Creating a Custom pose classification using Mediapipe with help of OpenCV
GitHub: CustomPose-Classification-Mediapipe
Sample OutPut
Video
Image
Demo
I am going to create a Custom Human pose classification using Yoga Pose Dataset.
1. Clone the Repository:
git clone https://github.com/naseemap47/CustomPose-Classification-Mediapipe.git
cd CustomPose-Classification-Mediapipe
Install Dependency
pip3 install -r requirements.txt
Download 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
- Combine both Train and Test data
├── Dataset
│ ├── Chair
│ │ ├── 1.jpg
│ │ ├── 2.jpg
│ │ ├── ...
│ ├── Cobra
│ │ ├── 1.jpg
│ │ ├── 2.jpg
│ │ ├── ...
. .
. .
2. Create Landmark Dataset for each Classes
-
-i,--dataset__ Path to Dataset -
-o,--save__ Path to save CSV file
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
-
-i,--dataset__ Path to CSV Data -
-o,--save__ Path to save model.h5 file
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
-
-m,--model__ Path to saved model.h5 -
-c,--conf__ Min prediction conf to detect pose class (0<conf<1) -
-i,--source__ Path to Image or Video, for webcam use Zero(0) -
--save__ It will save Images (on ImageOutput Dir) or Videos (“output.avi”)
# Image
python3 inference.py --model model.h5 --conf 0.75 --source data/test/image.jpg
# Video
python3 inference.py --model model.h5 --conf 0.75 --source data/test/video.mp4
# web-cam
python3 inference.py --model model.h5 --conf 0.75 --source 0
###### to save ######
# Image
python3 inference.py --model model.h5 --conf 0.75 --source data/test/image.jpg --save
# Video
python3 inference.py --model model.h5 --conf 0.75 --source data/test/video.mp4 --save
# web-cam
python3 inference.py --model model.h5 --conf 0.75 --source 0 --save
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
Example:
├── Dataset
│ ├── Pose_1
│ │ ├── 1.jpg
│ │ ├── 2.jpg
│ │ ├── ...
│ ├── Pose_2
│ │ ├── 1.jpg
│ │ ├── 2.jpg
│ │ ├── ...
. .
. .
2. Create Landmark Dataset for each Classes
-
-i,--dataset__ Path to Dataset -
-o,--save__ Path to save CSV file
python3 poseLandmark_csv.py -i <path_to_data_dir> -o <path_to_save_csv>
CSV file will be saved in <path_to_save_csv>
3. Create DeepLearinng Model to predict Human Pose
-
-i,--dataset__ Path to CSV Data -
-o,--save__ Path to save model.h5 file
python3 poseModel.py -i <path_to_save_csv> -o <path_to_save_model>
Model will saved in <path_to_save_model> and Model Metrics saved in metrics.png
4. Inference
Open inference.py
change Line-43: According to your Class Names, Write Class Order
Show Predicted Pose Class on Test Image or Video or Web-cam
-
-m,--model__ Path to saved model.h5 -
-c,--conf__ Min prediction conf to detect pose class (0<conf<1) -
-i,--source__ Path to Image or Video, for webcam use Zero(0) -
--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
Thank You…
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