hoàn thành chức năng remove cloud train

2026-01-26 13:44:55 +07:00
parent 61646de647
commit 5404f7393c
17 changed files with 4337 additions and 153 deletions
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+# Cloud Removal Model Upload Feature
+
+## Overview
+Added functionality to upload and use custom deep learning cloud removal models (.pth files) during prediction.
+
+## Features Implemented
+
+### 1. API Endpoints
+
+#### Upload Cloud Removal Model
+```
+POST /api/cloud-removal/upload
+```
+- Upload `.pth` cloud removal model files
+- Validates file extension (.pth only)
+- Security checks for filename
+- Returns file info (name, size)
+
+**Example:**
+```bash
+curl -X POST -F "file=@cloud_removal_unet_best.pth" \
+  http://localhost:8000/api/cloud-removal/upload
+```
+
+#### List Cloud Removal Models
+```
+GET /api/cloud-removal/models
+```
+Already existing - lists all `.pth` models in `model_train/` directory
+
+#### Delete Cloud Removal Model
+```
+DELETE /api/cloud-removal/models/{filename}
+```
+Already existing - deletes a specific cloud removal model
+
+### 2. Prediction Configuration Updates
+
+#### PredictionConfig
+Added new optional field:
+```python
+cloud_removal_model: Optional[str] = None  # .pth filename
+```
+
+#### PredictionWithNDVIConfig  
+Added new optional field:
+```python
+cloud_removal_model: Optional[str] = None  # .pth filename
+```
+
+### 3. Prediction Function Integration
+
+The `run_prediction()` function now:
+1. Accepts `cloud_removal_model` parameter
+2. Passes model path to `process_cloud_removal()`
+3. Logs which model is being used
+
+**Code:**
+```python
+cloud_removal_method = config.cloud_removal_method
+cloud_removal_model = config.cloud_removal_model
+
+s2_data, cloud_metadata = process_cloud_removal(
+    s2_data=s2_data,
+    method=cloud_removal_method,
+    model_path=f"model_train/{cloud_removal_model}" if cloud_removal_model else None,
+    verbose=True
+)
+```
+
+### 4. Web Interface Updates
+
+#### Upload Button
+- Added file input in "Deep Learning" cloud removal section
+- Upload button appears when "Deep Learning" method is selected
+- Real-time upload status feedback
+- Auto-refreshes model list after successful upload
+
+#### Model Selection
+- Dropdown shows all available `.pth` models
+- Auto-selects newly uploaded model
+- Shows model metadata (epoch, loss)
+
+## Usage Guide
+
+### Step 1: Train or Obtain a Cloud Removal Model
+Train using the cloud training interface or obtain a pre-trained `.pth` model.
+
+### Step 2: Upload Model
+1. Go to Prediction Interface
+2. Scroll to "Cloud Removal Method" section
+3. Select "Deep Learning (U-Net)" from dropdown
+4. Model upload section appears
+5. Click "📤 Upload Cloud Removal Model (.pth)"
+6. Select your `.pth` file
+7. Wait for upload confirmation
+
+### Step 3: Use Model in Prediction
+1. The uploaded model is automatically selected
+2. Configure other prediction parameters (bbox, dates, etc.)
+3. Click "🚀 Start Prediction (với NDVI)"
+4. The system will use your custom model for cloud removal
+
+## File Structure
+```
+model_train/
+├── cloud_removal_unet_best.pth         # User uploaded
+├── cloud_removal_unet_epoch_10.pth     # User uploaded
+├── model_mobilenet-lraspp_*.joblib     # Land classification models
+└── ...
+```
+
+## API Request Example
+
+### Using Uploaded Model
+```json
+{
+  "model_filename": "model_mobilenet-lraspp_20260105_225459.joblib",
+  "min_lon": 105.80,
+  "min_lat": 10.00,
+  "max_lon": 105.82,
+  "max_lat": 10.02,
+  "start_date": "2024-01-15",
+  "end_date": "2024-01-17",
+  "max_scenes": 3,
+  "cloud_cover": 30,
+  "resolution": 20,
+  "use_gpu": true,
+  "export_ndvi": true,
+  "export_classification": true,
+  "cloud_removal_method": "deep",
+  "cloud_removal_model": "cloud_removal_unet_best.pth"
+}
+```
+
+### Without Custom Model (Classical Methods)
+```json
+{
+  ...
+  "cloud_removal_method": "hybrid",
+  "cloud_removal_model": null
+}
+```
+
+## Security Features
+- Filename validation (no path traversal)
+- File extension validation (.pth only)
+- File existence checks
+- Duplicate filename detection
+
+## Error Handling
+- Invalid file type → 400 Bad Request
+- Duplicate filename → 400 Bad Request  
+- Upload failure → 500 Internal Server Error
+- Missing model when "deep" selected → Falls back to "hybrid" method
+
+## Notes
+- Uploaded models are stored in `model_train/` directory
+- Models must be PyTorch `.pth` files
+- Compatible with `cloud_removal.py` module
+- Works with both `/api/prediction/start` and `/api/predict/with-ndvi` endpoints
+
+## Testing
+
+### Test Upload
+```bash
+# Upload a model
+curl -X POST -F "file=@my_cloud_model.pth" \
+  http://localhost:8000/api/cloud-removal/upload
+
+# List models
+curl http://localhost:8000/api/cloud-removal/models
+
+# Delete model
+curl -X DELETE \
+  http://localhost:8000/api/cloud-removal/models/my_cloud_model.pth
+```
+
+### Test Prediction
+```bash
+curl -X POST http://localhost:8000/api/predict/with-ndvi \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model_filename": "model_mobilenet-lraspp_20260105_225459.joblib",
+    "min_lon": 105.80, "min_lat": 10.00,
+    "max_lon": 105.82, "max_lat": 10.02,
+    "start_date": "2024-01-15", "end_date": "2024-01-17",
+    "max_scenes": 2, "cloud_cover": 30, "resolution": 20,
+    "use_gpu": false, "export_ndvi": true,
+    "cloud_removal_method": "deep",
+    "cloud_removal_model": "cloud_removal_unet_best.pth"
+  }'
+```
+
+## Future Enhancements
+- Model metadata display (architecture, training date)
+- Model validation on upload
+- Multiple model format support (.pt, .onnx)
+- Model performance metrics
+- Batch upload support