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PlateCommon.py
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#coding=utf-8
import os
import random
# import sys
import numpy as np
import cv2
import argparse
# import PIL
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
from math import *
INDEX_PROVINCE = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9,
"苏": 10, "浙": 11, "皖": 12, "闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19,
"桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24, "藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29,
"新": 30}
INDEX_DIGIT = {"0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36, "6": 37, "7": 38, "8": 39, "9": 40}
INDEX_LETTER = {"A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47,
"H": 48, "J": 49, "K": 50, "L": 51, "M": 52, "N": 53,
"P": 54, "Q": 55, "R": 56, "S": 57, "T": 58,
"U": 59, "V": 60, "W": 61, "X": 62, "Y": 63, "Z": 64}
PLATE_CHARS_PROVINCE = {"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑",
"苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤",
"桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁",
"新"}
PLATE_CHARS_DIGIT = {"0", "1", "2", "3", "4", "5", "6", "7", "8", "9"}
PLATE_CHARS_LETTER = {"A", "B", "C", "D", "E", "F", "G",
"H", "J", "K", "L", "M", "N",
"P", "Q", "R", "S", "T",
"U", "V", "W", "X", "Y", "Z"}
index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12,
"闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24,
"藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
"6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
"J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
"W": 61, "X": 62, "Y": 63, "Z": 64}
chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
"Y", "Z"]
# 增加脏污效果
def AddSmudginess(img, Smu):
img_h, img_w = img.shape[:2]
rows = r(Smu.shape[0] - img_h)
cols = r(Smu.shape[1] - img_w)
adder = Smu[rows:rows + img_h, cols:cols + img_w]
adder = cv2.resize(adder, (img_w, img_h))
adder = cv2.bitwise_not(adder)
# adder = cv2.bitwise_not(adder)
# img = cv2.resize(img,(50,50))
# img = cv2.bitwise_not(img)
# img = cv2.bitwise_and(adder, img)
# img = cv2.bitwise_not(img)
val = random.random() * 0.5
img = cv2.addWeighted(img, 1 - val, adder, val, 0.0)
return img
def rot(img, angel, shape, max_angel):
""" 使图像轻微的畸变
img 输入图像
factor 畸变的参数
size 为图片的目标尺寸
"""
size_o = [shape[1],shape[0]]
# print size_o
# size = (shape[1]+ int(shape[0]*cos((float(max_angel )/180) * 3.14)),shape[0])
# print size
size = (shape[1] + int(shape[0] * sin((float(max_angel) / 180) * 3.14)), shape[0])
# print size
interval = abs(int(sin((float(angel) / 180) * 3.14) * shape[0]))
pts1 = np.float32([[0, 0], [0, size_o[1]], [size_o[0], 0], [size_o[0], size_o[1]]])
if(angel > 0):
pts2 = np.float32([[interval, 0], [0, size[1]], [size[0], 0], [size[0] - interval, size_o[1]]])
else:
pts2 = np.float32([[0, 0], [interval, size[1]], [size[0] - interval, 0], [size[0], size_o[1]]])
M = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, M, size)
return dst
def rotRandrom(img, factor, size):
shape = size
pts1 = np.float32([[0, 0], [0, shape[0]], [shape[1], 0], [shape[1], shape[0]]])
pts2 = np.float32([[r(factor), r(factor)],
[ r(factor), shape[0] - r(factor)],
[shape[1] - r(factor), r(factor)],
[shape[1] - r(factor), shape[0] - r(factor)]])
M = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, M, size)
return dst
def tfactor(img):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
hsv[:, :, 0] = hsv[:, :, 0] * (0.8 + np.random.random() * 0.2)
hsv[:, :, 1] = hsv[:, :, 1] * (0.3 + np.random.random() * 0.7)
hsv[:, :, 2] = hsv[:, :, 2] * (0.2 + np.random.random() * 0.8)
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img
def random_envirment(img, data_set):
index = r(len(data_set))
env = cv2.imread(data_set[index])
env = cv2.resize(env, (img.shape[1], img.shape[0]))
# bak = (img==0);
# bak = bak.astype(np.uint8)*255;
# inv = cv2.bitwise_and(bak,env)
# img = cv2.bitwise_or(inv,img)
val = random.random() * 0.4
img = cv2.addWeighted(img, 1 - val, env, val, 0.0)
return img
def random_scene(img, data_set):
'''将车牌放入自然场景图像中，并返回该图像和位置信息'''
bg_img_path = data_set[r(len(data_set))]
print(bg_img_path)
env = cv2.imread(bg_img_path)
if env is None:
print(bg_img_path, 'is not a good file')
return None, None
# print env.shape, img.shape
# 如果背景图片小于（65，21）则不使用
if env.shape[1] <= 65 or env.shape[0] <= 21:
print(env.shape)
return None, None
# 车牌宽高比变化范围是(1.5, 4.0)
new_height = img.shape[0] * (0.5 + np.random.random()) # 0.5 -- 1.5
new_width = img.shape[1] * (0.5 + np.random.random()) # 0.5 -- 1.5
scale = 0.3 + np.random.random() * 2.5
new_width = int(new_width * scale + 0.5)
new_height = int(new_height * scale + 0.5)
img = cv2.resize(img, (new_width, new_height))
if env.shape[1] <= img.shape[1] or env.shape[0] <= img.shape[0]:
print(env.shape, '---', img.shape)
return None, None
x = r(env.shape[1] - img.shape[1])
y = r(env.shape[0] - img.shape[0])
bak = (img == 0)
bak = bak.astype(np.uint8) * 255
inv = cv2.bitwise_and(bak, env[y:y + new_height, x:x + new_width, :])
img = cv2.bitwise_or(inv, img)
env[y:y + new_height, x:x + new_width, :] = img[:, :, :]
return env, (x, y, x + img.shape[1], y + img.shape[0])
def GenCh(f, val):
img = Image.new("RGB", (45, 70), (255, 255, 255))
draw = ImageDraw.Draw(img)
draw.text((0, 3), val, (0, 0, 0), font=f)
img = img.resize((23, 70))
A = np.array(img)
return A
def GenCh1(f, val):
img = Image.new("RGB", (23, 70), (255, 255, 255))
draw = ImageDraw.Draw(img)
# draw.text((0, 2), val.decode('utf-8'), (0, 0, 0), font=f)
draw.text((0, 2), val, (0, 0, 0), font=f)
A = np.array(img)
return A
def AddGauss(img, level):
return cv2.blur(img, (level * 2 + 1, level * 2 + 1))
def r(val):
return int(np.random.random() * val)
def AddNoiseSingleChannel(single):
diff = 255 - single.max()
noise = np.random.normal(0, 1 + r(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 addNoise(img, sdev=0.5, avg=10):
img[:, :, 0] = AddNoiseSingleChannel(img[:, :, 0])
img[:, :, 1] = AddNoiseSingleChannel(img[:, :, 1])
img[:, :, 2] = AddNoiseSingleChannel(img[:, :, 2])
return img