Update app.py

app.py

@@ -1,7 +1,6 @@
 # app.py
-
 import numpy as np
-np.int = int   # patch for PaddleOCR’nin eski np.int çağrılarına
+np.int = int   # patch for PaddleOCR’s old np.int calls
 
 import cv2, json, tempfile, re
 import gradio as gr
@@ -10,32 +9,41 @@ from paddleocr import PaddleOCR
 
 # ─── 1) Load models ───────────────────────────────────────────────
 yolo = YOLO("models/best.pt")
-ocr  = PaddleOCR(
-    det=False,                # Görüntü genel tespiti burada kapalı
-    rec=True,
-    rec_model_dir="models/ocr_model",
-    use_textline_orientation=False,
-    lang="en"
+
+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
 )
 
-# ─── 2) Turkish plate formatter ────────────────────────────────────
+# ─── 2) 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"
 
-# ─── yardımcı: OCR çıktısını normalize et ─────────────────────────
+# ─── Helper: 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 ile dönen format: ["TEXT", score]
+    # det=False formatı
     if isinstance(first, (list,tuple)) and len(first)==2 and isinstance(first[0], str):
         return first[0], float(first[1])
-    # det=True formatı (eskiden): [["TEXT", score], …]
+    # det=True formatı
     if isinstance(first, list) and first and isinstance(first[0], (list,tuple)):
         return first[0][0], float(first[0][1])
-    # tanımadı
     return "", 0.0
 
 # ─── 3) Single‐image inference ─────────────────────────────────────
@@ -46,60 +54,70 @@ def run_image(img, conf=0.25):
     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, det=False, cls=False)
+            recs = ocr.ocr(plate_img)  # artık det=False, rec_image_shape, dict hep init'de
         except Exception:
             recs = []
         raw, score = extract_text_score(recs)
+
         plate = format_turkish_plate(raw)
         label = f"{plate} ({score:.2f})"
+
         cv2.rectangle(out, (x1,y1),(x2,y2), (0,255,0), 2)
         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"
 
-# ─── 4) Video inference ───────────────────────────────────────────
+# ─── 4) 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))
-    records = []
-    idx = 0
+    writer  = cv2.VideoWriter(tmp_out, 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
+
         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:
+            if crop.size==0:
                 continue
+
             plate_img = cv2.resize(crop, (128,32))
             try:
-                recs = ocr.ocr(plate_img, det=False, cls=False)
+                recs = ocr.ocr(plate_img)
             except Exception:
                 recs = []
             raw, score = extract_text_score(recs)
+
             plate = format_turkish_plate(raw)
-            if plate != "Unknown":
+            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),
                         cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
+
         writer.write(frame)
+
     cap.release(); writer.release()
     with open("output.json","w") as f:
         json.dump(records, f, indent=2)
     return tmp_out, "Done"
 
-# ─── 5) Gradio UI ──────────────────────────────────────────────────
+# ─── 5) Gradio UI ─────────────────────────────────────────────────
 with gr.Blocks() as demo:
     gr.Markdown("## 🚗 License Plate Detection + Recognition")
     with gr.Row():