clahe

app.py

@@ -250,7 +250,6 @@
 # if __name__ == "__main__":
 #     demo.launch()
 
-
 import gradio as gr
 import cv2
 import numpy as np
@@ -425,6 +424,24 @@ class EnginePartDetector:
 
         return vis_img, cropped_img, stats_text
 
+    @staticmethod
+    def enhance_roi(roi: np.ndarray) -> np.ndarray:
+        """Apply high-contrast CLAHE to highlight blurred lines/features."""
+        if roi is None or roi.size == 0:
+            return roi
+        
+        # Convert to LAB space to apply CLAHE on L (luminance) channel
+        lab = cv2.cvtColor(roi, cv2.COLOR_RGB2LAB)
+        l, a, b = cv2.split(lab)
+        
+        # ClipLimit 10.0 provides very high contrast as requested
+        clahe = cv2.createCLAHE(clipLimit=10.0, tileGridSize=(8, 8))
+        cl = clahe.apply(l)
+        
+        merged = cv2.merge((cl, a, b))
+        enhanced = cv2.cvtColor(merged, cv2.COLOR_LAB2RGB)
+        return enhanced
+
     # ── Internal Helpers ──────────────────────────────────────────────────────
 
     @staticmethod
@@ -449,11 +466,14 @@ class EnginePartDetector:
         if "❌" in log or "⚠️" in log:
             return log, None
 
+        # Enhance ROI for better feature matching
+        roi_enhanced = self.enhance_roi(roi)
+
         # Layer 2
-        features = self.feature_extractor.extract(roi)
+        features = self.feature_extractor.extract(roi_enhanced)
         self.templates[part_name] = {
             "features": features,
-            "roi": roi
+            "roi": roi_enhanced
         }
         self._persist_templates()
 
@@ -474,8 +494,11 @@ class EnginePartDetector:
         if "❌" in log or "⚠️" in log:
             return log, None, vis, None
 
+        # Enhance ROI for better feature matching
+        roi_enhanced = self.enhance_roi(roi)
+
         # Layer 2
-        query_feat = self.feature_extractor.extract(roi)
+        query_feat = self.feature_extractor.extract(roi_enhanced)
 
         # Matching logic
         scores = []
@@ -504,7 +527,7 @@ class EnginePartDetector:
                 lines.append(f"  • `{name}`: {sim:.3f}")
 
         label_dict = {name: float(sim) for name, sim in scores[:5]}
-        return "\n".join(lines), label_dict, vis, roi
+        return "\n".join(lines), label_dict, vis, roi_enhanced
 
     def get_template_roi(self, part_name: str) -> np.ndarray | None:
         if part_name in self.templates: