc.SFrame('fire-smoke.sframe')
# Make a train-test split
train_data, test_data = data.random_split(0.8)
# Create the model
model = tc.image_classifier.create(train_data, target='label')
# Save predictions to an SArray
predictions = model.predict(test_data)
# Evaluate the model and print the results
metrics = model.evaluate(test_data)
print(metrics['accuracy'])
# Save the model for later use in Turi Create
model.save('fire-smoke.model')
# Export for use in Core ML
model.export_coreml('fire-smoke.mlmodel')
```
\
```Termcap
Performing feature extraction on resized images...
Completed 64/1633
Completed 128/1633
Completed 192/1633
Completed 256/1633
Completed 320/1633
Completed 384/1633
Completed 448/1633
Completed 512/1633
Completed 576/1633
Completed 640/1633
Completed 704/1633
Completed 768/1633
Completed 832/1633
Completed 896/1633
Completed 960/1633
Completed 1024/1633
Completed 1088/1633
Completed 1152/1633
Completed 1216/1633
Completed 1280/1633
Completed 1344/1633
Completed 1408/1633
Completed 1472/1633
Completed 1536/1633
Completed 1600/1633
Completed 1633/1633
PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
You can set ``validation_set=None`` to disable validation tracking.
Logistic regression:
--------------------------------------------------------
Number of examples : 1551
Number of classes : 3
Number of feature columns : 1
Number of unpacked features : 2048
Number of coefficients : 4098
Starting L-BFGS
--------------------------------------------------------
+-----------+----------+-----------+--------------+-------------------+---------------------+
| Iteration | Passes | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+-----------+--------------+-------------------+---------------------+
| 0 | 6 | 0.018611 | 0.891830 | 0.553836 | 0.560976 |
| 1 | 10 | 0.390832 | 1.622383 | 0.744681 | 0.792683 |
| 2 | 11 | 0.488541 | 1.943987 | 0.733075 | 0.804878 |
| 3 | 14 | 2.442703 | 2.512545 | 0.727917 | 0.841463 |
| 4 | 15 | 2.442703 | 2.826964 | 0.861380 | 0.853659 |
| 9 | 28 | 2.340435 | 5.492035 | 0.941328 | 0.975610 |
+-----------+----------+-----------+--------------+-------------------+---------------------+
Performing feature extraction on resized images...
Completed 64/395
Completed 128/395
Completed 192/395
Completed 256/395
Completed 320/395
Completed 384/395
Completed 395/395
0.9316455696202531
```
We just got an accuracy of 94% on Training Data and 97% on Validation Data!
|
