corrigiendo app

app.py

@@ -49,23 +49,55 @@ reader = easyocr.Reader(
 
 ALLOW = "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
 
-def preprocess_for_ocr(plate_bgr):
-    img = plate_bgr.copy()
-    h, w = img.shape[:2]
-    if max(h, w) < 160:
-        img = cv2.resize(img, (w*2, h*2), interpolation=cv2.INTER_CUBIC)
+def clamp(v, lo, hi): 
+    return max(lo, min(hi, v))
+
+def expand_box(xyxy, w, h, pad_ratio=0.10):
+    x1, y1, x2, y2 = [int(v) for v in xyxy]
+    bw, bh = x2 - x1, y2 - y1
+    px, py = int(bw * pad_ratio), int(bh * pad_ratio)
+    nx1 = clamp(x1 - px, 0, w - 1)
+    ny1 = clamp(y1 - py, 0, h - 1)
+    nx2 = clamp(x2 + px, 0, w - 1)
+    ny2 = clamp(y2 + py, 0, h - 1)
+    return nx1, ny1, nx2, ny2
+
+def ensure_min_size(img_bgr, target_long=320):
+    h, w = img_bgr.shape[:2]
+    m = max(h, w)
+    if m < target_long:
+        scale = target_long / float(m)
+        nh, nw = int(round(h * scale)), int(round(w * scale))
+        img_bgr = cv2.resize(img_bgr, (nw, nh), interpolation=cv2.INTER_CUBIC)
+    return img_bgr
+
+def preproc_adaptive(plate_bgr):
+    img = ensure_min_size(plate_bgr)           # asegura tamaño
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     gray = cv2.bilateralFilter(gray, 7, 50, 50)
-    th = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
-                               cv2.THRESH_BINARY, 31, 5)
+    th = cv2.adaptiveThreshold(
+        gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 5
+    )
+    # opcional: cerrar huecos finos
+    k = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
+    th = cv2.morphologyEx(th, cv2.MORPH_CLOSE, k, iterations=1)
     return th
 
-def ocr_plate(plate_bgr):
-    img = preprocess_for_ocr(plate_bgr)
-    out = reader.readtext(img, detail=1, allowlist=ALLOW)
+def preproc_clahe_otsu(plate_bgr):
+    img = ensure_min_size(plate_bgr)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+    eq = clahe.apply(gray)
+    _, th = cv2.threshold(eq, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    k = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
+    th = cv2.morphologyEx(th, cv2.MORPH_CLOSE, k, iterations=1)
+    return th
+
+def read_easy(img, allow=ALLOW):
+    out = reader.readtext(img, detail=1, allowlist=allow)
     cands = []
     for _, text, score in out:
-        t = "".join([c for c in text.upper() if c in ALLOW])
+        t = "".join(c for c in (text or "").upper() if c in allow)
         if len(t) >= 4:
             cands.append((t, float(score)))
     if not cands:
@@ -73,6 +105,25 @@ def ocr_plate(plate_bgr):
     cands.sort(key=lambda x: (x[1], len(x[0])), reverse=True)
     return cands[0]
 
+def preprocess_for_ocr(plate_bgr):
+    img = plate_bgr.copy()
+    h, w = img.shape[:2]
+    if max(h, w) < 160:
+        img = cv2.resize(img, (w*2, h*2), interpolation=cv2.INTER_CUBIC)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    gray = cv2.bilateralFilter(gray, 7, 50, 50)
+    th = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                               cv2.THRESH_BINARY, 31, 5)
+    return th
+
+def ocr_plate(plate_bgr):
+    # 1) adaptativa
+    t, s = read_easy(preproc_adaptive(plate_bgr))
+    if t:
+        return t, s
+    # 2) CLAHE + Otsu (fallback)
+    return read_easy(preproc_clahe_otsu(plate_bgr))
+
 def draw_box_text(img, xyxy, text, color=(0, 255, 0)):
     x1, y1, x2, y2 = [int(v) for v in xyxy]
     cv2.rectangle(img, (x1,y1), (x2,y2), color, 2)
@@ -83,9 +134,7 @@ def draw_box_text(img, xyxy, text, color=(0, 255, 0)):
 
 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=512, max_det=2, verbose=False
-    )[0]
+    res = yolo.predict(bgr, conf=conf, iou=iou, imgsz=640, max_det=2, 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
@@ -96,21 +145,20 @@ def run_on_image_bgr(bgr, conf=0.25, iou=0.45, with_ocr=True, annotate=True, max
     t0 = time.time()
     boxes, confs = detect_plates_bgr(bgr, conf, iou)
 
-    # Ordenar por confianza y quedarnos con top-K
     idx = np.argsort(-confs)[:max_plates]
     boxes = boxes[idx]
     confs = confs[idx]
 
     detections = []
     for xyxy, c in zip(boxes, confs):
-        x1, y1, x2, y2 = [int(v) for v in xyxy]
-        crop = bgr[max(0,y1):max(0,y2), max(0,x1):max(0,x2)]
+        x1, y1, x2, y2 = expand_box(xyxy, w, h, pad_ratio=0.12)  # 👈 12% borde
+        crop = bgr[y1:y2, x1:x2]
         txt, s = ("", 0.0)
         if with_ocr and crop.size:
             txt, s = ocr_plate(crop)
         if annotate:
             label = f"{txt or 'plate'} {c:.2f}"
-            draw_box_text(vis, xyxy, label)
+            draw_box_text(vis, (x1, y1, x2, y2), label)
         detections.append({
             "box_xyxy": [x1, y1, x2, y2],
             "det_conf": float(c),