corrigiendo app

app.py

@@ -133,8 +133,8 @@ def draw_box_text(img, xyxy, text, color=(0, 255, 0)):
         cv2.putText(img, text, (x1 + 2, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,0), 2, cv2.LINE_AA)
 
 def detect_plates_bgr(bgr, conf=0.25, iou=0.45):
-    # imgsz 512 suele ser ~2× más rápido que 640 en CPU
-    res = yolo.predict(bgr, conf=conf, iou=iou, imgsz=640, max_det=2, verbose=False)[0]
+    # 512 es buen sweet spot en CPU
+    res = yolo.predict(bgr, conf=conf, iou=iou, imgsz=512, max_det=1, verbose=False)[0]
     boxes = res.boxes.xyxy.cpu().numpy() if res.boxes is not None else np.empty((0,4))
     confs = res.boxes.conf.cpu().numpy() if res.boxes is not None else np.empty((0,))
     return boxes, confs
@@ -146,25 +146,24 @@ def run_on_image_bgr(bgr, conf=0.25, iou=0.45, with_ocr=True, annotate=True, max
     boxes, confs = detect_plates_bgr(bgr, conf, iou)
 
     idx = np.argsort(-confs)[:max_plates]
-    boxes = boxes[idx]
-    confs = confs[idx]
+    boxes, confs = boxes[idx], confs[idx]
 
     detections = []
     for xyxy, c in zip(boxes, confs):
-        x1, y1, x2, y2 = expand_box(xyxy, w, h, pad_ratio=0.12)  # 👈 12% borde
+        x1, y1, x2, y2 = expand_box(xyxy, w, h, pad_ratio=0.10)
         crop = bgr[y1:y2, x1:x2]
         txt, s = ("", 0.0)
-        if with_ocr and crop.size:
+
+        # 👇 no gastes OCR si la caja es floja
+        if with_ocr and crop.size and float(c) >= 0.55:
             txt, s = ocr_plate(crop)
+
         if annotate:
             label = f"{txt or 'plate'} {c:.2f}"
             draw_box_text(vis, (x1, y1, x2, y2), label)
-        detections.append({
-            "box_xyxy": [x1, y1, x2, y2],
-            "det_conf": float(c),
-            "ocr_text": txt,
-            "ocr_conf": float(s),
-        })
+
+        detections.append({"box_xyxy":[x1,y1,x2,y2],"det_conf":float(c),"ocr_text":txt,"ocr_conf":float(s)})
+
     dt_ms = int((time.time() - t0) * 1000)
     return vis, detections, (w, h), dt_ms
 
@@ -312,13 +311,9 @@ async def detect_upload(
 
 @app.on_event("startup")
 def _warmup():
-    import numpy as np
-    dummy = np.full((128, 256, 3), 255, dtype=np.uint8)
-    try:
-        _ = yolo.predict(dummy, conf=0.25, iou=0.45, verbose=False)
-    except Exception as e:
-        print("Warmup YOLO:", e)
-    try:
-        _ = reader.readtext(cv2.cvtColor(dummy, cv2.COLOR_BGR2GRAY), detail=0)
-    except Exception as e:
-        print("Warmup EasyOCR:", e)
+    import numpy as np, cv2
+    dummy = np.zeros((512, 512, 3), dtype=np.uint8)
+    try: _ = yolo.predict(dummy, conf=0.25, iou=0.45, imgsz=512, verbose=False)
+    except Exception as e: print("Warmup YOLO:", e)
+    try: _ = reader.readtext(cv2.cvtColor(dummy, cv2.COLOR_BGR2GRAY), detail=0)
+    except Exception as e: print("Warmup EasyOCR:", e)