Update README.md

README.md

@@ -137,6 +137,84 @@ with torch.no_grad():
     outputs = model(**inputs)
 ```
 
+## Coordinate Mapping
+
+The processor returns coordinate mapping information that allows you to map coordinates from the processed image back to the original image space. This is useful for applications like lesion detection, where you need to annotate or visualize detected features on the original image.
+
+### Output Format
+
+The processor returns these additional keys:
+- `scale_x`, `scale_y`: Scale factors for coordinate mapping (shape: `(B,)`)
+- `offset_x`, `offset_y`: Offset values for coordinate mapping (shape: `(B,)`)
+
+### Mapping Formula
+
+To map coordinates from the processed image back to original coordinates:
+
+```python
+orig_x = offset_x + cropped_x * scale_x
+orig_y = offset_y + cropped_y * scale_y
+```
+
+Where `cropped_x` and `cropped_y` are coordinates in the processed image (range: [0, size-1]).
+
+### Example: Single Point Mapping
+
+```python
+from PIL import Image
+
+# Process image
+processor = AutoImageProcessor.from_pretrained("iszt/eye-clahe-processor", trust_remote_code=True)
+image = Image.open("fundus.jpg")
+outputs = processor(image, return_tensors="pt")
+
+# Detected point in processed image (e.g., from a model prediction)
+detected_x, detected_y = 100.0, 150.0
+
+# Map back to original image coordinates
+orig_x = outputs['offset_x'] + detected_x * outputs['scale_x']
+orig_y = outputs['offset_y'] + detected_y * outputs['scale_y']
+
+print(f"Original coordinates: ({orig_x.item():.2f}, {orig_y.item():.2f})")
+```
+
+### Example: Multiple Points in Batch
+
+```python
+import torch
+
+# Process batch of images
+images = [Image.open(f"image_{i}.jpg") for i in range(4)]
+outputs = processor(images, return_tensors="pt")
+
+# Detected points for each image (B, N, 2) where N is number of points
+detected_points = torch.tensor([
+    [[50.0, 60.0], [100.0, 120.0]],  # Image 0: 2 points
+    [[75.0, 80.0], [150.0, 160.0]],  # Image 1: 2 points
+    [[90.0, 95.0], [180.0, 190.0]],  # Image 2: 2 points
+    [[65.0, 70.0], [130.0, 140.0]],  # Image 3: 2 points
+])
+
+# Map all points back to original coordinates
+B, N, _ = detected_points.shape
+scale_x = outputs['scale_x'].view(B, 1, 1)
+scale_y = outputs['scale_y'].view(B, 1, 1)
+offset_x = outputs['offset_x'].view(B, 1, 1)
+offset_y = outputs['offset_y'].view(B, 1, 1)
+
+orig_x = offset_x + detected_points[..., 0:1] * scale_x
+orig_y = offset_y + detected_points[..., 1:2] * scale_y
+
+original_points = torch.cat([orig_x, orig_y], dim=-1)  # (B, N, 2)
+```
+
+### Use Cases
+
+- **Lesion Detection**: Map detected lesion coordinates back for visualization
+- **Optic Disc Localization**: Track anatomical landmarks through preprocessing
+- **Vessel Segmentation**: Align segmentation masks with original images
+- **Quality Control**: Verify feature alignment across processing pipeline
+
 ## Technical Details
 
 ### Eye Center Detection