Update app.py

app.py

@@ -49,50 +49,68 @@ def normalize_ocr(recs):
         return first[1][0], float(first[1][1])
     return "", 0.0
 
+# Plaka format kontrolü (Türk plakası değilse orijinal yazı korunur)
 def format_plate(s: str) -> str:
-    """‘DD AAA DDDD’  veya Unknown"""
     s = re.sub(r'[^A-Z0-9]', '', s.upper())
     m = re.match(r'^(\d{2})([A-Z]{1,3})(\d{2,4})$', s)
-    return f"{m.group(1)} {m.group(2)} {m.group(3)}" if m else "Unknown"
-
-# ─── 5) Single-image inference
+    return f"{m.group(1)} {m.group(2)} {m.group(3)}" if m else f"RAW: {s}" if s else "Unknown"
+
+# Perspektif düzeltme (tek sıra plaka kenarı düz olmayan durumlar için)
+def correct_perspective(image, box):
+    x1, y1, x2, y2 = box
+    h, w = image.shape[:2]
+    margin = 5  # biraz dışarıdan al
+
+    x1 = max(0, x1 - margin)
+    y1 = max(0, y1 - margin)
+    x2 = min(w, x2 + margin)
+    y2 = min(h, y2 + margin)
+
+    crop = image[y1:y2, x1:x2]
+    if crop.size == 0: return None
+
+    # OCR input boyutuna perspektif düzeltme
+    src_pts = np.float32([
+        [0, 0], [crop.shape[1], 0],
+        [crop.shape[1], crop.shape[0]], [0, crop.shape[0]]
+    ])
+    dst_pts = np.float32([
+        [0, 0], [128, 0],
+        [128, 32], [0, 32]
+    ])
+    M = cv2.getPerspectiveTransform(src_pts, dst_pts)
+    warped = cv2.warpPerspective(crop, M, (128, 32))
+    return warped
+
+# Güncellenmiş image fonksiyonu
 def run_image(img, conf=0.25):
-    # YOLO wants BGR
     bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
     res = yolo(bgr, conf=conf)[0]
     out = bgr.copy()
 
     for box in res.boxes.xyxy.cpu().numpy().astype(int):
-        x1,y1,x2,y2 = box
-        crop = out[y1:y2, x1:x2]
-        if crop.size == 0:
-            continue
-
-        # resize to OCR input
-        plate_img = cv2.resize(crop, (128,32))
-        # safe OCR
+        warped = correct_perspective(out, box)
+        if warped is None: continue
+
         try:
-            recs = ocr.ocr(plate_img, det=False, cls=True)
-        except Exception:
+            recs = ocr.ocr(warped, det=False, cls=True)
+        except:
             recs = []
         txt, score = normalize_ocr(recs)
         plate = format_plate(txt)
         label = f"{plate} ({score:.2f})"
 
-        # draw
-        cv2.rectangle(out, (x1,y1),(x2,y2), (0,255,0), 2)
-        cv2.putText(out, label, (x1, y1-8),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
+        x1, y1, x2, y2 = box
+        cv2.rectangle(out, (x1, y1), (x2, y2), (0,255,0), 2)
+        cv2.putText(out, label, (x1, y1-8), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
 
     return cv2.cvtColor(out, cv2.COLOR_BGR2RGB), f"{len(res.boxes)} plate(s) detected"
 
-# ─── 6) Video inference
+# Güncellenmiş video fonksiyonu
 def run_video(video_file, conf=0.25):
     cap = cv2.VideoCapture(video_file)
     fps = cap.get(cv2.CAP_PROP_FPS) or 30
-    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
+    w, h = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     out_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
     writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w,h))
     records, idx = [], 0
@@ -101,24 +119,27 @@ def run_video(video_file, conf=0.25):
         ret, frame = cap.read()
         if not ret: break
         idx += 1; t = idx/fps
-
         res = yolo(frame, conf=conf)[0]
+
         for box in res.boxes.xyxy.cpu().numpy().astype(int):
-            x1,y1,x2,y2 = box
-            crop = frame[y1:y2, x1:x2]
-            if crop.size == 0: continue
+            warped = correct_perspective(frame, box)
+            if warped is None: continue
 
-            plate_img = cv2.resize(crop, (128,32))
             try:
-                recs = ocr.ocr(plate_img, det=False, cls=True)
+                recs = ocr.ocr(warped, det=False, cls=True)
             except:
                 recs = []
             txt, score = normalize_ocr(recs)
             plate = format_plate(txt)
+            if plate.startswith("RAW:"):
+                raw_txt = plate[5:]
+            else:
+                raw_txt = plate
 
-            if plate != "Unknown":
-                records.append({"time_s":round(t,2),"plate":plate,"conf":round(score,3)})
+            if raw_txt != "Unknown":
+                records.append({"time_s":round(t,2),"plate":raw_txt,"conf":round(score,3)})
 
+            x1, y1, x2, y2 = box
             cv2.rectangle(frame, (x1,y1),(x2,y2), (0,255,0), 2)
             cv2.putText(frame, plate, (x1, y1-8),
                         cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
@@ -130,6 +151,7 @@ def run_video(video_file, conf=0.25):
         json.dump(records, f, indent=2)
     return out_path, "Done"
 
+
 # ─── 7) Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("## 🚗 License Plate Detection + Recognition")