Human-Action-Recognition-and-Video-Classification-using-SVM-and-Deep-CNN-

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# ML_Capstone ( Human Action Recognition and Video Classification using SVM and Deep-CNN )

## code instructions
Please install requirement packages from requirements.txt file. 
Please download data files from this site http://crcv.ucf.edu/data/UCF101/UCF101.rar

`SVM`
1. To create train and test data for SVM run the following commands 
python split_data.py train
python split_data.py test
This will create train and test folder with relevant video frames.

2. To train and test the above created data run the following command 
python walk.py 
 OR 
3. To test the data from already trained model
gunzip svm.pkl.gz file to svm.pkl and run python test_from_model.py

4. To evaluate the results and create heatmap run 
python eval_results.py

`CNN`
1. To create train and test data for CNN run the following commands 
python split_data_cnn.py train
python split_data_cnn.py test
This will create train and test folder with relevant video frames.

2. To convert our dataset according to Cifar10 format follow following steps 
git clone https://github.com/gskielian/PNG-2-CIFAR10
run ./resize-script.sh to resize image frames to 32*32 rgb pixels
now run, python convert-images-to-cifar-format.py 
this will create batch file for train and test.
Copy and paste these files to /tmp/cifar10-data/ folder

3. Next train cifar10 model (https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10) on our above created dataset 
python cifar10/cifar10_train.py

4. To test from trained cifar10 model (https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10) try 
python cifar10_eval.py

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