diff --git a/mv-and-ip/.gitignore b/mv-and-ip/.gitignore
index 26ca391..12d44ef 100644
--- a/mv-and-ip/.gitignore
+++ b/mv-and-ip/.gitignore
@@ -5,6 +5,7 @@
 # All image files
 *.jpg
 *.png
+*.webp
 
 ## ======== Python ========
 # Python-generated files
diff --git a/mv-and-ip/car_plate.py b/mv-and-ip/car_plate.py
index 8234cae..00e81e3 100644
--- a/mv-and-ip/car_plate.py
+++ b/mv-and-ip/car_plate.py
@@ -7,84 +7,178 @@ import logging
 from pathlib import Path
 from dataclasses import dataclass
 
+# Reference:
+# - Claude Code
+# - https://www.cnblogs.com/linuxAndMcu/p/19144795
+# - https://www.51halcon.com/forum.php?mod=viewthread&tid=6562
+
+UNI_HW: int = 1000
+
+
+def _uniform_car_plate(img: cv.typing.MatLike) -> cv.typing.MatLike:
+    """
+    Uniform the image size to 512x512 while maintaining aspect ratio, padding with black.
+
+    :param img: The image in BGR format to be uniformed.
+    :return: The uniformed image in BGR format.
+    """
+    # Calculate the new width and height for given image
+    h, w = img.shape[:2]
+    scale = min(UNI_HW / w, UNI_HW / h)
+    new_w = int(w * scale)
+    new_h = int(h * scale)
+
+    # Resize the image
+    resized_img = cv.resize(img, (new_w, new_h))
+
+    # Create a black canvas of size UNI_HW x UNI_HW
+    padded_img = np.zeros((UNI_HW, UNI_HW, 3), dtype=np.uint8)
+
+    # Calculate position to paste the resized image (centered)
+    y_offset = (UNI_HW - new_h) // 2
+    x_offset = (UNI_HW - new_w) // 2
+
+    # Paste the resized image onto the canvas
+    padded_img[y_offset : y_offset + new_h, x_offset : x_offset + new_w] = resized_img
+
+    # Return the padded image
+    return padded_img
+
+
+@dataclass
+class CarPlateHsvBoundary:
+    lower_bound: cv.typing.MatLike
+    upper_bound: cv.typing.MatLike
+
+
+CAR_PLATE_HSV_BOUNDARIES: tuple[CarPlateHsvBoundary, ...] = (
+    # 中国蓝牌 HSV 范围
+    CarPlateHsvBoundary(np.array([100, 80, 60]), np.array([130, 255, 255])),
+    # 中国绿牌 HSV 范围
+    CarPlateHsvBoundary(np.array([35, 43, 46]), np.array([99, 255, 255])),
+    # 中国黄牌 HSV 范围
+    CarPlateHsvBoundary(np.array([32, 43, 46]), np.array([68, 255, 255])),
+)
+
+
+def _batchly_mask_car_plate(
+    hsv: cv.typing.MatLike,
+) -> typing.Iterator[cv.typing.MatLike]:
+    """ """
+    for boundary in CAR_PLATE_HSV_BOUNDARIES:
+        # 以给定HSV范围检测符合该颜色的位置
+        mask = cv.inRange(hsv, boundary.lower_bound, boundary.upper_bound)
+
+        # 形态学：闭运算填孔 + 开运算去噪
+        kernel_close = cv.getStructuringElement(cv.MORPH_RECT, (25, 10))
+        kernel_open = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
+        mask = cv.morphologyEx(mask, cv.MORPH_CLOSE, kernel_close)
+        mask = cv.morphologyEx(mask, cv.MORPH_OPEN, kernel_open)
+
+        # Return value
+        yield mask
+
+
+@dataclass
+class CarPlateRegion:
+    x: int
+    y: int
+    w: int
+    h: int
+
+
+MIN_AREA: float = 3000
+MIN_ASPECT_RATIO: float = 1.5
+MAX_ASPECT_RATIO: float = 6.0
+BEST_ASPECT_RATIO: float = 3.5
+
+
+def _analyse_car_plate_connection(
+    mask: cv.typing.MatLike,
+) -> typing.Optional[CarPlateRegion]:
+    # 连通域分析，筛选最符合车牌长宽比的区域
+    num_labels, labels, stats, _ = cv.connectedComponentsWithStats(mask, connectivity=8)
+
+    best: typing.Optional[CarPlateRegion] = None
+    best_score = 0
+
+    for i in range(1, num_labels):
+        x, y, w, h, area = stats[i]
+        # 检查面积
+        if area < MIN_AREA:
+            continue
+        # 标准车牌宽高比约 3:1 ~ 5:1
+        ratio = w / (h + 1e-5)
+        if ratio >= MIN_ASPECT_RATIO and ratio <= MAX_ASPECT_RATIO:
+            score = area * (1 - abs(ratio - BEST_ASPECT_RATIO) / BEST_ASPECT_RATIO)
+            if score > best_score:
+                best_score = score
+                best = CarPlateRegion(x, y, w, h)
+
+    return best
+
+
 def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLike]:
-    """Extract the car plate part from given image.
+    """
+    Extract the car plate part from given image.
 
     :param img: The image containing car plate in BGR format.
     :return: The image of binary car plate in U8 format if succeed, otherwise None.
     """
-    # ── 1. 利用蓝色车牌颜色在 HSV 空间定位车牌 ──────────────────────────
+    img = _uniform_car_plate(img)
+
+    # 转换到HSV空间
     hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
-    # 中国蓝牌 HSV 范围
-    lower_blue = np.array([100, 80, 60])
-    upper_blue = np.array([130, 255, 255])
-    mask_blue = cv.inRange(hsv, lower_blue, upper_blue)
 
-    # 形态学：闭运算填孔 + 开运算去噪
-    kernel_close = cv.getStructuringElement(cv.MORPH_RECT, (25, 10))
-    kernel_open  = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
-    mask_blue = cv.morphologyEx(mask_blue, cv.MORPH_CLOSE, kernel_close)
-    mask_blue = cv.morphologyEx(mask_blue, cv.MORPH_OPEN,  kernel_open)
+    # 利用车牌颜色在 HSV 空间定位车牌
+    candidate: typing.Optional[CarPlateRegion] = None
+    for mask in _batchly_mask_car_plate(hsv):
+        # 连通域分析，筛选最符合车牌长宽比的区域作为车牌
+        candidate = _analyse_car_plate_connection(mask)
+        # 找到任意一个就退出
+        if candidate is not None: break
 
-    # ── 2. 连通域分析，筛选最符合车牌长宽比的区域 ──────────────────────
-    num_labels, labels, stats, _ = cv.connectedComponentsWithStats(mask_blue, connectivity=8)
+    if candidate is None:
+        logging.error('Can not find any car plate.')
+        return None
 
-    best = None
-    best_score = 0
-    h_img, w_img = img.shape[:2]
-
-    for i in range(1, num_labels):
-        x, y, w, h, area = stats[i]
-        if area < 3000:
-            continue
-        ratio = w / (h + 1e-5)
-        # 标准车牌宽高比约 3:1 ~ 5:1
-        if 2.5 < ratio < 6.0:
-            score = area * (1 - abs(ratio - 3.5) / 3.5)
-            if score > best_score:
-                best_score = score
-                best = (x, y, w, h)
-
-    assert best is not None, "未找到车牌区域"
-    x, y, w, h = best
-    # 稍微扩边
+    # 稍微扩边获取最终车牌区域
     pad = 6
-    x1 = max(x - pad, 0)
-    y1 = max(y - pad, 0)
-    x2 = min(x + w + pad, w_img)
-    y2 = min(y + h + pad, h_img)
-
-    plate_color = img[y1:y2, x1:x2].copy()
-    print(f"车牌区域: x={x1}, y={y1}, w={x2-x1}, h={y2-y1}")
+    h_img, w_img = img.shape[:2]
+    x1 = max(candidate.x - pad, 0)
+    y1 = max(candidate.y - pad, 0)
+    x2 = min(candidate.x + candidate.w + pad, w_img)
+    y2 = min(candidate.y + candidate.h + pad, h_img)
+    logging.info(f'车牌区域: x={x1}, y={y1}, w={x2 - x1}, h={y2 - y1}')
 
     # # 在原图上标记（仅供调试）
     # debug = img.copy()
     # cv.rectangle(debug, (x1, y1), (x2, y2), (0, 255, 0), 3)
     # cv.imwrite('./debug_detected.jpg', debug)
-
-    # ── 3. 二值化：文字/边缘 → 黑色，背景 → 白色 ─────────────────────
-    gray = cv.cvtColor(plate_color, cv.COLOR_BGR2GRAY)
+    
+    # 二值化：文字/边缘 → 黑色，背景 → 白色
+    gray = cv.cvtColor(img[y1:y2, x1:x2], cv.COLOR_BGR2GRAY)
 
     # 高斯模糊降噪
     blurred = cv.GaussianBlur(gray, (3, 3), 0)
 
     # Otsu 自动阈值，得到白字黑底，再取反 → 黑字白底
-    _, binary_otsu = cv.threshold(blurred, 0, 255,
-                                cv.THRESH_BINARY + cv.THRESH_OTSU)
-    binary = cv.bitwise_not(binary_otsu)          # 反转：字符变黑，背景变白
+    _, binary_otsu = cv.threshold(blurred, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+    # 反转：字符变黑，背景变白
+    binary = cv.bitwise_not(binary_otsu)
 
     # 去除小噪点（开运算）
     kernel_denoise = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
     binary = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel_denoise)
 
-    return binary
+    #return binary
     # cv.imwrite('./plate_binary.png', binary)
     # print("二值化结果已保存: plate_binary.png")
 
     # ── 4. 叠加边框轮廓（细化文字边缘，参考效果图）─────────────────────
     # Canny 边缘叠加让效果更接近参考图
     edges = cv.Canny(blurred, 40, 120)
-    edges_inv = cv.bitwise_not(edges)             # 边缘→黑色
+    edges_inv = cv.bitwise_not(edges)  # 边缘→黑色
     combined = cv.bitwise_and(binary, edges_inv)  # 合并
     # 再做一次轻微腐蚀让字体略粗
     kernel_dilate = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))