本文参考github代码：https://github.com/loveandhope/license-plate-generator
效果：
一、代码目录结构：
background目录下存放各种背景图片
font目录下存放车牌中文、字符的ttf字体
images目录下存放蓝色底牌、新能源绿色底牌、污渍（噪声）的图片
完整代码可参考：https://download.csdn.net/download/benben044/87546578?spm=1001.2014.3001.5503
二、生成流程
本代码可以根据车牌list生成对应的车牌图片list。
（1）生成白底黑字的车牌号码图片
首先，生成一个白底的空白车牌号图片
img = np.array(Image.new("RGB", (880, 280), (255, 255, 255)))然后，逐字（字符）生成图片
ImageDraw.Draw(img).text((0, self.height_offset), char, self.fg_color, font=self.font_en)最后，将上一步生成的图片逐个复制到上上一步步生成的图片中
img[:, char_width_start:char_width_end] = self.generate_char_image(plate_num[i])因为生成的图片和img的第一维（height）大小相同，所以在img中直接使用符号":"。
（2）生成车牌底牌
直接读取底牌的图片即可。
plate_image = cv2.imread(LicensePlateImageGenerator.single_blue_plate_bg)（3）生成最后的车牌图片（以蓝牌为例）
首先，将文字图片转为黑底白字
img = cv2.bitwise_not(char_img)
此时，背景部分值为0，字部分值为255。
然后，将黑底背景变为蓝色底牌背景
img = cv2.bitwise_or(img, template_image)
此时，黑色背景值0 与 蓝色背景值x进行二进制的or操作，只保留了蓝色背景值，实现了背景的替换。
（4）数据增强（增加噪声）
高斯模糊：
通过cv2.blur() 方法实现
高斯噪声：
def add_single_channel_noise(self, single):
""" 添加高斯噪声
:param single: 单一通道的图像数据
"""
diff = 255 - single.max()
noise = np.random.normal(0, 1 + self.rand_reduce(6), single.shape)
noise = (noise - noise.min()) / (noise.max() - noise.min())
noise = diff * noise
noise = noise.astype(np.uint8)
dst = single + noise
return dst添加污渍：
通过cv2.bitwise_not() 和cv2.bitwise_and()操作完成
添加饱和度光照的噪声：
通过调整HSV颜色空间实现，
- Hue：色调
- Saturation：饱和度
- Value：明亮度
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# 色调，饱和度，亮度
hsv[:, :, 0] = hsv[:, :, 0] * (self.hue_keep + np.random.random() * (1 - self.hue_keep))
hsv[:, :, 1] = hsv[:, :, 1] * (self.saturation_keep + np.random.random() * (1 - self.saturation_keep))
hsv[:, :, 2] = hsv[:, :, 2] * (self.value_keep + np.random.random() * (1 - self.value_keep))
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)添加透视变换：
透视变换api整体和放射变化类似，先通过点与点的映射关系获取变换矩阵，然后再将图形进行转换。
以左向投影为例：
-> 
其计算过程如下：
寻找源图像中4个点和目标图像中的4个点，在左向倾斜时右边两个顶点的height值会做修改。
shape = img.shape
size_src = (shape[1], shape[0]) # width, height
# 源图像四个顶点坐标
pts1 = np.float32([[0, 0], [0, size_src[1]], [size_src[0], 0], [size_src[0], size_src[1]]])
# 计算图片进行投影倾斜后的位置
interval = abs(int(math.sin((float(angle) / 180) * math.pi) * shape[0]))
# 目标图像上四个顶点的坐标
if is_left:
pts2 = np.float32([[0, 0], [0, size_src[1]],
[size_src[0], interval], [size_src[0], size_src[1] - interval]])
else:
pts2 = np.float32([[0, interval], [0, size_src[1] - interval],
[size_src[0], 0], [size_src[0], size_src[1]]])
# 获取 3x3的投影映射/透视变换 矩阵
matrix = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, matrix, size_src)
三、python代码：
import os
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
import numpy as np
import cv2
import random
import math
class CharsImageGenerator(object):
"""生成字符图像，背景为白色，字体为黑色"""
# 数字和英文字母列表
numerals = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',
'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
'U', 'V', 'W', 'X', 'Y', 'Z']
def __init__(self, plate_type):
self.plate_type = plate_type
# 字符图片参数
self.font_ch = ImageFont.truetype("./font/platech.ttf", 180, 0) # 中文字体格式
self.font_en = ImageFont.truetype('./font/platechar.ttf', 240, 0) # 英文字体格式
self.bg_color = (255, 255, 255) # 车牌背景颜色
self.fg_color = (0, 0, 0) # 车牌号的字体颜色
self.plate_height = 280 # 车牌高度
self.left_offset = 32 # 车牌号左边第一个字符的偏移量
self.height_offset = 10 # 高度方向的偏移量
self.char_height = 180 # 字符高度
self.chinese_original_width = 180 # 中文字符原始宽度
self.english_original_width = 90 # 非中文字符原始宽度
if plate_type in ['single_blue', 'single_yellow']:
self.char_num = 7
self.char_width = 90 # 字符校正后的宽度
self.plate_width = 880 # 车牌的宽度
self.char_interval = 24 # 字符间的间隔
self.point_size = 20 # 第2个字符与第三个字符间有一个点，该点的尺寸
elif plate_type == 'small_new_energy':
self.char_num = 8
self.first_char_width = 90 # 第一个字符校正后的宽度
self.char_width = 86 # 其余字符校正后宽度
self.plate_width = 960 # 车牌的宽度
self.char_interval = 18 # 字符间的间隔
self.point_size = 62 # 第2个字符与第三个字符间有一个点，该点的尺寸
else:
raise ValueError('目前不支持该类型车牌！')
def generate_images(self, plate_num_str_list):
if self.plate_type in ['single_blue', 'single_yellow', ]:
plate_images = self.generate_440_140_plate(plate_num_str_list)
elif self.plate_type == 'small_new_energy':
plate_images = self.generate_480_140_plate(plate_num_str_list)
else:
raise ValueError('该类型车牌目前功能尚未完成！')
return plate_images
def generate_440_140_plate(self, plate_num_str_list):
""" 生成440 * 140尺寸的7位车牌字符图片
:param plate_nums:
:return:
"""
plate_images = list()
for plate_num in plate_num_str_list:
# 创建空白车牌号图片
img = np.array(Image.new("RGB", (self.plate_width, self.plate_height), self.bg_color))
# 每个字符的x轴起始、终止位置
char_width_start = self.left_offset
char_width_end = char_width_start + self.char_width
img[:, char_width_start:char_width_end] = self.generate_char_image(
plate_num[0]) # 生成的图片和img的第一维大小相同，所以在img中直接使用符号":"
char_width_start = char_width_end + self.char_interval
char_width_end = char_width_start + self.char_width
img[:, char_width_start:char_width_end] = self.generate_char_image(plate_num[1])
# 隔开特殊间隙，继续添加车牌的后续车牌号
char_width_end = char_width_end + self.point_size + self.char_interval
for i in range(2, len(plate_num)):
char_width_start = char_width_end + self.char_interval
char_width_end = char_width_start + self.char_width
img[:, char_width_start:char_width_end] = self.generate_char_image(plate_num[i])
plate_images.append(img)
# chars_image debug
# cv2.imshow("chars_image debug", img)
# cv2.waitKey()
return plate_images
def generate_char_image(self, char):
""" 生成字符图片
:param char: 字符
:return:
"""
# 根据是否中文字符，选择生成模式
if char in CharsImageGenerator.numerals or char in CharsImageGenerator.alphabet:
img = self.generate_en_char_image(char)
else:
img = self.generate_ch_char_image(char)
return img
def generate_ch_char_image(self, char):
""" 生成中文字符图片
:param char: 待生成的中文字符
"""
img = Image.new("RGB", (self.chinese_original_width, self.plate_height), self.bg_color)
ImageDraw.Draw(img).text((0, self.height_offset), char, self.fg_color, font=self.font_ch)
img = img.resize((self.char_width, self.plate_height))
return np.array(img)
def generate_en_char_image(self, char):
"""" 生成英文字符图片
:param char: 待生成的英文字符
"""
img = Image.new("RGB", (self.english_original_width, self.plate_height), self.bg_color)
ImageDraw.Draw(img).text((0, self.height_offset), char, self.fg_color, font=self.font_en)
img = img.resize((self.char_width, self.plate_height))
return np.array(img)
class LicensePlateImageGenerator(object):
"""根据车牌类型生成底牌图片"""
single_blue_plate_bg = './images/single_blue.bmp'
small_new_energy_plate_bg = './images/small_new_energy.jpg'
def __init__(self, plate_type):
self.plate_type = plate_type
if plate_type == 'single_blue':
plate_image = cv2.imread(LicensePlateImageGenerator.single_blue_plate_bg)
elif plate_type == 'small_new_energy':
plate_image = cv2.imread(LicensePlateImageGenerator.small_new_energy_plate_bg)
else:
raise ValueError('该类型车牌目前功能尚未完成！')
# template_image debug
# cv2.imshow("template_image debug", plate_image)
# cv2.waitKey()
self.bg = plate_image
def generate_template_image(self, width, height):
return cv2.resize(self.bg, (width, height))
class ImageAugmentation(object):
"""图像增强操作：HSV变化，添加背景，高斯噪声，污渍"""
horizontal_sight_directions = ('left', 'mid', 'right')
vertical_sight_directions = ('up', 'mid', 'down')
def __init__(self, plate_type, template_image):
self.plate_type = plate_type
# 确定字符颜色是否应该为黑色
if plate_type == 'single_blue':
# 字符为白色
self.is_black_char = False
elif plate_type in ['single_yellow', 'small_new_energy']:
# 字符为黑字
self.is_black_char = True
else:
raise ValueError('暂时不支持该类型车牌')
self.template_image = template_image
# 透视变换
self.angle_horizontal = 15
self.angle_vertical = 15
self.angle_up_down = 10
self.angle_left_right = 5
self.factor = 10
# 色调，饱和度，亮度
self.hue_keep = 0.8
self.saturation_keep = 0.3
self.value_keep = 0.2
# 自然环境照片的路径列表
self.env_data_paths = ImageAugmentation.search_file("background")
# 高斯噪声level
self.level = 1 + ImageAugmentation.rand_reduce(4)
# 污渍
self.smu = cv2.imread("images/smu.jpg")
def left_right_transfer(self, img, is_left=True, angle=None):
"""
左右视角，默认左视角
:param img:
:param is_left:
:param angle: 角度
:return:
"""
if angle is None:
angle = self.angle_left_right
shape = img.shape
size_src = (shape[1], shape[0]) # width, height
# 源图像四个顶点坐标
pts1 = np.float32([[0, 0], [0, size_src[1]], [size_src[0], 0], [size_src[0], size_src[1]]])
# 计算图片进行投影倾斜后的位置
interval = abs(int(math.sin((float(angle) / 180) * math.pi) * shape[0]))
# 目标图像上四个顶点的坐标
if is_left:
pts2 = np.float32([[0, 0], [0, size_src[1]],
[size_src[0], interval], [size_src[0], size_src[1] - interval]])
else:
pts2 = np.float32([[0, interval], [0, size_src[1] - interval],
[size_src[0], 0], [size_src[0], size_src[1]]])
# 获取 3x3的投影映射/透视变换 矩阵
matrix = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, matrix, size_src)
return dst, matrix, size_src
def up_down_transfer(self, img, is_down=True, angle=None):
""" 上下视角，默认下视角
:param img: 正面视角原始图片
:param is_down: 是否下视角
:param angle: 角度
:return:
"""
if angle is None:
angle = self.rand_reduce(self.angle_up_down)
shape = img.shape
size_src = (shape[1], shape[0])
# 源图像四个顶点坐标
pts1 = np.float32([[0, 0], [0, size_src[1]], [size_src[0], 0], [size_src[0], size_src[1]]])
# 计算图片进行投影倾斜后的位置
interval = abs(int(math.sin((float(angle) / 180) * math.pi) * shape[0]))
# 目标图像上四个顶点的坐标
if is_down:
pts2 = np.float32([[interval, 0], [0, size_src[1]],
[size_src[0] - interval, 0], [size_src[0], size_src[1]]])
else:
pts2 = np.float32([[0, 0], [interval, size_src[1]],
[size_src[0], 0], [size_src[0] - interval, size_src[1]]])
# 获取 3x3的投影映射/透视变换 矩阵
matrix = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, matrix, size_src)
return dst, matrix, size_src
def vertical_tilt_transfer(self, img, is_left_high=True):
""" 添加按照指定角度进行垂直倾斜(上倾斜或下倾斜，最大倾斜角度self.angle_vertical一半）
:param img: 输入图像的numpy
:param is_left_high: 图片投影的倾斜角度，左边是否相对右边高
"""
angle = self.rand_reduce(self.angle_vertical)
shape = img.shape
size_src = [shape[1], shape[0]]
# 源图像四个顶点坐标
pts1 = np.float32([[0, 0], [0, size_src[1]], [size_src[0], 0], [size_src[0], size_src[1]]])
# 计算图片进行上下倾斜后的距离，及形状
interval = abs(int(math.sin((float(angle) / 180) * math.pi) * shape[1]))
size_target = (int(math.cos((float(angle) / 180) * math.pi) * shape[1]), shape[0] + interval)
# 目标图像上四个顶点的坐标
if is_left_high:
pts2 = np.float32([[0, 0], [0, size_target[1] - interval],
[size_target[0], interval], [size_target[0], size_target[1]]])
else:
pts2 = np.float32([[0, interval], [0, size_target[1]],
[size_target[0], 0], [size_target[0], size_target[1] - interval]])
# 获取 3x3的投影映射/透视变换 矩阵
matrix = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, matrix, size_target)
return dst, matrix, size_target
def horizontal_tilt_transfer(self, img, is_right_tilt=True):
""" 添加按照指定角度进行水平倾斜(右倾斜或左倾斜，最大倾斜角度self.angle_horizontal一半）
:param img: 输入图像的numpy
:param is_right_tilt: 图片投影的倾斜方向（右倾，左倾）
"""
angle = self.rand_reduce(self.angle_horizontal)
shape = img.shape
size_src = [shape[1], shape[0]]
# 源图像四个顶点坐标
pts1 = np.float32([[0, 0], [0, size_src[1]], [size_src[0], 0], [size_src[0], size_src[1]]])
# 计算图片进行左右倾斜后的距离，及形状
interval = abs(int(math.sin((float(angle) / 180) * math.pi) * shape[0]))
size_target = (shape[1] + interval, int(math.cos((float(angle) / 180) * math.pi) * shape[0]))
# 目标图像上四个顶点的坐标
if is_right_tilt:
pts2 = np.float32([[interval, 0], [0, size_target[1]],
[size_target[0], 0], [size_target[0] - interval, size_target[1]]])
else:
pts2 = np.float32([[0, 0], [interval, size_target[1]],
[size_target[0] - interval, 0], [size_target[0], size_target[1]]])
# 获取 3x3的投影映射/透视变换 矩阵
matrix = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, matrix, size_target)
return dst, matrix, size_target
def sight_transfer(self, images, horizontal_sight_direction, vertical_sight_direction):
"""
对图片进行视角变换
:param images:
:param horizontal_sight_direction: 水平视角变换方向
:param vertical_sight_direction: 垂直视角变换方向
:return:
"""
flag = 0
img_num = len(images)
# 左右视角
if horizontal_sight_direction == 'left':
flag += 1
images[0], matrix, size = self.left_right_transfer(images[0], is_left=True)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
elif horizontal_sight_direction == 'right':
flag -= 1
images[0], matrix, size = self.left_right_transfer(images[0], is_left=False)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
else:
pass
# 上下视角
if vertical_sight_direction == 'down':
flag += 1
images[0], matrix, size = self.up_down_transfer(images[0], is_down=True)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
elif vertical_sight_direction == 'up':
flag -= 1
images[0], matrix, size = self.up_down_transfer(images[0], is_down=False)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
else:
pass
# 左下视角 或 右上视角
if abs(flag) == 2:
images[0], matrix, size = self.vertical_tilt_transfer(images[0], is_left_high=True)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
images[0], matrix, size = self.horizontal_tilt_transfer(images[0], is_right_tilt=True)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
# 左上视角 或 右下视角
elif abs(flag) == 1:
images[0], matrix, size = self.vertical_tilt_transfer(images[0], is_left_high=False)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
images[0], matrix, size = self.horizontal_tilt_transfer(images[0], is_right_tilt=False)
for i in range(1, img_num):
images[i] = cv2.warpPerspective(images[i], matrix, size)
else:
pass
return images
@staticmethod
def search_file(search_path, file_format='.jpg'):
"""在指定目录search_path下，递归目录搜索指定尾缀的文件"""
file_path_list = []
for root_path, dir_names, file_names in os.walk(search_path):
for filename in file_names:
if filename.endswith(file_format):
file_path_list.append(os.path.join(root_path, filename))
return file_path_list
@staticmethod
def rand_reduce(val):
return int(np.random.random() * val)
def add_gauss(self, img, level=None):
""" 添加高斯模糊
:param img: 待加噪图片
:param level: 加噪水平
"""
if level is None:
level = self.level
return cv2.blur(img, (level * 2 + 1, level * 2 + 1))
def add_single_channel_noise(self, single):
""" 添加高斯噪声
:param single: 单一通道的图像数据
"""
diff = 255 - single.max()
noise = np.random.normal(0, 1 + self.rand_reduce(6), single.shape)
noise = (noise - noise.min()) / (noise.max() - noise.min())
noise = diff * noise
noise = noise.astype(np.uint8)
dst = single + noise
return dst
def add_noise(self, img):
"""添加噪声"""
img[:, :, 0] = self.add_single_channel_noise(img[:, :, 0])
img[:, :, 1] = self.add_single_channel_noise(img[:, :, 1])
img[:, :, 2] = self.add_single_channel_noise(img[:, :, 2])
return img
def add_smudge(self, img, smu=None):
"""添加污渍"""
if smu is None:
smu = self.smu
# 截取某一部分
rows = self.rand_reduce(smu.shape[0] - img.shape[0])
cols = self.rand_reduce(smu.shape[1] - img.shape[1])
add_smu = smu[rows:rows + img.shape[0], cols:cols + img.shape[1]]
img = cv2.bitwise_not(img)
img = cv2.bitwise_and(add_smu, img)
img = cv2.bitwise_not(img)
return img
def rand_environment(self, img, env_data_paths=None):
""" 添加自然环境的噪声
:param img: 待加噪图片
:param env_data_paths: 自然环境图片路径列表
"""
if env_data_paths is None:
env_data_paths = self.env_data_paths
# 随机选取环境照片
index = self.rand_reduce(len(env_data_paths))
env = cv2.imread(env_data_paths[index])
env = cv2.resize(env, (img.shape[1], img.shape[0]))
# 找到黑背景，反转为白
bak = (img == 0)
for i in range(bak.shape[2]):
bak[:, :, 0] &= bak[:, :, i]
for i in range(bak.shape[2]):
bak[:, :, i] = bak[:, :, 0]
bak = bak.astype(np.uint8) * 255
# 环境照片用白掩码裁剪，然后与原图非黑部分合并
inv = cv2.bitwise_and(bak, env)
img = cv2.bitwise_or(inv, img)
return img
def rand_hsv(self, img):
""" 添加饱和度光照的噪声
:param img: BGR格式的图片
:return 加了饱和度、光照噪声的BGR图片
"""
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# 色调，饱和度，亮度
hsv[:, :, 0] = hsv[:, :, 0] * (self.hue_keep + np.random.random() * (1 - self.hue_keep))
hsv[:, :, 1] = hsv[:, :, 1] * (self.saturation_keep + np.random.random() * (1 - self.saturation_keep))
hsv[:, :, 2] = hsv[:, :, 2] * (self.value_keep + np.random.random() * (1 - self.value_keep))
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img
def augment(self, img, horizontal_sight_direction=None, vertical_sight_direction=None):
"""
综合上面的加载操作，进行全流程加噪
:param img:
:param horizontal_sight_direction: 水平视角方向
:param vertical_sight_direction: 垂直视角方向
:return:
"""
if horizontal_sight_direction is None:
horizontal_sight_direction = ImageAugmentation.horizontal_sight_directions[random.randint(0, 2)]
if vertical_sight_direction is None:
vertical_sight_direction = ImageAugmentation.vertical_sight_directions[random.randint(0, 2)]
if not self.is_black_char:
# 转为黑底白字
img = cv2.bitwise_not(img)
img = cv2.bitwise_or(img, self.template_image)
# 基于视角的变换
img = self.sight_transfer([img], horizontal_sight_direction, vertical_sight_direction)
img = img[0]
img = self.rand_environment(img)
img = self.rand_hsv(img)
else:
# 底牌加车牌文字
img = cv2.bitwise_and(img, self.template_image)
# 基于视角的变换
img = self.sight_transfer([img], horizontal_sight_direction, vertical_sight_direction)
img = img[0]
img = self.rand_environment(img)
img = self.rand_hsv(img)
img = self.add_gauss(img)
img = self.add_noise(img)
img = self.add_smudge(img)
return img
class LicensePlateGenerator(object):
@staticmethod
def generate_license_plate_images(plate_type, plate_num_str_list, save_path):
"""
生成特定类型的的车牌图片，并保存到指定目录下
:param plate_type: 车牌类型
:param plate_num_str_list: 车牌号码列表
:param save_path: 文件根目录
:return:
"""
save_path = os.path.join(save_path, plate_type)
if not os.path.exists(save_path):
os.makedirs(save_path)
print('\r>> 生成车牌号图片...')
# 生成车牌号码，白底黑字
chars_image_generator = CharsImageGenerator(plate_type)
chars_images = chars_image_generator.generate_images(plate_num_str_list)
# 生成车牌底牌
license_template_generator = LicensePlateImageGenerator(plate_type)
template_image = license_template_generator.generate_template_image(chars_image_generator.plate_width, chars_image_generator.plate_height)
print('\r>> 生成车牌图片...')
# 数据增强及车牌字符颜色修正，并保存
augmentation = ImageAugmentation(plate_type, template_image)
plate_height = 72
plate_width = int(chars_image_generator.plate_width * plate_height / chars_image_generator.plate_height)
i = 1
for index, char_image in enumerate(chars_images):
image_name = str(i) + "_" + plate_num_str_list[index] + ".jpg"
image_path = os.path.join(save_path, image_name)
image = augmentation.augment(char_image)
image = cv2.resize(image, (plate_width, plate_height))
cv2.imencode('.jpg', image)[1].tofile(image_path)
print("\r>> {} done...".format(image_name))
i += 1
if __name__ == '__main__':
# 保存文件夹名称
file_path = os.path.join(os.getcwd(), 'plate_images')
# 车牌号码列表
plate_num_str_list = ["浙A5B5T3"]
LicensePlateGenerator.generate_license_plate_images('single_blue', plate_num_str_list, file_path)
