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# CSRNet Model Integration Guide
## Quick Integration Steps
### 1. Copy Required Files
Copy these files to your project:
```
models/Model.json # Model architecture
weights/model_A.weights.h5 # Trained weights
```
### 2. Install Dependencies
```bash
pip install tensorflow keras numpy pillow opencv-python h5py
```
### 3. Integration Code
```python
import numpy as np
from PIL import Image
from keras.models import model_from_json
def load_csrnet_model(model_path='models/Model.json', weights_path='weights/model_A.weights.h5'):
"""Load the trained CSRNet model"""
with open(model_path, 'r') as f:
model = model_from_json(f.read())
model.load_weights(weights_path)
return model
def preprocess_image(image_path):
"""Preprocess image for CSRNet"""
im = Image.open(image_path).convert('RGB')
im = np.array(im) / 255.0
# Normalize with ImageNet mean/std
im[:,:,0] = (im[:,:,0] - 0.485) / 0.229
im[:,:,1] = (im[:,:,1] - 0.456) / 0.224
im[:,:,2] = (im[:,:,2] - 0.406) / 0.225
return np.expand_dims(im, axis=0)
def predict_crowd_count(model, image_path):
"""Predict crowd count from image"""
image = preprocess_image(image_path)
density_map = model.predict(image)
count = int(np.sum(density_map))
return count, density_map
# Usage Example
model = load_csrnet_model()
count, heatmap = predict_crowd_count(model, 'test_image.jpg')
print(f"Predicted crowd count: {count}")
```
### 4. API Integration (Flask Example)
```python
from flask import Flask, request, jsonify
import base64
import io
app = Flask(__name__)
model = load_csrnet_model()
@app.route('/predict', methods=['POST'])
def predict():
file = request.files['image']
img = Image.open(file.stream)
img.save('temp.jpg')
count, _ = predict_crowd_count(model, 'temp.jpg')
return jsonify({'crowd_count': count})
if __name__ == '__main__':
app.run(port=5000)
```
### 5. Web Integration (JavaScript)
```javascript
async function predictCrowdCount(imageFile) {
const formData = new FormData();
formData.append('image', imageFile);
const response = await fetch('http://localhost:5000/predict', {
method: 'POST',
body: formData
});
const result = await response.json();
console.log('Crowd count:', result.crowd_count);
}
```
## Important Notes
⚠️ **Model Accuracy**: Current model trained with only 5 epochs × 20 steps. For production:
- Retrain with 50+ epochs × 200+ steps
- Or download pre-trained weights from the original repo
## File Structure
```
your_project/
├── models/
│ └── Model.json
├── weights/
│ └── model_A.weights.h5
└── app.py
```
## Performance Tips
- Load model once at startup (not per request)
- Use GPU for faster inference
- Resize large images before prediction
- Cache model in memory for web apps
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