Update app.py

app.py

@@ -1,39 +1,29 @@
-# 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
-import json
-import tempfile
-import 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) Modelleri yükle
 yolo = YOLO("models/best.pt")
-
-ocr = PaddleOCR(
-    det=False,                   # no detector on full image
-    rec=True,                    # recognition only
+ocr  = PaddleOCR(
+    det=False,                   # tespit yok
+    rec=True,                    # sadece okuma
     rec_model_dir="models/ocr_model",
-    cls=True,                    # turn on angle classifier
-    use_angle_cls=True,          # v2.x flag
-    use_space_char=True          # allow spaces
+    rec_image_shape="3,32,128",  # inference.yml ile uyumlu
+    cls=True,                    # açı düzeltmesi
+    use_angle_cls=True,
+    use_space_char=True
 )
 
-
-# ─── 2) Turkish plate formatter ────────────────────────────────────
-def format_turkish_plate(s: str) -> str:
+# 2) Plaka formatlayıcı
+def format_turkish_plate(s):
     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 için işlem
 def run_image(img, conf=0.25):
     bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
     res = yolo(bgr, conf=conf)[0]
@@ -41,102 +31,81 @@ def run_image(img, conf=0.25):
 
     for box, score in zip(res.boxes.xyxy.cpu().numpy(),
                           res.boxes.conf.cpu().numpy()):
-        x1, y1, x2, y2 = box.astype(int)
+        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))
-
-        # <-- ALWAYS pass det=False here
         try:
             recs = ocr.ocr(plate_img, det=False, cls=True)
             if recs and recs[0]:
-                raw_text, ocr_score = recs[0][1][0], recs[0][1][1]
+                raw,ocr_score = recs[0][1][0], recs[0][1][1]
             else:
-                raw_text, ocr_score = "", 0.0
-        except Exception:
-            raw_text, ocr_score = "", 0.0
+                raw,ocr_score = "", 0.0
+        except:
+            raw,ocr_score = "", 0.0
 
-        plate = format_turkish_plate(raw_text)
+        plate = format_turkish_plate(raw)
         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.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+        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 için işlem
 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
+    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
+        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):
+        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))
-
-            # <-- ALWAYS pass det=False here
+            plate_img = cv2.resize(crop, (128,32))
             try:
                 recs = ocr.ocr(plate_img, det=False, cls=True)
                 if recs and recs[0]:
-                    raw_text, ocr_score = recs[0][1][0], recs[0][1][1]
+                    raw,ocr_score = recs[0][1][0], recs[0][1][1]
                 else:
-                    raw_text, ocr_score = "", 0.0
-            except Exception:
-                raw_text, ocr_score = "", 0.0
-
-            plate = format_turkish_plate(raw_text)
-            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)
+                    raw,ocr_score = "", 0.0
+            except:
+                raw,ocr_score = "", 0.0
 
-        writer.write(frame)
+            plate = format_turkish_plate(raw)
+            if plate!="Unknown":
+                records.append({"time_s":round(t,2),"plate":plate,"conf":round(ocr_score,3)})
 
-    cap.release()
-    writer.release()
+            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)
 
-    with open("output.json", "w") as f:
-        json.dump(records, f, indent=2)
-
-    return tmp_out, "Done"
+        writer.write(frame)
 
+    cap.release(); writer.release()
+    with open("output.json","w") as f:
+        json.dump(records,f,indent=2)
+    return outfp, "Done"
 
-# ─── 5) Gradio UI ─────────────────────────────────────────────────
+# 5) Gradio Arayüzü
 with gr.Blocks() as demo:
     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")
+            conf   = gr.Slider(0,1,0.25,0.01, label="YOLO Confidence")
             btn_i  = gr.Button("Run Image")
             btn_v  = gr.Button("Run Video")
         with gr.Column():
@@ -144,8 +113,8 @@ with gr.Blocks() as demo:
             vid_out = gr.Video(label="Annotated Video")
             status  = gr.Textbox(label="Status / JSON Path")
 
-    btn_i.click(run_image, [img_in, conf], [img_out, status])
-    btn_v.click(run_video, [vid_in, conf], [vid_out, status])
+    btn_i.click(run_image, [img_in,conf],[img_out,status])
+    btn_v.click(run_video, [vid_in,conf],[vid_out,status])
 
-if __name__ == "__main__":
+if __name__=="__main__":
     demo.launch()