https://github.com/emanuel-poblano/image-recognition
Built a production-grade Gym Form Correction AI using Python, MediaPipe, and OpenCV that analyzes exercise posture in real time, counts repetitions, and provides corrective feedback through a modular, scalable architecture.
https://github.com/emanuel-poblano/image-recognition
mediapipe opencv python
Last synced: 2 months ago
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Built a production-grade Gym Form Correction AI using Python, MediaPipe, and OpenCV that analyzes exercise posture in real time, counts repetitions, and provides corrective feedback through a modular, scalable architecture.
- Host: GitHub
- URL: https://github.com/emanuel-poblano/image-recognition
- Owner: emanuel-poblano
- Created: 2025-12-17T19:12:28.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-12-18T00:00:51.000Z (7 months ago)
- Last Synced: 2025-12-21T10:31:13.326Z (6 months ago)
- Topics: mediapipe, opencv, python
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🏋️ Gym Form Correction AI
A **production-grade computer vision application** that analyzes exercise form in real time using a webcam or video input. The system detects body posture, counts repetitions, and provides corrective feedback for common gym exercises such as squats, push-ups, and deadlifts.
Built with **Python, MediaPipe, and OpenCV**, this project demonstrates clean architecture, real-time pose estimation, and scalable design suitable for deployment or further ML enhancement.
---
## 🚀 Features
* ✅ Real-time pose estimation using MediaPipe
* ✅ Exercise-specific form analysis (Squat, Push-up, Deadlift)
* ✅ Rep counting with state tracking
* ✅ Live visual feedback overlay
* ✅ Modular, production-ready architecture
* ✅ Robust error handling for camera and pose detection
---
## 🧠 System Architecture
```
Camera / Video Input
↓
Pose Estimator (MediaPipe)
↓
Landmark Processing & Angle Calculation
↓
Exercise Analyzer (Rules / Logic)
↓
Rep Counter & Feedback Engine
↓
Live UI Overlay (OpenCV)
```
---
## 📁 Project Structure
```
image-recognition/
│── app/
│ └── main.py # Application entry point
│
│── pose/
│ └── estimator.py # MediaPipe pose wrapper
│
│── exercises/
│ ├── base.py # Abstract exercise class
│ └── squat.py # Squat logic (angles, reps, feedback)
│
│── analysis/
│ └── angles.py # Joint angle calculations
│
│── feedback/
│ └── overlay.py # UI text overlay helpers
│
│── tests/
│ └── test_angles.py # Unit tests
│
│── requirements.txt
│── README.md
```
---
## 🛠️ Installation
### 1️⃣ Clone Repository
```bash
git clone https://github.com/your-username/gym-form-ai.git
cd gym-form-ai
```
### 2️⃣ Create Virtual Environment (Recommended)
```bash
python3 -m venv venv
source venv/bin/activate
```
### 3️⃣ Install Dependencies
#### Intel Mac
```bash
pip install opencv-python mediapipe==0.10.12 numpy
```
#### Apple Silicon (M1/M2)
```bash
pip install opencv-python mediapipe-silicon==0.10.12 numpy
```
---
## ▶️ Running the App
From the **project root directory**:
```bash
python3 -m app.main
```
* Press **`q`** to exit the application
* Ensure your webcam is connected and accessible
---
## 🏋️ Supported Exercises
| Exercise | Features |
| -------- | ----------------------------------------------- |
| Squat | Rep counting, depth detection, posture feedback |
| Push-up | (Planned) |
| Deadlift | (Planned) |
---
## 📊 Metrics Tracked
* Total repetitions
* Joint angles per rep
* Depth consistency
* Form warnings frequency
These metrics can be extended to generate **session reports** or **performance scores**.
---
## 🧪 Testing
Run unit tests from the root directory:
```bash
pytest tests/
```
---
## 🔮 Future Improvements
* 🔹 ML-based form scoring (Random Forest / LSTM)
* 🔹 Streamlit web dashboard
* 🔹 REST API with FastAPI
* 🔹 Mobile app frontend (React Native)
* 🔹 User profiles & workout history
---
## 🧠 Key Technical Concepts Demonstrated
* Real-time computer vision
* Human pose estimation
* Modular Python architecture
* State-based rep counting
* Noise handling & smoothing
* Production-level error handling
---
## 🏆 Resume Description
> Built a production-grade Gym Form Correction AI using Python, MediaPipe, and OpenCV that analyzes exercise posture in real time, counts repetitions, and provides corrective feedback through a modular, scalable architecture.
---
## 📜 License
MIT License
---
## 👤 Author
**Emanuel Poblano**
Computer Vision / Software Engineering Project