Update app.py

app.py

@@ -1,86 +1,106 @@
 # app.py
+
 import numpy as np
-np.int = int   # patch for PaddleOCR’s old np.int calls
+np.int = int   # PaddleOCR’nin eski np.int kullanım yaması
 
-import cv2, json, tempfile, re
-import gradio as gr
+import cv2, json, tempfile, re, gradio as gr
 from ultralytics import YOLO
 from paddleocr import PaddleOCR
 
-# 1) Load models
+# ─── 1) Load models ───────────────────────────────────────────────
 yolo = YOLO("models/best.pt")
-ocr  = PaddleOCR(
-    text_detection_model_dir=None,
-    text_recognition_model_dir="models/ocr_model",
-    use_textline_orientation=False,
-    lang="en"
+
+ocr = PaddleOCR(
+    det=False,                    # OCR’nin kendi tespitini kapatıyoruz
+    rec=True,                     # sadece okuma
+    rec_model_dir="models/ocr_model",
+    rec_image_shape="3,32,128",   # inference.yml ile birebir aynı
+    cls=True,                     # açı sınıflandırıcısı
+    use_angle_cls=True,           # v2.x flag
+    use_space_char=True           # boşluk okumasına izin
 )
 
-# 2) Turkish plate formatter
+# ─── 2) Plaka formatlayıcı ─────────────────────────────────────────
 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) Single‐image inference
+# ─── 3) Görüntü pipeline ────────────────────────────────────────────
 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, yolo_score in zip(res.boxes.xyxy.cpu().numpy(),
-                               res.boxes.conf.cpu().numpy()):
+    for box, _ 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))
 
-        plate_img = cv2.resize(crop, (128,32))
-        recs = ocr.ocr(plate_img, cls=False)
+        # burada dahili tespiti kapattık
+        try:
+            recs = ocr.ocr(plate_img, det=False, cls=True)
+        except Exception:
+            recs = []
 
         if recs and recs[0]:
-            _, (raw, ocr_score) = recs[0]
+            raw_text, ocr_score = recs[0][1][0], recs[0][1][1]
         else:
-            raw, ocr_score = "", 0.0
+            raw_text, ocr_score = "", 0.0
 
-        plate = format_turkish_plate(raw)
+        plate = format_turkish_plate(raw_text)
         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"
 
-# 4) Video inference (same pattern)
+# ─── 4) Video pipeline ─────────────────────────────────────────────
 def run_video(video_file, conf=0.25):
     cap = cv2.VideoCapture(video_file)
     fps = cap.get(cv2.CAP_PROP_FPS)
-    w,h = int(cap.get(3)), int(cap.get(4))
+    w  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    h  = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     outfp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
     writer = cv2.VideoWriter(outfp, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w,h))
-    records, idx = [], 0
+    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 (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, cls=False)
+            try:
+                recs = ocr.ocr(plate_img, det=False, cls=True)
+            except Exception:
+                recs = []
 
             if recs and recs[0]:
-                _, (raw, ocr_score) = recs[0]
+                raw_text, ocr_score = recs[0][1][0], recs[0][1][1]
             else:
-                raw, ocr_score = "", 0.0
+                raw_text, ocr_score = "", 0.0
 
-            plate = format_turkish_plate(raw)
+            plate = format_turkish_plate(raw_text)
             if plate != "Unknown":
-                records.append({"time_s":round(t,2), "plate":plate, "conf":round(ocr_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),
@@ -91,25 +111,26 @@ 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
+    return outfp, "Done"
 
-# 5) 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")
-            vid_in = gr.File(label="Video (.mp4)")
-            conf   = gr.Slider(0,1,0.25,0.01)
+            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")
             b1     = gr.Button("Run Image")
             b2     = gr.Button("Run Video")
         with gr.Column():
-            img_out = gr.Image(type="numpy")
-            vid_out = gr.Video()
-            status  = gr.Textbox()
+            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])
 
-if __name__=="__main__":
-    demo.launch()
+if __name__ == "__main__":
+    demo.launch()