Upload processor/redaction.py with huggingface_hub

processor/redaction.py

@@ -0,0 +1,42 @@
+import cv2
+import easyocr
+import numpy as np
+
+# Initialize the EasyOCR reader (loads model on first run)
+reader = easyocr.Reader(['en'], gpu=False)  # Set gpu=True if applicable
+
+def redact_names(img: np.ndarray) -> np.ndarray:
+    """
+    Scans the image for text. If it finds text, we apply cv2.inpaint
+    to seamlessly remove the detected text, mimicking "redaction" without a flat background block.
+    """
+    # Create an initial mask of zeros
+    mask = np.zeros(img.shape[:2], dtype=np.uint8)
+    
+    h, w = img.shape[:2]
+    
+    # Run OCR on the image
+    results = reader.readtext(img)
+    
+    has_text = False
+    for (bbox, text, prob) in results:
+        # Bounding box points: top_left, top_right, bottom_right, bottom_left
+        (tl, tr, br, bl) = bbox
+        tl = (int(tl[0]), int(tl[1]))
+        br = (int(br[0]), int(br[1]))
+        tr = (int(tr[0]), int(tr[1]))
+        
+        # Heuristic: Redact text only if it's in the typical name region
+        # e.g., Bottom 35% of the card, Left 65% of the card
+        if tl[1] > h * 0.65 and tr[0] < w * 0.65:
+            has_text = True
+            pad = 8  # Slightly wider pad for clean redaction
+            cv2.rectangle(mask, (max(0, tl[0]-pad), max(0, tl[1]-pad)), (min(w, br[0]+pad), min(h, br[1]+pad)), 255, -1)
+
+    if has_text:
+        # Inpaint to remove the text seamlessly using the nearby background
+        # Use cv2.INPAINT_TELEA or cv2.INPAINT_NS
+        result = cv2.inpaint(img, mask, inpaintRadius=7, flags=cv2.INPAINT_TELEA)
+        return result
+        
+    return img