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diff --git a/Content/posts/2020-01-16-Image-Classifier-Using-Turicreate.md b/Content/posts/2020-01-16-Image-Classifier-Using-Turicreate.md new file mode 100644 index 0000000..5045a57 --- /dev/null +++ b/Content/posts/2020-01-16-Image-Classifier-Using-Turicreate.md @@ -0,0 +1,268 @@ +--- +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 +--- + +# 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! |