Update app.py

app.py

@@ -9,61 +9,56 @@ from paddleocr import PaddleOCR
 
 # ─── 1) Load models ───────────────────────────────────────────────
 yolo = YOLO("models/best.pt")
-
-ocr = PaddleOCR(
-    det=False,                            # OCR'nin kendi detector'ünü kapat
-    rec=True,                             # sadece recognition
-    rec_model_dir="models/ocr_model",     # içinde inference.pdmodel / .pdparams / .yml var
-    rec_image_shape="3,32,128",           # height=32, width=128
-    rec_char_dict_path=(
-        "/usr/local/lib/python3.10/site-packages/"
-        "paddleocr/ppocr/utils/ppocr_keys_v1.txt"
-    ),                                     # doğru dict dosyası
-    use_angle_cls=False,                  # açı sınıflandırmasını kapat (opsiyonel)
-    use_space_char=True                   # boşluk karakterini destekle
+ocr  = PaddleOCR(
+    det=False,                    # <<< OCR’nin kendi detector’ını tamamen kapatıyoruz
+    rec=True,
+    text_recognition_model_dir="models/ocr_model",
+    use_textline_orientation=False,
+    lang="en"
 )
 
-# ─── 2) 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"
 
-# ─── Helper: OCR çıktısını normalize et ────────────────────────────
+# ─── 3) OCR çıktısını normalize et ─────────────────────────────────
 def extract_text_score(recs):
-    """
-    recs (det=False) → [ [text,score], … ]
-    recs (det=True)  → [ [[text,score],…], … ]
-    """
     if not recs:
         return "", 0.0
     first = recs[0]
-    # det=False formatı
+    # format: [text, score]
     if isinstance(first, (list,tuple)) and len(first)==2 and isinstance(first[0], str):
-        return first[0], float(first[1])
-    # det=True formatı
-    if isinstance(first, list) and first and isinstance(first[0], (list,tuple)):
-        return first[0][0], float(first[0][1])
-    return "", 0.0
+        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
 
-# ─── 3) Single‐image inference ─────────────────────────────────────
+# ─── 4) 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
         crop = out[y1:y2, x1:x2]
-        if crop.size==0:
+        if crop.size == 0: 
             continue
 
         plate_img = cv2.resize(crop, (128,32))
-        try:
-            recs = ocr.ocr(plate_img)  # artık det=False, rec_image_shape, dict hep init'de
-        except Exception:
-            recs = []
-        raw, score = extract_text_score(recs)
+        # <<< mutlaka det=False veriyoruz
+        recs = ocr.ocr(plate_img, det=False, cls=False)
 
+        raw, score = extract_text_score(recs)
         plate = format_turkish_plate(raw)
         label = f"{plate} ({score:.2f})"
 
@@ -73,38 +68,36 @@ def run_image(img, conf=0.25):
 
     return cv2.cvtColor(out, cv2.COLOR_BGR2RGB), f"{len(res.boxes)} plate(s) detected"
 
-# ─── 4) Video inference ────────────────────────────────────────────
+# ─── 5) Video inference ────────────────────────────────────────────
 def run_video(video_file, conf=0.25):
     cap = cv2.VideoCapture(video_file)
     fps = cap.get(cv2.CAP_PROP_FPS)
-    w  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    h  = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    tmp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
-    writer  = cv2.VideoWriter(tmp_out, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w,h))
+    w,h = int(cap.get(3)), int(cap.get(4))
+    outfp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    writer = cv2.VideoWriter(outfp, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w,h))
     records, idx = [], 0
 
     while True:
         ret, frame = cap.read()
         if not ret: break
-        idx += 1; t = idx/fps
+        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
+        for (x1,y1,x2,y2) in res.boxes.xyxy.cpu().numpy().astype(int):
             crop = frame[y1:y2, x1:x2]
-            if crop.size==0:
+            if crop.size == 0:
                 continue
 
             plate_img = cv2.resize(crop, (128,32))
-            try:
-                recs = ocr.ocr(plate_img)
-            except Exception:
-                recs = []
-            raw, score = extract_text_score(recs)
+            recs = ocr.ocr(plate_img, det=False, cls=False)
 
+            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)})
+            if plate != "Unknown":
+                records.append({"time_s":round(t,2),
+                                "plate":plate,
+                                "conf":round(score,3)})
 
             cv2.rectangle(frame, (x1,y1),(x2,y2), (0,255,0), 2)
             cv2.putText(frame, plate, (x1, y1-5),
@@ -115,9 +108,9 @@ 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 tmp_out, "Done"
+    return outfp, "Done"
 
-# ─── 5) Gradio UI ─────────────────────────────────────────────────
+# ─── 6) Gradio UI ──────────────────────────────────────────────────
 with gr.Blocks() as demo:
     gr.Markdown("## 🚗 License Plate Detection + Recognition")
     with gr.Row():
@@ -131,6 +124,7 @@ with gr.Blocks() as demo:
             img_out = gr.Image(type="numpy", label="Annotated Image")
             vid_out = gr.Video(label="Annotated Video")
             status  = gr.Textbox(label="Status / JSON Path")
+
     b1.click(run_image, [img_in,conf], [img_out,status])
     b2.click(run_video, [vid_in,conf], [vid_out,status])