Update app.py

app.py

@@ -1,49 +1,46 @@
+# app.py
 import numpy as np
-np.int = int   # PaddleOCR’nin eski np.int kullanım yaması
+np.int = int   # patch for PaddleOCR’s old np.int calls
 
-import cv2, json, tempfile, re, gradio as gr
+import cv2, json, tempfile, re
+import gradio as gr
 from ultralytics import YOLO
 from paddleocr import PaddleOCR
 
-# 1) Modelleri yükle
+# 1) Load models
 yolo = YOLO("models/best.pt")
 ocr  = PaddleOCR(
-    det=False,                   # tespit yok
-    rec=True,                    # sadece okuma
-    rec_model_dir="models/ocr_model",
-    rec_image_shape="3,32,128",  # inference.yml ile uyumlu
-    cls=True,                    # açı düzeltmesi
-    use_angle_cls=True,
-    use_space_char=True
+    text_detection_model_dir=None,
+    text_recognition_model_dir="models/ocr_model",
+    use_textline_orientation=False,
+    lang="en"
 )
 
-# 2) Plaka formatlayıcı
-def format_turkish_plate(s):
+# 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) Görüntü için işlem
+# 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, score in zip(res.boxes.xyxy.cpu().numpy(),
-                          res.boxes.conf.cpu().numpy()):
+    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))
-        try:
-            recs = ocr.ocr(plate_img, det=False, cls=True)
-            if recs and recs[0]:
-                raw,ocr_score = recs[0][1][0], recs[0][1][1]
-            else:
-                raw,ocr_score = "", 0.0
-        except:
-            raw,ocr_score = "", 0.0
+        plate_img = cv2.resize(crop, (128,32))
+        recs = ocr.ocr(plate_img, cls=False)
+
+        if recs and recs[0]:
+            _, (raw, ocr_score) = recs[0]
+        else:
+            raw, ocr_score = "", 0.0
 
         plate = format_turkish_plate(raw)
         label = f"{plate} ({ocr_score:.2f})"
@@ -54,67 +51,65 @@ def run_image(img, conf=0.25):
 
     return cv2.cvtColor(out, cv2.COLOR_BGR2RGB), f"{len(res.boxes)} plate(s) detected"
 
-# 4) Video için işlem
+# 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)
     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
+    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
-
             plate_img = cv2.resize(crop, (128,32))
-            try:
-                recs = ocr.ocr(plate_img, det=False, cls=True)
-                if recs and recs[0]:
-                    raw,ocr_score = recs[0][1][0], recs[0][1][1]
-                else:
-                    raw,ocr_score = "", 0.0
-            except:
-                raw,ocr_score = "", 0.0
+            recs = ocr.ocr(plate_img, cls=False)
+
+            if recs and recs[0]:
+                _, (raw, ocr_score) = recs[0]
+            else:
+                raw, ocr_score = "", 0.0
 
             plate = format_turkish_plate(raw)
-            if plate!="Unknown":
-                records.append({"time_s":round(t,2),"plate":plate,"conf":round(ocr_score,3)})
+            if plate != "Unknown":
+                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.FONT_HERSHEY_SIMPLEX,0.6,(0,255,0),2)
+            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 outfp, "Done"
+        json.dump(records, f, indent=2)
+    return outfp
 
-# 5) Gradio Arayüzü
+# 5) Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🚗 License Plate Detection & Recognition")
+    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")
-            btn_i  = gr.Button("Run Image")
-            btn_v  = gr.Button("Run Video")
+            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()
 
-    btn_i.click(run_image, [img_in,conf],[img_out,status])
-    btn_v.click(run_video, [vid_in,conf],[vid_out,status])
+    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()