6 minute readCreated on January 16, 2020Last 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...
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-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
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-0.9316455696202531
-

We just got an accuracy of 94% on Training Data and 97% on Validation Data!

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