import numpy as np
import glob
def get_plate(image_):
global plate
rawImage = image_.copy()
# 去噪处理
image_ = cv2.GaussianBlur(image_, (3, 3), 0)
# 色彩空间转换
image_ = cv2.cvtColor(image_, cv2.COLOR_BGR2GRAY)
# Sobel算子
Sobel_x = cv2.Sobel(image_, cv2.CV_16S, 1, 0)
absX = cv2.convertScaleAbs(Sobel_x)
image_ = absX
# 阈值处理
ret, image_ = cv2.threshold(image_, 0, 255, cv2.THRESH_OTSU)
# 闭运算 先膨胀后腐蚀 车牌各个字符是分散的 让车牌构成一体
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 5))
image_ = cv2.morphologyEx(image_, cv2.MORPH_CLOSE, kernelX)
# 开运算 先腐蚀后膨胀 去除噪声
kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))
image_ = cv2.morphologyEx(image_, cv2.MORPH_OPEN, kernelY)
# 中值滤波 去除噪声
image_ = cv2.medianBlur(image_, 15)
# 查找轮廓
contours, w1 = cv2.findContours(image_, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 逐个遍历 将宽度>3倍高度的轮廓确定为车牌
for item in contours:
rect = cv2.boundingRect(item)
x = rect[0]
y = rect[1]
weight = rect[2]
height = rect[3]
if weight > (height * 3):
plate = rawImage[y:y + height, x:x + weight]
return plate
def preprocessor(image_):
# 图像去噪灰度处理
image_ = cv2.GaussianBlur(image_, (3, 3), 0)
# 色彩空间转换
gray_image = cv2.cvtColor(image_, cv2.COLOR_RGB2GRAY)
# 阈值处理
ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
# 膨胀处理 让一个字构成一个整体
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
image_ = cv2.dilate(image_, kernel)
return image_
def split_plate(image_):
# 查找轮廓，各个字符的轮廓
contours, hierarchy = cv2.findContours(image_, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
words = []
# 遍历所有轮廓
for item in contours:
rect = cv2.boundingRect(item)
words.append(rect)
# 按照x轴坐标值排序（自左向右排序）
words = sorted(words, key=lambda s: s[0], reverse=False)
# 用word存放左上角起始点及长宽值
plateChars = []
for word in words:
# 筛选字符的轮廓(高宽比在1.5-8之间，宽度大于3)
if (word[3] > (word[2] * 1.5)) and (word[3] 3):
plateChar = image_[word[1]:word[1] + word[3], word[0]:word[0] + word[2]]
plateChars.append(plateChar)
return plateChars
template_dict = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9',
10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H',
18: 'J', 19: 'K', 20: 'L', 21: 'M', 22: 'N', 23: 'P', 24: 'Q', 25: 'R',
26: 'S', 27: 'T', 28: 'U', 29: 'V', 30: 'W', 31: 'X', 32: 'Y', 33: 'Z',
34: '京', 35: '津', 36: '冀', 37: '晋', 38: '蒙', 39: '辽', 40: '吉', 41: '黑',
42: '沪', 43: '苏', 44: '浙', 45: '皖', 46: '闽', 47: '赣', 48: '鲁', 49: '豫',
50: '鄂', 51: '湘', 52: '粤', 53: '桂', 54: '琼', 55: '渝', 56: '川', 57: '贵',
58: '云', 59: '藏', 60: '陕', 61: '甘', 62: '青', 63: '宁', 64: '新',
65: '港', 66: '澳', 67: '台'}
def get_characters():
c = []
for i_ in range(0, 67):
words = []
words.extend(glob.glob(f'template/{template_dict.get(i_)}/*.*'))
c.append(words)
return c
def get_match_value(template, image_):
# 读取模板图像
templateImage = cv2.imdecode(np.fromfile(template, dtype=np.uint8), 1)
# 模板图像色彩空间转换 BGR-->灰度
templateImage = cv2.cvtColor(templateImage, cv2.COLOR_BGR2GRAY)
# 模板图像阈值处理 灰度-->二值
ret, templateImage = cv2.threshold(templateImage, 0, 255, cv2.THRESH_OTSU)
# 获取待识别图像的尺寸
height, width = image_.shape
# 将模板图像调整为与待识别图像尺寸一致
templateImage = cv2.resize(templateImage, (width, height))
# 计算模板图像、待识别图像的模板匹配值
result = cv2.matchTemplate(image_, templateImage, cv2.TM_CCOEFF)
# 将计算结果返回
return result[0][0]
def match_chars(plates, chars_):
# 存储所有的识别结果
results_ = []
# 逐个遍历要识别的字符
for i_ in plates:
# 最佳匹配
best_match = []
# words 对应的是每一个字符
for words in chars_:
match = []
for word in words:
result = get_match_value(word, i_)
match.append(result)
best_match.append(max(match))
i_ = best_match.index(max(best_match))
r = template_dict[i_]
results_.append(r)
return results_
if __name__ == '__main__':
# 读取原始图像
image = cv2.imread("test_img.jpg")
# 获取车牌
image = get_plate(image)
# 预处理
image = preprocessor(image)
# 分割车牌 将每个字符独立出来
plate_chars = split_plate(image)
# 获取所有模板文件
chars = get_characters()
# 使用模板chars逐个识别字符集plates
results = match_chars(plate_chars, chars)
# 将列表转换为字符串并输出识别结果
print("".join(results))
附件解压密码为 52pj
