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feat: mix my code with claude code

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yyc12345
2026-04-08 23:22:32 +08:00
parent 8e1e080654
commit 5392084f0f
2 changed files with 142 additions and 47 deletions
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1
mv-and-ip/.gitignore vendored
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@@ -5,6 +5,7 @@
# All image files
*.jpg
*.png
*.webp
## ======== Python ========
# Python-generated files

188
mv-and-ip/car_plate.py
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@@ -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))