Python+Tensorflow+CNN實(shí)現(xiàn)車(chē)牌識(shí)別的示例代碼
更新時(shí)間：2019年10月11日 09:13:00 作者：蘋(píng)果の芳菲
這篇文章主要介紹了Python+Tensorflow+CNN實(shí)現(xiàn)車(chē)牌識(shí)別的示例代碼，文中通過(guò)示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來(lái)一起學(xué)習(xí)學(xué)習(xí)吧
一、項(xiàng)目概述
本次項(xiàng)目目標(biāo)是實(shí)現(xiàn)對(duì)自動(dòng)生成的帶有各種噪聲的車(chē)牌識(shí)別。在噪聲干擾情況下，車(chē)牌字符分割較困難，此次車(chē)牌識(shí)別是將車(chē)牌7個(gè)字符同時(shí)訓(xùn)練，字符包括31個(gè)省份簡(jiǎn)稱(chēng)、10個(gè)阿拉伯?dāng)?shù)字、24個(gè)英文字母（'O'和'I'除外），共有65個(gè)類(lèi)別，7個(gè)字符使用單獨(dú)的loss函數(shù)進(jìn)行訓(xùn)練。
（運(yùn)行環(huán)境：tensorflow1.14.0-GPU版）
二、生成車(chē)牌數(shù)據(jù)集
import os
import cv2 as cv
import numpy as np
from math import *
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
index = {"京": 0, "滬": 1, "津": 2, "渝": 3, "冀": 4, "晉": 5, "蒙": 6, "遼": 7, "吉": 8, "黑": 9,
"蘇": 10, "浙": 11, "皖": 12, "閩": 13, "贛": 14, "魯": 15, "豫": 16, "鄂": 17, "湘": 18, "粵": 19,
"桂": 20, "瓊": 21, "川": 22, "貴": 23, "云": 24, "藏": 25, "陜": 26, "甘": 27, "青": 28, "寧": 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", "滬", "津", "渝", "冀", "晉", "蒙", "遼", "吉", "黑",
"蘇", "浙", "皖", "閩", "贛", "魯", "豫", "鄂", "湘", "粵",
"桂", "瓊", "川", "貴", "云", "藏", "陜", "甘", "青", "寧",
"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"]
def AddSmudginess(img, Smu):
"""
模糊處理
:param img: 輸入圖像
:param Smu: 模糊圖像
:return: 添加模糊后的圖像
"""
rows = r(Smu.shape[0] - 50)
cols = r(Smu.shape[1] - 50)
adder = Smu[rows:rows + 50, cols:cols + 50]
adder = cv.resize(adder, (50, 50))
img = cv.resize(img,(50,50))
img = cv.bitwise_not(img)
img = cv.bitwise_and(adder, img)
img = cv.bitwise_not(img)
return img
def rot(img, angel, shape, max_angel):
"""
添加透視畸變
"""
size_o = [shape[1], shape[0]]
size = (shape[1]+ int(shape[0] * cos((float(max_angel ) / 180) * 3.14)), shape[0])
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 = cv.getPerspectiveTransform(pts1, pts2)
dst = cv.warpPerspective(img, M, size)
return dst
def rotRandrom(img, factor, size):
"""
添加放射畸變
:param img: 輸入圖像
:param factor: 畸變的參數(shù)
:param size: 圖片目標(biāo)尺寸
:return: 放射畸變后的圖像
"""
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 = cv.getPerspectiveTransform(pts1, pts2)
dst = cv.warpPerspective(img, M, size)
return dst
def tfactor(img):
"""
添加飽和度光照的噪聲
"""
hsv = cv.cvtColor(img,cv.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 = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)
return img
def random_envirment(img, noplate_bg):
"""
添加自然環(huán)境的噪聲, noplate_bg為不含車(chē)牌的背景圖
"""
bg_index = r(len(noplate_bg))
env = cv.imread(noplate_bg[bg_index])
env = cv.resize(env, (img.shape[1], img.shape[0]))
bak = (img == 0)
bak = bak.astype(np.uint8) * 255
inv = cv.bitwise_and(bak, env)
img = cv.bitwise_or(inv, img)
return img
def GenCh(f, val):
"""
生成中文字符
"""
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):
"""
生成英文字符
"""
img =Image.new("RGB", (23, 70), (255, 255, 255))
draw = ImageDraw.Draw(img)
draw.text((0, 2), val, (0, 0, 0), font=f) # val.decode('utf-8')
A = np.array(img)
return A
def AddGauss(img, level):
"""
添加高斯模糊
"""
return cv.blur(img, (level * 2 + 1, level * 2 + 1))
def r(val):
return int(np.random.random() * val)
def AddNoiseSingleChannel(single):
"""
添加高斯噪聲
"""
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.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
class GenPlate:
def __init__(self, fontCh, fontEng, NoPlates):
self.fontC = ImageFont.truetype(fontCh, 43, 0)
self.fontE = ImageFont.truetype(fontEng, 60, 0)
self.img = np.array(Image.new("RGB", (226, 70),(255, 255, 255)))
self.bg = cv.resize(cv.imread("data\\images\\template.bmp"), (226, 70)) # template.bmp:車(chē)牌背景圖
self.smu = cv.imread("data\\images\\smu2.jpg") # smu2.jpg:模糊圖像
self.noplates_path = []
for parent, parent_folder, filenames in os.walk(NoPlates):
for filename in filenames:
path = parent + "\\" + filename
self.noplates_path.append(path)
def draw(self, val):
offset = 2
self.img[0:70, offset+8:offset+8+23] = GenCh(self.fontC, val[0])
self.img[0:70, offset+8+23+6:offset+8+23+6+23] = GenCh1(self.fontE, val[1])
for i in range(5):
base = offset + 8 + 23 + 6 + 23 + 17 + i * 23 + i * 6
self.img[0:70, base:base+23] = GenCh1(self.fontE, val[i+2])
return self.img
def generate(self, text):
if len(text) == 7:
fg = self.draw(text) # decode(encoding="utf-8")
fg = cv.bitwise_not(fg)
com = cv.bitwise_or(fg, self.bg)
com = rot(com, r(60)-30, com.shape,30)
com = rotRandrom(com, 10, (com.shape[1], com.shape[0]))
com = tfactor(com)
com = random_envirment(com, self.noplates_path)
com = AddGauss(com, 1+r(4))
com = addNoise(com)
return com
@staticmethod
def genPlateString(pos, val):
"""
生成車(chē)牌string，存為圖片
生成車(chē)牌list，存為label
"""
plateStr = ""
plateList=[]
box = [0, 0, 0, 0, 0, 0, 0]
if pos != -1:
box[pos] = 1
for unit, cpos in zip(box, range(len(box))):
if unit == 1:
plateStr += val
plateList.append(val)
else:
if cpos == 0:
plateStr += chars[r(31)]
plateList.append(plateStr)
elif cpos == 1:
plateStr += chars[41 + r(24)]
plateList.append(plateStr)
else:
plateStr += chars[31 + r(34)]
plateList.append(plateStr)
plate = [plateList[0]]
b = [plateList[i][-1] for i in range(len(plateList))]
plate.extend(b[1:7])
return plateStr, plate
@staticmethod
def genBatch(batchsize, outputPath, size):
"""
將生成的車(chē)牌圖片寫(xiě)入文件夾，對(duì)應(yīng)的label寫(xiě)入label.txt
:param batchsize: 批次大小
:param outputPath: 輸出圖像的保存路徑
:param size: 輸出圖像的尺寸
:return: None
"""
if not os.path.exists(outputPath):
os.mkdir(outputPath)
outfile = open('data\\plate\\label.txt', 'w', encoding='utf-8')
for i in range(batchsize):
plateStr, plate = G.genPlateString(-1, -1)
# print(plateStr, plate)
img = G.generate(plateStr)
img = cv.resize(img, size)
cv.imwrite(outputPath + "\\" + str(i).zfill(2) + ".jpg", img)
outfile.write(str(plate) + "\n")
if __name__ == '__main__':
G = GenPlate("data\\font\\platech.ttf", 'data\\font\\platechar.ttf', "data\\NoPlates")
G.genBatch(101, 'data\\plate', (272, 72))
生成的車(chē)牌圖像尺寸盡量不要超過(guò)300，本次尺寸選取：272 * 72
生成車(chē)牌所需文件：
- 字體文件：中文‘platech.ttf'，英文及數(shù)字‘platechar.ttf'
- 背景圖：來(lái)源于不含車(chē)牌的車(chē)輛裁剪圖片
- 車(chē)牌（藍(lán)底）：template.bmp
- 噪聲圖像：smu2.jpg
車(chē)牌生成后保存至plate文件夾，示例如下：
三、數(shù)據(jù)導(dǎo)入
from genplate import *
import matplotlib.pyplot as plt
# 產(chǎn)生用于訓(xùn)練的數(shù)據(jù)
class OCRIter:
def __init__(self, batch_size, width, height):
super(OCRIter, self).__init__()
self.genplate = GenPlate("data\\font\\platech.ttf", 'data\\font\\platechar.ttf', "data\\NoPlates")
self.batch_size = batch_size
self.height = height
self.width = width
def iter(self):
data = []
label = []
for i in range(self.batch_size):
img, num = self.gen_sample(self.genplate, self.width, self.height)
data.append(img)
label.append(num)
return np.array(data), np.array(label)
@staticmethod
def rand_range(lo, hi):
return lo + r(hi - lo)
def gen_rand(self):
name = ""
label = list([])
label.append(self.rand_range(0, 31)) #產(chǎn)生車(chē)牌開(kāi)頭32個(gè)省的標(biāo)簽
label.append(self.rand_range(41, 65)) #產(chǎn)生車(chē)牌第二個(gè)字母的標(biāo)簽
for i in range(5):
label.append(self.rand_range(31, 65)) #產(chǎn)生車(chē)牌后續(xù)5個(gè)字母的標(biāo)簽
name += chars[label[0]]
name += chars[label[1]]
for i in range(5):
name += chars[label[i+2]]
return name, label
def gen_sample(self, genplate, width, height):
num, label = self.gen_rand()
img = genplate.generate(num)
img = cv.resize(img, (height, width))
img = np.multiply(img, 1/255.0)
return img, label #返回的label為標(biāo)簽，img為車(chē)牌圖像
'''
# 測(cè)試代碼
O = OCRIter(2, 272, 72)
img, lbl = O.iter()
for im in img:
plt.imshow(im, cmap='gray')
plt.show()
print(img.shape)
print(lbl)
'''
四、CNN模型構(gòu)建
import tensorflow as tf
def cnn_inference(images, keep_prob):
W_conv = {
'conv1': tf.Variable(tf.random.truncated_normal([3, 3, 3, 32],
stddev=0.1)),
'conv2': tf.Variable(tf.random.truncated_normal([3, 3, 32, 32],
stddev=0.1)),
'conv3': tf.Variable(tf.random.truncated_normal([3, 3, 32, 64],
stddev=0.1)),
'conv4': tf.Variable(tf.random.truncated_normal([3, 3, 64, 64],
stddev=0.1)),
'conv5': tf.Variable(tf.random.truncated_normal([3, 3, 64, 128],
stddev=0.1)),
'conv6': tf.Variable(tf.random.truncated_normal([3, 3, 128, 128],
stddev=0.1)),
'fc1_1': tf.Variable(tf.random.truncated_normal([5*30*128, 65],
stddev=0.01)),
'fc1_2': tf.Variable(tf.random.truncated_normal([5*30*128, 65],
stddev=0.01)),
'fc1_3': tf.Variable(tf.random.truncated_normal([5*30*128, 65],
stddev=0.01)),
'fc1_4': tf.Variable(tf.random.truncated_normal([5*30*128, 65],
stddev=0.01)),
'fc1_5': tf.Variable(tf.random.truncated_normal([5*30*128, 65],
stddev=0.01)),
'fc1_6': tf.Variable(tf.random.truncated_normal([5*30*128, 65],
stddev=0.01)),
'fc1_7': tf.Variable(tf.random.truncated_normal([5*30*128, 65],
stddev=0.01)),
}
b_conv = {
'conv1': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[32])),
'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[32])),
'conv3': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[64])),
'conv4': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[64])),
'conv5': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[128])),
'conv6': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[128])),
'fc1_1': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[65])),
'fc1_2': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[65])),
'fc1_3': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[65])),
'fc1_4': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[65])),
'fc1_5': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[65])),
'fc1_6': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[65])),
'fc1_7': tf.Variable(tf.constant(0.1, dtype=tf.float32,
shape=[65])),
}
# 第1層卷積層
conv1 = tf.nn.conv2d(images, W_conv['conv1'], strides=[1,1,1,1], padding='VALID')
conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])
conv1 = tf.nn.relu(conv1)
# 第2層卷積層
conv2 = tf.nn.conv2d(conv1, W_conv['conv2'], strides=[1,1,1,1], padding='VALID')
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = tf.nn.relu(conv2)
# 第1層池化層
pool1 = tf.nn.max_pool2d(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')
# 第3層卷積層
conv3 = tf.nn.conv2d(pool1, W_conv['conv3'], strides=[1,1,1,1], padding='VALID')
conv3 = tf.nn.bias_add(conv3, b_conv['conv3'])
conv3 = tf.nn.relu(conv3)
# 第4層卷積層
conv4 = tf.nn.conv2d(conv3, W_conv['conv4'], strides=[1,1,1,1], padding='VALID')
conv4 = tf.nn.bias_add(conv4, b_conv['conv4'])
conv4 = tf.nn.relu(conv4)
# 第2層池化層
pool2 = tf.nn.max_pool2d(conv4, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')
# 第5層卷積層
conv5 = tf.nn.conv2d(pool2, W_conv['conv5'], strides=[1,1,1,1], padding='VALID')
conv5 = tf.nn.bias_add(conv5, b_conv['conv5'])
conv5 = tf.nn.relu(conv5)
# 第4層卷積層
conv6 = tf.nn.conv2d(conv5, W_conv['conv6'], strides=[1,1,1,1], padding='VALID')
conv6 = tf.nn.bias_add(conv6, b_conv['conv6'])
conv6 = tf.nn.relu(conv6)
# 第3層池化層
pool3 = tf.nn.max_pool2d(conv6, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')
#第1_1層全連接層
# print(pool3.shape)
reshape = tf.reshape(pool3, [-1, 5 * 30 * 128])
fc1 = tf.nn.dropout(reshape, keep_prob)
fc1_1 = tf.add(tf.matmul(fc1, W_conv['fc1_1']), b_conv['fc1_1'])
#第1_2層全連接層
fc1_2 = tf.add(tf.matmul(fc1, W_conv['fc1_2']), b_conv['fc1_2'])
#第1_3層全連接層
fc1_3 = tf.add(tf.matmul(fc1, W_conv['fc1_3']), b_conv['fc1_3'])
#第1_4層全連接層
fc1_4 = tf.add(tf.matmul(fc1, W_conv['fc1_4']), b_conv['fc1_4'])
#第1_5層全連接層
fc1_5 = tf.add(tf.matmul(fc1, W_conv['fc1_5']), b_conv['fc1_5'])
#第1_6層全連接層
fc1_6 = tf.add(tf.matmul(fc1, W_conv['fc1_6']), b_conv['fc1_6'])
#第1_7層全連接層
fc1_7 = tf.add(tf.matmul(fc1, W_conv['fc1_7']), b_conv['fc1_7'])
return fc1_1, fc1_2, fc1_3, fc1_4, fc1_5, fc1_6, fc1_7
def calc_loss(logit1, logit2, logit3, logit4, logit5, logit6, logit7, labels):
labels = tf.convert_to_tensor(labels, tf.int32)
loss1 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logit1, labels=labels[:, 0]))
tf.compat.v1.summary.scalar('loss1', loss1)
loss2 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logit2, labels=labels[:, 1]))
tf.compat.v1.summary.scalar('loss2', loss2)
loss3 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logit3, labels=labels[:, 2]))
tf.compat.v1.summary.scalar('loss3', loss3)
loss4 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logit4, labels=labels[:, 3]))
tf.compat.v1.summary.scalar('loss4', loss4)
loss5 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logit5, labels=labels[:, 4]))
tf.compat.v1.summary.scalar('loss5', loss5)
loss6 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logit6, labels=labels[:, 5]))
tf.compat.v1.summary.scalar('loss6', loss6)
loss7 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logit7, labels=labels[:, 6]))
tf.compat.v1.summary.scalar('loss7', loss7)
return loss1, loss2, loss3, loss4, loss5, loss6, loss7
def train_step(loss1, loss2, loss3, loss4, loss5, loss6, loss7, learning_rate):
optimizer1 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_op1 = optimizer1.minimize(loss1)
optimizer2 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_op2 = optimizer2.minimize(loss2)
optimizer3 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_op3 = optimizer3.minimize(loss3)
optimizer4 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_op4 = optimizer4.minimize(loss4)
optimizer5 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_op5 = optimizer5.minimize(loss5)
optimizer6 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_op6 = optimizer6.minimize(loss6)
optimizer7 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_op7 = optimizer7.minimize(loss7)
return train_op1, train_op2, train_op3, train_op4, train_op5, train_op6, train_op7
def pred_model(logit1, logit2, logit3, logit4, logit5, logit6, logit7, labels):
labels = tf.convert_to_tensor(labels, tf.int32)
labels = tf.reshape(tf.transpose(labels), [-1])
logits = tf.concat([logit1, logit2, logit3, logit4, logit5, logit6, logit7], 0)
prediction = tf.nn.in_top_k(logits, labels, 1)
accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
tf.compat.v1.summary.scalar('accuracy', accuracy)
return accuracy
五、模型訓(xùn)練
import os
import time
import datetime
import numpy as np
import tensorflow as tf
from input_data import OCRIter
import model
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'
img_h = 72
img_w = 272
num_label = 7
batch_size = 32
epoch = 10000
learning_rate = 0.0001
logs_path = 'logs\\1005'
model_path = 'saved_model\\1005'
image_holder = tf.compat.v1.placeholder(tf.float32, [batch_size, img_h, img_w, 3])
label_holder = tf.compat.v1.placeholder(tf.int32, [batch_size, 7])
keep_prob = tf.compat.v1.placeholder(tf.float32)
def get_batch():
data_batch = OCRIter(batch_size, img_h, img_w)
image_batch, label_batch = data_batch.iter()
return np.array(image_batch), np.array(label_batch)
logit1, logit2, logit3, logit4, logit5, logit6, logit7 = model.cnn_inference(
image_holder, keep_prob)
loss1, loss2, loss3, loss4, loss5, loss6, loss7 = model.calc_loss(
logit1, logit2, logit3, logit4, logit5, logit6, logit7, label_holder)
train_op1, train_op2, train_op3, train_op4, train_op5, train_op6, train_op7 = model.train_step(
loss1, loss2, loss3, loss4, loss5, loss6, loss7, learning_rate)
accuracy = model.pred_model(logit1, logit2, logit3, logit4, logit5, logit6, logit7, label_holder)
input_image=tf.compat.v1.summary.image('input', image_holder)
summary_op = tf.compat.v1.summary.merge(tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.SUMMARIES))
init_op = tf.compat.v1.global_variables_initializer()
with tf.compat.v1.Session() as sess:
sess.run(init_op)
train_writer = tf.compat.v1.summary.FileWriter(logs_path, sess.graph)
saver = tf.compat.v1.train.Saver()
start_time1 = time.time()
for step in range(epoch):
# 生成車(chē)牌圖像以及標(biāo)簽數(shù)據(jù)
img_batch, lbl_batch = get_batch()
start_time2 = time.time()
time_str = datetime.datetime.now().isoformat()
feed_dict = {image_holder:img_batch, label_holder:lbl_batch, keep_prob:0.6}
_1, _2, _3, _4, _5, _6, _7, ls1, ls2, ls3, ls4, ls5, ls6, ls7, acc = sess.run(
[train_op1, train_op2, train_op3, train_op4, train_op5, train_op6, train_op7,
loss1, loss2, loss3, loss4, loss5, loss6, loss7, accuracy], feed_dict)
summary_str = sess.run(summary_op, feed_dict)
train_writer.add_summary(summary_str,step)
duration = time.time() - start_time2
loss_total = ls1 + ls2 + ls3 + ls4 + ls5 + ls6 + ls7
if step % 10 == 0:
sec_per_batch = float(duration)
print('%s: Step %d, loss_total = %.2f, acc = %.2f%%, sec/batch = %.2f' %
(time_str, step, loss_total, acc * 100, sec_per_batch))
if step % 5000 == 0 or (step + 1) == epoch:
checkpoint_path = os.path.join(model_path,'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
end_time = time.time()
print("Training over. It costs {:.2f} minutes".format((end_time - start_time1) / 60))
六、訓(xùn)練結(jié)果展示
訓(xùn)練參數(shù)：
batch_size = 32
epoch = 10000
learning_rate = 0.0001
在tensorboard中查看訓(xùn)練過(guò)程
accuracy :
accuracy
曲線在epoch = 10000左右時(shí)達(dá)到收斂，最終精確度在94%左右
loss :
以上三張分別是loss1，loss2， loss7的曲線圖像，一號(hào)位字符是省份簡(jiǎn)稱(chēng)，識(shí)別相對(duì)字母數(shù)字較難，loss1=0.08左右，二號(hào)位字符是字母，loss2穩(wěn)定在0.001左右，但是隨著字符往后，loss值也將越來(lái)越大，7號(hào)位字符loss7穩(wěn)定在0.6左右。
七、預(yù)測(cè)單張車(chē)牌
import os
import cv2 as cv
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
import model
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3' # 只顯示 Error
index = {"京": 0, "滬": 1, "津": 2, "渝": 3, "冀": 4, "晉": 5, "蒙": 6, "遼": 7, "吉": 8, "黑": 9,
"蘇": 10, "浙": 11, "皖": 12, "閩": 13, "贛": 14, "魯": 15, "豫": 16, "鄂": 17, "湘": 18, "粵": 19,
"桂": 20, "瓊": 21, "川": 22, "貴": 23, "云": 24, "藏": 25, "陜": 26, "甘": 27, "青": 28, "寧": 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", "滬", "津", "渝", "冀", "晉", "蒙", "遼", "吉", "黑",
"蘇", "浙", "皖", "閩", "贛", "魯", "豫", "鄂", "湘", "粵",
"桂", "瓊", "川", "貴", "云", "藏", "陜", "甘", "青", "寧",
"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"]
def get_one_image(test):
""" 隨機(jī)獲取單張車(chē)牌圖像 """
n = len(test)
rand_num =np.random.randint(0,n)
img_dir = test[rand_num]
image_show = Image.open(img_dir)
plt.imshow(image_show) # 顯示車(chē)牌圖片
image = cv.imread(img_dir)
image = image.reshape(72, 272, 3)
image = np.multiply(image, 1 / 255.0)
return image
batch_size = 1
x = tf.compat.v1.placeholder(tf.float32, [batch_size, 72, 272, 3])
keep_prob = tf.compat.v1.placeholder(tf.float32)
test_dir = 'data\\plate\\'
test_image = []
for file in os.listdir(test_dir):
test_image.append(test_dir + file)
test_image = list(test_image)
image_array = get_one_image(test_image)
logit1, logit2, logit3, logit4, logit5, logit6, logit7 = model.cnn_inference(x, keep_prob)
model_path = 'saved_model\\1005'
saver = tf.compat.v1.train.Saver()
with tf.compat.v1.Session() as sess:
print ("Reading checkpoint...")
ckpt = tf.train.get_checkpoint_state(model_path)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
pre1, pre2, pre3, pre4, pre5, pre6, pre7 = sess.run(
[logit1, logit2, logit3, logit4, logit5, logit6, logit7],
feed_dict={x:image_array, keep_prob:1.0})
prediction = np.reshape(np.array([pre1, pre2, pre3, pre4, pre5, pre6, pre7]), [-1, 65])
max_index = np.argmax(prediction, axis=1)
print(max_index)
line = ''
result = np.array([])
for i in range(prediction.shape[0]):
if i == 0:
result = np.argmax(prediction[i][0:31])
if i == 1:
result = np.argmax(prediction[i][41:65]) + 41
if i > 1:
result = np.argmax(prediction[i][31:65]) + 31
line += chars[result]+" "
print ('predicted: ' + line)
plt.show()
隨機(jī)測(cè)試20張車(chē)牌，18張預(yù)測(cè)正確，2張預(yù)測(cè)錯(cuò)誤，從最后兩幅預(yù)測(cè)錯(cuò)誤的圖片可以看出，模型對(duì)相似字符以及遮擋字符識(shí)別成功率仍有待提高。測(cè)試結(jié)果部分展示如下：
八、總結(jié)
本次構(gòu)建的CNN模型較為簡(jiǎn)單，只有6卷積層+3池化層+1全連接層，可以通過(guò)增加模型深度以及每層之間的神經(jīng)元數(shù)量來(lái)優(yōu)化模型，提高識(shí)別的準(zhǔn)確率。此次訓(xùn)練數(shù)據(jù)集來(lái)源于自動(dòng)生成的車(chē)牌，由于真實(shí)的車(chē)牌圖像與生成的車(chē)牌圖像在噪聲干擾上有所區(qū)分，所以識(shí)別率上會(huì)有所出入。如果使用真實(shí)的車(chē)牌數(shù)據(jù)集，需要對(duì)車(chē)牌進(jìn)行濾波、均衡化、腐蝕、矢量量化等預(yù)處理方法。
以上就是本文的全部?jī)?nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持腳本之家。
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