import cv2
import numpy as np
from numpy.linalg import norm
import sys
import os
import json
SZ = 20 #训练图片长宽
MAX_WIDTH = 1000 #原始图片最大宽度
Min_Area = 2000 #车牌区域允许最大面积
PROVINCE_START = 1000
#读取图片文件
def imreadex(filename):
return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)
def point_limit(point):
if point[0] < 0:
point[0] = 0
if point[1] < 0:
point[1] = 0
#根据设定的阈值和图片直方图,找出波峰,用于分隔字符
def find_waves(threshold, histogram):
up_point = -1#上升点
is_peak = False
if histogram[0] > threshold:
up_point = 0
is_peak = True
wave_peaks = []
for i,x in enumerate(histogram):
if is_peak and x < threshold:
if i - up_point > 2:
is_peak = False
wave_peaks.append((up_point, i))
elif not is_peak and x >= threshold:
is_peak = True
up_point = i
if is_peak and up_point != -1 and i - up_point > 4:
wave_peaks.append((up_point, i))
return wave_peaks
#根据找出的波峰,分隔图片,从而得到逐个字符图片
def seperate_card(img, waves):
part_cards = []
for wave in waves:
part_cards.append(img[:, wave[0]:wave[1]])
return part_cards
#来自opencv的sample,用于svm训练
def deskew(img):
m = cv2.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
return img
#来自opencv的sample,用于svm训练
def preprocess_hog(digits):
samples = []
for img in digits:
gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
mag, ang = cv2.cartToPolar(gx, gy)
bin_n = 16
bin = np.int32(bin_n*ang/(2*np.pi))
bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
hist = np.hstack(hists)
# transform to Hellinger kernel
eps = 1e-7
hist /= hist.sum() + eps
hist = np.sqrt(hist)
hist /= norm(hist) + eps
samples.append(hist)
return np.float32(samples)
#不能保证包括所有省份
provinces = [
"zh_cuan", "川",
"zh_e", "鄂",
"zh_gan", "赣",
"zh_gan1", "甘",
"zh_gui", "贵",
"zh_gui1", "桂",
"zh_hei", "黑",
"zh_hu", "沪",
"zh_ji", "冀",
"zh_jin", "津",
"zh_jing", "京",
"zh_jl", "吉",
"zh_liao", "辽",
"zh_lu", "鲁",
"zh_meng", "蒙",
"zh_min", "闽",
"zh_ning", "宁",
"zh_qing", "靑",
"zh_qiong", "琼",
"zh_shan", "陕",
"zh_su", "苏",
"zh_sx", "晋",
"zh_wan", "皖",
"zh_xiang", "湘",
"zh_xin", "新",
"zh_yu", "豫",
"zh_yu1", "渝",
"zh_yue", "粤",
"zh_yun", "云",
"zh_zang", "藏",
"zh_zhe", "浙"
]
class StatModel(object):
def load(self, fn):
self.model = self.model.load(fn)
def save(self, fn):
self.model.save(fn)
class SVM(StatModel):
def __init__(self, C = 1, gamma = 0.5):
self.model = cv2.ml.SVM_create()
self.model.setGamma(gamma)
self.model.setC(C)
self.model.setKernel(cv2.ml.SVM_RBF)
self.model.setType(cv2.ml.SVM_C_SVC)
#训练svm
def train(self, samples, responses):
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
#字符识别
def predict(self, samples):
r = self.model.predict(samples)
return r[1].ravel()
class CardPredictor:
def __init__(self):
#车牌识别的部分参数保存在js中,便于根据图片分辨率做调整
f = open('config.js')
j = json.load(f)
for c in j["config"]:
if c["open"]:
self.cfg = c.copy()
break
else:
raise RuntimeError('没有设置有效配置参数')
def __del__(self):
self.save_traindata()
def train_svm(self):
#识别英文字母和数字
self.model = SVM(C=1, gamma=0.5)
#识别中文
self.modelchinese = SVM(C=1, gamma=0.5)
if os.path.exists("svm.dat"):
self.model.load("svm.dat")
else:
chars_train = []
chars_label = []
for root, dirs, files in os.walk("train\\chars2"):
if len(os.path.basename(root)) > 1:
continue
root_int = ord(os.path.basename(root))
for filename in files:
filepath = os.path.join(root,filename)
digit_img = cv2.imread(filepath)
digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)
chars_train.append(digit_img)
#chars_label.append(1)
chars_label.append(root_int)
chars_train = list(map(deskew, chars_train))
chars_train = preprocess_hog(chars_train)
#chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)
chars_label = np.array(chars_label)
print(chars_train.shape)
self.model.train(chars_train, chars_label)
if os.path.exists("svmchinese.dat"):
self.modelchinese.load("svmchinese.dat")
else:
chars_train = []
chars_label = []
for root, dirs, files in os.walk("train\\charsChinese"):
if not os.path.basename(root).startswith("zh_"):
continue
pinyin = os.path.basename(root)
index = provinces.index(pinyin) + PROVINCE_START + 1 #1是拼音对应的汉字
for filename in files:
filepath = os.path.join(root,filename)
digit_img = cv2.imread(filepath)
digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)
chars_train.append(digit_img)
#chars_label.append(1)
chars_label.append(index)
chars_train = list(map(deskew, chars_train))
chars_train = preprocess_hog(chars_train)
#chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)
chars_label = np.array(chars_label)
print(chars_train.shape)
self.modelchinese.train(chars_train, chars_label)
def save_traindata(self):
if not os.path.exists("svm.dat"):
self.model.save("svm.dat")
if not os.path.exists("svmchinese.dat"):
self.modelchinese.save("svmchinese.dat")
def accurate_place(self, card_img_hsv, limit1, limit2, color):
row_num, col_num = card_img_hsv.shape[:2]
xl = col_num
xr = 0
yh = 0
yl = row_num
#col_num_limit = self.cfg["col_num_limit"]
row_num_limit = self.cfg["row_num_limit"]
col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5#绿色有渐变
for i in range(row_num):
count = 0
for j in range(col_num):
H = card_img_hsv.item(i, j, 0)
S = card_img_hsv.item(i, j, 1)
V = card_img_hsv.item(i, j, 2)
if limit1 < H <= limit2 and 34 < S and 46 < V:
count += 1
if count > col_num_limit:
if yl > i:
yl = i
if yh < i:
yh = i
for j in range(col_num):
count = 0
for i in range(row_num):
H = card_img_hsv.item(i, j, 0)
S = card_img_hsv.item(i, j, 1)
V = card_img_hsv.item(i, j, 2)
if limit1 < H <= limit2 and 34 < S and 46 < V:
count += 1
if count > row_num - row_num_limit:
if xl > j:
xl = j
if xr < j:
xr = j
return xl, xr, yh, yl
def predict(self, car_pic):
if type(car_pic) == type(""):
img = imreadex(car_pic)
else:
img = car_pic
pic_hight, pic_width = img.shape[:2]
if pic_width > MAX_WIDTH:
resize_rate = MAX_WIDTH / pic_width
img = cv2.resize(img, (MAX_WIDTH, int(pic_hight*resize_rate)), interpolation=cv2.INTER_AREA)
blur = self.cfg["blur"]
#高斯去噪
if blur > 0:
img = cv2.GaussianBlur(img, (blur, blur), 0)#图片分辨率调整
oldimg = img
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#equ = cv2.equalizeHist(img)
#img = np.hstack((img, equ))
#去掉图像中不会是车牌的区域
kernel = np.ones((20, 20), np.uint8)
img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0);
#找到图像边缘
ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_edge = cv2.Canny(img_thresh, 100, 200)
#使用开运算和闭运算让图像边缘成为一个整体
kernel = np.ones((self.cfg["morphologyr"], self.cfg["morphologyc"]), np.uint8)
img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel)
img_edge2 = cv2.morphologyEx(img_edge1, cv
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毕业设计:基于Python+OpenCV的车牌识别系统.zip (29个子文件)
Graduation Design
train
chars2.7z 3.18MB
charsChinese.7z 1.05MB
.gitattributes 66B
predict.py 17KB
surface.py 4KB
.idea
LicensePlateRecognition.iml 398B
vcs.xml 180B
workspace.xml 2KB
misc.xml 288B
modules.xml 298B
svm.dat 4.32MB
config.js 243B
test
wA87271.jpg 51KB
2.jpg 2.59MB
1.jpg 4.33MB
u=1691206349,1754151067&fm=26&gp=0.jpg 24KB
下载.jpg 14KB
car3.jpg 26KB
wATH859.jpg 113KB
u=1955857547,2196176231&fm=26&gp=0.jpg 33KB
car5.jpg 28KB
car4.jpg 25KB
car7.jpg 27KB
u=3022089789,3948911321&fm=26&gp=0.jpg 49KB
lLD9016.jpg 24KB
cAA662F.jpg 61KB
wAUB816.jpg 138KB
svmchinese.dat 3.43MB
__pycache__
predict.cpython-37.pyc 13KB
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