CustomPose-Classification-Mediapipe

Creating a Custom pose classification using Mediapipe with help of OpenCV

Sample Video Output:

Sample Image Output:

(Demo) Let's Get Started...

Using this Custom Pose Classification, I am going to Create a Yoga Pose Classification. Using Yoga Poses Dataset.

Clone this Repository

git clone https://github.com/naseemap47/CustomPose-Classification-Mediapipe.git
cd CustomPose-Classification-Mediapipe

Install Dependency

pip3 install -r requirements.txt

1.Download Dataset:

Yoga Poses Dataset:

wget -O yoga_poses.zip http://download.tensorflow.org/data/pose_classification/yoga_poses.zip

About Dataset:

• 5 Classes: Chair, Cobra, Dog, Tree and Warrior
• Contain Train and Test data
• Combain both Train and Test data

Dataset Structure:

├── Dataset
│   ├── Chair
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   ├── ...
│   ├── Cobra
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   ├── ...
.   .
.   .

2.Create Landmark Dataset for each Classes

python3 poseLandmark_csv.py -i <path_to_data_dir> -o <path_to_save_csv>

Example:

python3 poseLandmark_csv.py -i data/ -o data.csv

CSV file will be saved in <path_to_save_csv>

3.Create DeepLearinng Model to predict Human Pose

python3 poseModel.py -i <path_to_save_csv> -o <path_to_save_model>

Example:

python3 poseModel.py -i data.csv -o model.h5

Model will saved in <path_to_save_model> and Model Metrics saved in metrics.png

4.Inference

Show Predicted Pose Class on Test Image or Video or Web-cam
To Save:

• --save: It will save Images (on ImageOutput Dir) or Videos ("output.avi")

python3 inference.py --model <path_to_model> \
                     --conf <model_prediction_confidence> \
                     --source <image or video or web-cam>

# to save
python3 inference.py --model <path_to_model> \
                     --conf <model_prediction_confidence> \
                     --source <image or video or web-cam> \
                     --save

Example:

python3 inference.py --model model.h5 --conf 0.75 --source data/test/image.jpg
python3 inference.py --model model.h5 --conf 0.75 --source data/test/video.mp4
python3 inference.py --model model.h5 --conf 0.75 --source 0  # web-cam

# to save
python3 inference.py --model model.h5 --conf 0.75 --source data/test/image.jpg --save
python3 inference.py --model model.h5 --conf 0.75 --source data/test/video.mp4 --save
python3 inference.py --model model.h5 --conf 0.75 --source 0 --save # web-cam

To Exit Window - Press Q-key

Custom Pose Classification

Clone this Repository

git clone https://github.com/naseemap47/CustomPose-Classification-Mediapipe.git
cd CustomPose-Classification-Mediapipe
git checkout custom

1.Take your Custom Pose Dataset

Dataset Structure:

├── Dataset
│   ├── Pose_1
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   ├── ...
│   ├── Pose_2
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   ├── ...
.   .
.   .

2.Create Landmark Dataset for each Classes

CSV file will be saved in <path_to_save_csv>

python3 poseLandmark_csv.py -i <path_to_data_dir> -o <path_to_save_csv>

3.Create DeepLearinng Model to predict Human Pose

Model will saved in <path_to_save_model> and Model Metrics saved in metrics.png

python3 poseModel.py -i <path_to_save_csv> -o <path_to_save_model>

4.Inference

Open inference.py

change Line-43: According to your Class Names, Write Class Order
To Save:

• --save: It will save Images (on ImageOutput Dir) or Videos ("output.avi")

python3 inference.py --model <path_to_model> \
                     --conf <model_prediction_confidence> \
                     --source <image or video or web-cam> \
                     
# to save
python3 inference.py --model <path_to_model> \
                     --conf <model_prediction_confidence> \
                     --source <image or video or web-cam> \
                     --save

Show Predicted Pose Class on Test Image or Video or Web-cam

To Exit Window - Press Q-key