6 minute read

Created on January 16, 2020

Last modified on June 1, 2020

Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire

For setting up Kaggle with Google Colab, please refer to my previous post

Dataset

Mounting Google Drive

import os from google.colab import drive drive . mount ( '/content/drive' )

Downloading Dataset from Kaggle

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

!mkdir default smoke fire





!ls data/data/img_data/train/default/*.jpg





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

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





!mkdir train !mv default ./train !mv smoke ./train !mv fire ./train

Making the Image Classifier

Making an SFrame

!pip install turicreate





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' )





+-------------------------+------------------------+ | 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

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' )





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!