Upload README_ONNX.md

README_ONNX.md

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+# Hướng dẫn sử dụng DeepPrint ONNX Model
+
+File ONNX model này chỉ chứa **DeepPrint model** (không có LoFTR). LoFTR là bước tiền xử lý riêng biệt.
+
+## File ONNX Model
+
+- **Đường dẫn**: `onnx_models/deepprint_fvc_db1_loftr_final.onnx`
+- **Input**: `input_image` - shape `[batch_size, 1, 299, 299]` (grayscale image)
+- **Outputs**: 
+  - `texture_embeddings`: shape `[batch_size, 256]`
+  - `minutia_embeddings`: shape `[batch_size, 256]`
+
+## Cài đặt
+
+```bash
+pip install onnxruntime
+```
+
+## Cách sử dụng
+
+### 1. Sử dụng script có sẵn
+
+```bash
+# Extract embedding từ 1 ảnh
+python use_onnx_model.py --image1 path/to/fingerprint.tif
+
+# So sánh 2 ảnh fingerprint
+python use_onnx_model.py --image1 path/to/image1.tif --image2 path/to/image2.tif --threshold 0.5
+```
+
+### 2. Sử dụng trong code Python
+
+```python
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+import torchvision.transforms.functional as VTF
+import sys
+
+# Add DeepPrint path
+sys.path.append("deepprint/fixed-length-fingerprint-extractors")
+from flx.image_processing.binarization import LazilyAllocatedBinarizer
+from flx.data.image_helpers import pad_and_resize_to_deepprint_input_size
+
+# Load ONNX model
+onnx_model_path = "onnx_models/deepprint_fvc_db1_loftr_final.onnx"
+session = ort.InferenceSession(onnx_model_path)
+
+# Preprocess image
+def preprocess_image(image_path):
+    img = Image.open(image_path).convert('L')
+    img_tensor = VTF.to_tensor(img)
+    
+    # Binarize fingerprint
+    binarizer = LazilyAllocatedBinarizer(5.0)
+    img_tensor = binarizer(img_tensor)
+    
+    # Pad to square and resize to 299x299
+    img_tensor = pad_and_resize_to_deepprint_input_size(img_tensor)
+    img_tensor = img_tensor.unsqueeze(0)  # Add batch dimension
+    
+    return img_tensor.numpy().astype(np.float32)
+
+# Extract embeddings
+input_tensor = preprocess_image("path/to/fingerprint.tif")
+outputs = session.run(['texture_embeddings', 'minutia_embeddings'], 
+                      {'input_image': input_tensor})
+
+texture_emb = outputs[0][0]  # [256]
+minutia_emb = outputs[1][0]  # [256]
+combined_emb = np.concatenate([texture_emb, minutia_emb])  # [512]
+
+# Tính similarity giữa 2 embeddings
+def cosine_similarity(emb1, emb2):
+    emb1_norm = emb1 / (np.linalg.norm(emb1) + 1e-8)
+    emb2_norm = emb2 / (np.linalg.norm(emb2) + 1e-8)
+    return np.dot(emb1_norm, emb2_norm)
+
+similarity = cosine_similarity(combined_emb1, combined_emb2)
+print(f"Similarity: {similarity:.6f}")
+```
+
+## Preprocessing Pipeline
+
+Model yêu cầu preprocessing theo thứ tự sau:
+
+1. **Load image**: PIL Image (grayscale)
+2. **Convert to tensor**: `VTF.to_tensor()` → shape `[1, H, W]`
+3. **Binarize**: `LazilyAllocatedBinarizer(5.0)` → enhance fingerprint ridges
+4. **Pad & Resize**: `pad_and_resize_to_deepprint_input_size()` → `[1, 299, 299]`
+5. **Add batch dimension**: `unsqueeze(0)` → `[1, 1, 299, 299]`
+
+