Update app.py

app.py

@@ -7,68 +7,51 @@ import gradio as gr
 from ultralytics import YOLO
 from paddleocr import PaddleOCR
 
-# ─── 1) Load models ───────────────────────────────────────────────
+# 1) Load models
 yolo = YOLO("models/best.pt")
 ocr  = PaddleOCR(
-    det=False,                    # <<< OCR’nin kendi detector’ını tamamen kapatıyoruz
-    rec=True,
+    text_detection_model_dir=None,
     text_recognition_model_dir="models/ocr_model",
     use_textline_orientation=False,
     lang="en"
 )
 
-# ─── 2) Turkish plate formatter ────────────────────────────────────
+# 2) Turkish plate formatter
 def format_turkish_plate(s: str) -> str:
     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"
 
-# ─── 3) OCR çıktısını normalize et ─────────────────────────────────
-def extract_text_score(recs):
-    if not recs:
-        return "", 0.0
-    first = recs[0]
-    # format: [text, score]
-    if isinstance(first, (list,tuple)) and len(first)==2 and isinstance(first[0], str):
-        text, score = first[0], first[1]
-    # format: [[...], (text,score)] veya [[...], [text,score]]
-    elif isinstance(first, (list,tuple)) and len(first)==2 and isinstance(first[1], (list,tuple)):
-        text, score = first[1][0], first[1][1]
-    else:
-        return "", 0.0
-    try:
-        score = float(score)
-    except:
-        score = 0.0
-    return text, score
-
-# ─── 4) Single‐image inference ─────────────────────────────────────
+# 3) Single‐image inference
 def run_image(img, conf=0.25):
     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
+    for box, yolo_score in zip(res.boxes.xyxy.cpu().numpy(),
+                               res.boxes.conf.cpu().numpy()):
+        x1,y1,x2,y2 = box.astype(int)
         crop = out[y1:y2, x1:x2]
-        if crop.size == 0: 
-            continue
+        if crop.size==0: continue
 
         plate_img = cv2.resize(crop, (128,32))
-        # <<< mutlaka det=False veriyoruz
-        recs = ocr.ocr(plate_img, det=False, cls=False)
+        recs = ocr.ocr(plate_img, cls=False)
+
+        if recs and recs[0]:
+            _, (raw, ocr_score) = recs[0]
+        else:
+            raw, ocr_score = "", 0.0
 
-        raw, score = extract_text_score(recs)
         plate = format_turkish_plate(raw)
-        label = f"{plate} ({score:.2f})"
+        label = f"{plate} ({ocr_score:.2f})"
 
         cv2.rectangle(out, (x1,y1),(x2,y2), (0,255,0), 2)
-        cv2.putText(out, label, (x1, y1-5),
+        cv2.putText(out, label, (x1,y1-5),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
 
     return cv2.cvtColor(out, cv2.COLOR_BGR2RGB), f"{len(res.boxes)} plate(s) detected"
 
-# ─── 5) Video inference ────────────────────────────────────────────
+# 4) Video inference (same pattern)
 def run_video(video_file, conf=0.25):
     cap = cv2.VideoCapture(video_file)
     fps = cap.get(cv2.CAP_PROP_FPS)
@@ -86,21 +69,21 @@ def run_video(video_file, conf=0.25):
         res = yolo(frame, conf=conf)[0]
         for (x1,y1,x2,y2) in res.boxes.xyxy.cpu().numpy().astype(int):
             crop = frame[y1:y2, x1:x2]
-            if crop.size == 0:
-                continue
-
+            if crop.size==0: continue
             plate_img = cv2.resize(crop, (128,32))
-            recs = ocr.ocr(plate_img, det=False, cls=False)
+            recs = ocr.ocr(plate_img, cls=False)
+
+            if recs and recs[0]:
+                _, (raw, ocr_score) = recs[0]
+            else:
+                raw, ocr_score = "", 0.0
 
-            raw, score = extract_text_score(recs)
             plate = format_turkish_plate(raw)
             if plate != "Unknown":
-                records.append({"time_s":round(t,2),
-                                "plate":plate,
-                                "conf":round(score,3)})
+                records.append({"time_s":round(t,2), "plate":plate, "conf":round(ocr_score,3)})
 
             cv2.rectangle(frame, (x1,y1),(x2,y2), (0,255,0), 2)
-            cv2.putText(frame, plate, (x1, y1-5),
+            cv2.putText(frame, plate, (x1,y1-5),
                         cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
 
         writer.write(frame)
@@ -108,25 +91,25 @@ def run_video(video_file, conf=0.25):
     cap.release(); writer.release()
     with open("output.json","w") as f:
         json.dump(records, f, indent=2)
-    return outfp, "Done"
+    return outfp
 
-# ─── 6) Gradio UI ─────────���────────────────────────────────────────
+# 5) Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("## 🚗 License Plate Detection + Recognition")
     with gr.Row():
         with gr.Column():
-            img_in = gr.Image(type="numpy", label="Upload Image")
-            vid_in = gr.File(label="Upload Video (.mp4)")
-            conf   = gr.Slider(0,1,0.25,0.01, label="YOLO Confidence")
+            img_in = gr.Image(type="numpy")
+            vid_in = gr.File(label="Video (.mp4)")
+            conf   = gr.Slider(0,1,0.25,0.01)
             b1     = gr.Button("Run Image")
             b2     = gr.Button("Run Video")
         with gr.Column():
-            img_out = gr.Image(type="numpy", label="Annotated Image")
-            vid_out = gr.Video(label="Annotated Video")
-            status  = gr.Textbox(label="Status / JSON Path")
+            img_out = gr.Image(type="numpy")
+            vid_out = gr.Video()
+            status  = gr.Textbox()
 
     b1.click(run_image, [img_in,conf], [img_out,status])
     b2.click(run_video, [vid_in,conf], [vid_out,status])
 
 if __name__=="__main__":
-    demo.launch()
+    demo.launch()