Update app.py

app.py

@@ -1,75 +1,119 @@
 # app.py
 import cv2, json, tempfile, re
 import numpy as np
+# restore the old alias so that `np.int` still works in older code
 np.int = int
 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,               # turn off detection
-    rec=True,                # enable recognition only
+    det=False,               # built-in detection kapalı
+    rec=True,                # sadece recognition
     rec_model_dir="models/ocr_model",
-    cls=False,               # no angle‐classification
+    cls=False,               # angle-classification kapalı
 )
 
-# 2) Turkish plate formatter
+# ─── 2) Turkish plate formatter ────────────────────────────────────
 def format_turkish_plate(s: str) -> str:
     m = re.match(r"^(\d{2})([A-Z]{1,3})(\d{2,4})$", s.replace(" ",""))
     return f"{m[1]} {m[2]} {m[3]}" if m else "Unknown"
 
-# 3) Single‐image pipeline
+# ─── 3) Single-image pipeline ─────────────────────────────────────
 def run_image(img, conf=0.25):
+    # BGR ← RGB
     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()):
+    count = 0
+
+    for box,score in zip(res.boxes.xyxy.cpu().numpy(),
+                         res.boxes.conf.cpu().numpy()):
         x1,y1,x2,y2 = box.astype(int)
         roi = out[y1:y2, x1:x2]
-        if roi.size==0: continue
-        plate_img = cv2.resize(roi,(128,32))
-        rec = ocr.ocr(plate_img, cls=True)[0]
-        text = "".join(w[1][0] for w in rec) if rec else ""
-        label= format_turkish_plate(text)
-        cv2.rectangle(out,(x1,y1),(x2,y2),(0,255,0),2)
-        cv2.putText(out,f"{label} {score:.2f}",(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)"
-
-# 4) Video pipeline
+        if roi.size == 0:
+            continue
+
+        # Resize for OCR
+        plate_img = cv2.resize(roi, (128,32))
+        recs = ocr.ocr(plate_img, cls=True)
+        if not recs or not recs[0]:
+            # BOŞ sonuç: OCR hiç text tespit edemedi demektir.
+            continue
+
+        rec = recs[0]
+        text = "".join(w[1][0] for w in rec)
+        formatted = format_turkish_plate(text)
+        score_rec = min(w[1][1] for w in rec)
+
+        label = f"{formatted} {score_rec:.2f}"
+        cv2.rectangle(out, (x1,y1), (x2,y2), (0,255,0), 2)
+        cv2.putText(out, label, (x1, y1-6),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
+        count += 1
+
+    return cv2.cvtColor(out, cv2.COLOR_BGR2RGB), f"{count} plate(s) detected"
+
+# ─── 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))
-    outfp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
-    writer= cv2.VideoWriter(outfp, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w,h))
-    records=[]
-    idx=0
+    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))
+
+    out_fp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    writer = cv2.VideoWriter(out_fp,
+                             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 (x1,y1,x2,y2) in res.boxes.xyxy.cpu().numpy().astype(int):
-            roi=frame[y1:y2,x1:x2]
-            if roi.size==0: continue
-            plate_img=cv2.resize(roi,(128,32))
-            rec=ocr.ocr(plate_img, cls=True)[0]
-            text="".join(w[1][0] for w in rec) if rec else ""
-            label=format_turkish_plate(text)
-            score=min(w[1][1] for w in rec) if rec else 0.0
-            if label!="Unknown":
-                records.append({"time_s":round(t,2),"plate":label,"conf":round(score,3)})
-            cv2.rectangle(frame,(x1,y1),(x2,y2),(0,255,0),2)
-            cv2.putText(frame,label,(x1,y1-5),cv2.FONT_HERSHEY_SIMPLEX,0.6,(0,255,0),2)
+        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
+            roi = frame[y1:y2, x1:x2]
+            if roi.size == 0:
+                continue
+
+            plate_img = cv2.resize(roi, (128,32))
+            recs = ocr.ocr(plate_img, cls=True)
+            if not recs or not recs[0]:
+                continue
+
+            rec = recs[0]
+            text = "".join(w[1][0] for w in rec)
+            formatted = format_turkish_plate(text)
+            score_rec = min(w[1][1] for w in rec)
+
+            records.append({
+                "time_s": round(t,2),
+                "plate": formatted,
+                "conf": round(score_rec,3)
+            })
+
+            cv2.rectangle(frame, (x1,y1), (x2,y2), (0,255,0), 2)
+            cv2.putText(frame, f"{formatted} {score_rec:.2f}",
+                        (x1, y1-6),
+                        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
 
-# 5) Gradio UI
+    cap.release()
+    writer.release()
+    with open("output.json", "w") as f:
+        json.dump(records, f, indent=2)
+    return out_fp
+
+# ─── 5) Gradio UI ────────────────────────────────────────────────
 with gr.Blocks() as demo:
     gr.Markdown("## 🚗 License Plate Detection + Recognition")
     with gr.Row():
@@ -83,7 +127,9 @@ with gr.Blocks() as demo:
             img_out = gr.Image(type="numpy", label="Annotated Image")
             vid_out = gr.Video(label="Annotated Video")
             txt_out = gr.Textbox(label="Status / JSON Path")
-    btn1.click(run_image, [img_in,slider],[img_out,txt_out])
-    btn2.click(run_video, [vid_in,slider],[vid_out,txt_out])
+
+    btn1.click(run_image, [img_in, slider], [img_out, txt_out])
+    btn2.click(run_video, [vid_in, slider], [vid_out, txt_out])
+
 if __name__=="__main__":
     demo.launch()