158 lines
5.4 KiB
Python
158 lines
5.4 KiB
Python
import cv2 as cv
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import numpy as np
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import matplotlib.pyplot as plt
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import argparse
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import typing
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import logging
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from pathlib import Path
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from dataclasses import dataclass
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def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLike]:
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"""Extract the car plate part from given image.
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:param img: The image containing car plate in BGR format.
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:return: The image of binary car plate in U8 format if succeed, otherwise None.
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"""
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# ── 1. 利用蓝色车牌颜色在 HSV 空间定位车牌 ──────────────────────────
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hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
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# 中国蓝牌 HSV 范围
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lower_blue = np.array([100, 80, 60])
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upper_blue = np.array([130, 255, 255])
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mask_blue = cv.inRange(hsv, lower_blue, upper_blue)
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# 形态学：闭运算填孔 + 开运算去噪
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kernel_close = cv.getStructuringElement(cv.MORPH_RECT, (25, 10))
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kernel_open = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
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mask_blue = cv.morphologyEx(mask_blue, cv.MORPH_CLOSE, kernel_close)
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mask_blue = cv.morphologyEx(mask_blue, cv.MORPH_OPEN, kernel_open)
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# ── 2. 连通域分析，筛选最符合车牌长宽比的区域 ──────────────────────
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num_labels, labels, stats, _ = cv.connectedComponentsWithStats(mask_blue, connectivity=8)
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best = None
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best_score = 0
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h_img, w_img = img.shape[:2]
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for i in range(1, num_labels):
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x, y, w, h, area = stats[i]
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if area < 3000:
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continue
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ratio = w / (h + 1e-5)
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# 标准车牌宽高比约 3:1 ~ 5:1
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if 2.5 < ratio < 6.0:
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score = area * (1 - abs(ratio - 3.5) / 3.5)
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if score > best_score:
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best_score = score
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best = (x, y, w, h)
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assert best is not None, "未找到车牌区域"
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x, y, w, h = best
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# 稍微扩边
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pad = 6
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x1 = max(x - pad, 0)
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y1 = max(y - pad, 0)
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x2 = min(x + w + pad, w_img)
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y2 = min(y + h + pad, h_img)
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plate_color = img[y1:y2, x1:x2].copy()
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print(f"车牌区域: x={x1}, y={y1}, w={x2-x1}, h={y2-y1}")
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# # 在原图上标记（仅供调试）
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# debug = img.copy()
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# cv.rectangle(debug, (x1, y1), (x2, y2), (0, 255, 0), 3)
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# cv.imwrite('./debug_detected.jpg', debug)
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# ── 3. 二值化：文字/边缘 → 黑色，背景 → 白色 ─────────────────────
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gray = cv.cvtColor(plate_color, cv.COLOR_BGR2GRAY)
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# 高斯模糊降噪
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blurred = cv.GaussianBlur(gray, (3, 3), 0)
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# Otsu 自动阈值，得到白字黑底，再取反 → 黑字白底
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_, binary_otsu = cv.threshold(blurred, 0, 255,
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cv.THRESH_BINARY + cv.THRESH_OTSU)
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binary = cv.bitwise_not(binary_otsu) # 反转：字符变黑，背景变白
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# 去除小噪点（开运算）
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kernel_denoise = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
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binary = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel_denoise)
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return binary
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# cv.imwrite('./plate_binary.png', binary)
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# print("二值化结果已保存: plate_binary.png")
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# ── 4. 叠加边框轮廓（细化文字边缘，参考效果图）─────────────────────
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# Canny 边缘叠加让效果更接近参考图
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edges = cv.Canny(blurred, 40, 120)
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edges_inv = cv.bitwise_not(edges) # 边缘→黑色
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combined = cv.bitwise_and(binary, edges_inv) # 合并
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# 再做一次轻微腐蚀让字体略粗
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kernel_dilate = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
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combined = cv.erode(combined, kernel_dilate, iterations=1)
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return combined
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# cv.imwrite('./plate_final.png', combined)
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# print("最终结果已保存: plate_final.png")
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@dataclass
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class Cli:
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input_file: Path
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"""The path to input file"""
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output_file: Path
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"""The path to output file"""
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@staticmethod
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def from_cmdline() -> "Cli":
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# Build parser
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parser = argparse.ArgumentParser(
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prog="Car Plate Extractor",
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description="Extract the car plate part from given image.",
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)
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parser.add_argument(
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"-i",
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"--in",
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required=True,
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type=str,
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action="store",
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dest="input_file",
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metavar="in.jpg",
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help="""The path to input image containing car plate.""",
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)
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parser.add_argument(
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"-o",
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"--out",
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required=True,
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type=str,
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action="store",
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dest="output_file",
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metavar="out.png",
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help="""The path to output image for extracted car plate.""",
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)
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# Parse argument from cmdline and return
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args = parser.parse_args()
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return Cli(Path(args.input_file), Path(args.output_file))
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def main():
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# Setup logging format
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logging.basicConfig(format="[%(levelname)s] %(message)s", level=logging.DEBUG)
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# Get user request
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cli = Cli.from_cmdline()
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# Load file
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in_img = cv.imread(str(cli.input_file), cv.IMREAD_COLOR)
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if in_img is None:
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logging.error(f"Fail to load image {cli.input_file}")
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return
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# Save extracted file if possible
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out_img = extract_car_plate(in_img)
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if out_img is not None:
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cv.imwrite(str(cli.output_file), out_img)
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if __name__ == "__main__":
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main()
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