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import cv2
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import os
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import numpy as np
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import pytesseract
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def locate_license_plate(image_path):
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if not os.path.isfile(image_path):
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print(f"Error: File '{image_path}' does not exist.")
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return None
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"""
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根据输入的图像路径，定位蓝色车牌并返回裁剪出的车牌区域
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"""
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# 读取图像
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car = cv2.imread(image_path, 1)
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if car is None:
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print(f"Error: Unable to read image '{image_path}'")
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return None
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# 定义蓝色所对应的色彩空间范围
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lower_blue = np.array([100, 110, 110])
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upper_blue = np.array([130, 255, 255])
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# 将图像转换到HSV颜色空间
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hsv = cv2.cvtColor(car, cv2.COLOR_BGR2HSV)
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# 获取蓝色区域的掩模
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mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
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# 将掩模转换为灰度图像
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mask = mask_blue
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# 形态学处理，开运算和闭运算
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matrix = np.ones((20, 20), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, matrix)
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, matrix)
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# 二值化
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ret, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)
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# 查找轮廓
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contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 寻找最大轮廓并定位车牌
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max_area = 0
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best_contour = None
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for contour in contours:
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area = cv2.contourArea(contour)
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if area > max_area:
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max_area = area
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best_contour = contour
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# 获取定位车牌的外接矩形框
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rect = cv2.minAreaRect(best_contour)
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box = cv2.boxPoints(rect)
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box = np.int32(box)
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# 获取旋转矩阵
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angle = rect[2]
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flag = 0
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print(angle)
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if angle < -45:
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angle += 90
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flag = 1
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if angle > 45:
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angle -= 90
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flag = 1
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print(angle)
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center = (rect[0][0], rect[0][1])
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size = (int(rect[1][0]), int(rect[1][1]))
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M = cv2.getRotationMatrix2D(center, angle, 1.0)
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# 旋转图像
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height, width = car.shape[:2]
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rotated = cv2.warpAffine(car, M, (width, height))
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# 获取旋转后矩形框的坐标
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if flag == 0:
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box = cv2.boxPoints(((center[0], center[1]), (size[0], size[1]), 0.0))
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box = np.int32(box)
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# 裁剪车牌区域
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xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]
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ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]
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x1, x2 = min(xs), max(xs)
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y1, y2 = min(ys), max(ys)
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ROI_plate = rotated[y1:y2, x1:x2]
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return ROI_plate
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def preprocess_image_for_ocr(image):
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"""
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预处理图像以提高 OCR 识别率
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"""
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# 去除左右上下各3个像素的边框
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height, width = image.shape[:2]
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image = image[3:height - 3, 3:width - 3]
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# 调整图像大小到 165x40
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image = cv2.resize(image, (165, 40), interpolation=cv2.INTER_AREA)
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# 转换为灰度图像
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 二值化
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_, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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# 去噪
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denoised = cv2.medianBlur(binary, 3)
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return denoised
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def find_split_line(image):
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"""
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在宽度15-30像素之间查找分割线
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"""
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height, width = image.shape
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column_sums = np.sum(image[3:height - 3, 15:30], axis=0) # 忽略顶部和底部的像素
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zero_columns = np.where(column_sums == 0)[0] # 找到像素和为0的列
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if len(zero_columns) == 0:
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# 如果没有找到和为0的列，返回默认分割线为22
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split_line = 22
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else:
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# 计算零列的中值作为分割线
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split_line = int(np.median(zero_columns)) + 15 # 加上偏移量15
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print(split_line)
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return split_line
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def recognize_characters(image):
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"""
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使用OCR技术识别车牌上的字符
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"""
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preprocessed_image = preprocess_image_for_ocr(image)
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# 找到分割线
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split_line = find_split_line(preprocessed_image)
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# 切割图像为两部分
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part1 = preprocessed_image[:, :split_line] # 宽度0-split_line部分
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part2 = preprocessed_image[:, split_line:] # 剩余部分
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# 调用 Tesseract OCR 识别宽度0-24部分
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custom_config_chi_sim = r'--oem 3 --psm 6 -l chi_sim'
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result_chi_sim = pytesseract.image_to_string(part1, config=custom_config_chi_sim)
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# 调用 Tesseract OCR 识别剩余部分
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custom_config_default = r'--oem 3 --psm 6'
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result_default = pytesseract.image_to_string(part2, config=custom_config_default)
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# 合并两个部分的识别结果
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recognized_characters = result_chi_sim.strip() + result_default.strip()
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return recognized_characters
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