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...
+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
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+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!

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