图像分类之花卉图像分类（五）测试数据
这个时候测试集就要用到了，为了便于观察，我们这里先给测试集重命名，这样子，哪张图片分类错了，我们也比较好找。重命名代码：#!/usr/bin/python# -*- coding:utf-8 -*-import osouter_path = 'D:/flowers/test'folderlist = os.listdir(outer_path)# 列举文件夹for folde...
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这个时候测试集就要用到了，为了便于观察，我们这里先给测试集重命名，这样子，哪张图片分类错了，我们也比较好找。
重命名代码：
#!/usr/bin/python
# -*- coding:utf-8 -*-
import os
outer_path = 'D:/flowers/test'
folderlist = os.listdir(outer_path) # 列举文件夹
for folder in folderlist:
inner_path = os.path.join(outer_path, folder)
total_num_folder = len(folderlist) # 文件夹的总数
print('total have %d folders' % (total_num_folder))
filelist = os.listdir(inner_path) # 列举图片
i = 0
for item in filelist:
total_num_file = len(filelist) # 单个文件夹内图片的总数
if item.endswith('.jpg'):
src = os.path.join(os.path.abspath(inner_path), item) # 原图的地址
dst = os.path.join(os.path.abspath(inner_path), str(folder) + '_' + str(
i) + '.jpg') # 新图的地址（这里可以把str(folder) + '_' + str(i) + '.jpg'改成你想改的名称）
try:
os.rename(src, dst)
print
'converting %s to %s ...' % (src, dst)
i += 1
except:
continue
print
'total %d to rename & converted %d jpgs' % (total_num_file, i)
随便打开测试集的，可以看到结果如下：
下面说下测试代码：
测试一整个文件夹的，这里的整个文件夹并不是指test文件夹，而是test文件夹下的五个子文件夹的任意一个，代码restall.py如下：
from PIL import Image
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import model
#from input_data import get_files
import input_data
import os
import warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
warnings.filterwarnings('ignore')
#自己改最后面的sunflowers改成别的
img_dir = 'D:/test/sunflowers/'
# 测试图片
def evaluate_one_image(image_array):
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 5
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 64, 64, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES,1,0)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[64, 64, 3])
logs_train_dir = 'D:/biye/save/train'
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print('No checkpoint file found')
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
return max_index
# ------------------------------------------------------------------------
if __name__ == '__main__':
roses=0
tulips=0
dandelion=0
sunflowers=0
daisy=0
for name in os.listdir(img_dir):
i=img_dir+name
img = Image.open(i)
imag = img.resize([64, 64])
image = np.array(imag)
if evaluate_one_image(image) == 0:
print(i+'可能是玫瑰花')
roses+=1
elif evaluate_one_image(image) == 1:
print (i+'可能是郁金香')
tulips+=1
elif evaluate_one_image(image) == 2:
print(i+'可能是蒲公英')
dandelion+=1
elif evaluate_one_image(image) == 4:
print(i+'有可能是雏菊')
daisy+=1
elif evaluate_one_image(image) == 3:
print(i+'可能是向日葵')
sunflowers+=1
print('玫瑰花：%d 郁金香：%d' %(roses,tulips))
print('蒲公英：%d 雏菊：%d 向日葵：%d' %(dandelion,daisy,sunflowers))
#!/bin/python
import wx
# from test import evaluate_one_image
from PIL import Image
import numpy as np
import tensorflow as tf
import model
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def evaluate_one_image(image_array):
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 5
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 64, 64, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES,1,0)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[64, 64, 3])
logs_train_dir = 'D:/biye/save/train'
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print('No checkpoint file found')
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
return max_index
class HelloFrame(wx.Frame):
def __init__(self,*args,**kw):
super(HelloFrame,self).__init__(*args,**kw)
pnl = wx.Panel(self)
self.pnl = pnl
st = wx.StaticText(pnl, pos=(200, 0))
font = st.GetFont()
font.PointSize += 10
font = font.Bold()
st.SetFont(font)
# 选择图像文件按钮
btn = wx.Button(pnl, -1, "select")
btn.Bind(wx.EVT_BUTTON, self.OnSelect)
self.makeMenuBar()
self.CreateStatusBar()
def makeMenuBar(self):
menuBar = wx.MenuBar()
self.SetMenuBar(menuBar)
def OnSelect(self, event):
wildcard = "image source(*.jpg)|*.jpg|" \
"Compile Python(*.pyc)|*.pyc|" \
"All file(*.*)|*.*"
dialog = wx.FileDialog(None, "Choose a file", os.getcwd(),
"", wildcard, wx.ID_OPEN)
if dialog.ShowModal() == wx.ID_OK:
print(dialog.GetPath())
img = Image.open(dialog.GetPath())
imag = img.resize([64, 64])
image = np.array(imag)
print(evaluate_one_image(image))
if evaluate_one_image(image) == 0:
result = ('这可能是玫瑰花')
elif evaluate_one_image(image) == 1:
result = ('这可能是郁金香')
elif evaluate_one_image(image) == 2:
result = ('这可能是蒲公英')
elif evaluate_one_image(image) == 4:
result = ('这有可能是雏菊')
elif evaluate_one_image(image) == 3:
result = ('这可能是向日葵')
# result =evaluate_one_image(image)
result_text = wx.StaticText(self.pnl, label=result, pos=(320, 0))
font = result_text.GetFont()
font.PointSize += 8
result_text.SetFont(font)
self.initimage(name= dialog.GetPath())
# 生成图片控件
def initimage(self, name):
imageShow = wx.Image(name, wx.BITMAP_TYPE_ANY)
sb = wx.StaticBitmap(self.pnl, -1, imageShow.ConvertToBitmap(), pos=(0,30), size=(600,600))
return sb
if __name__ == '__main__':
app = wx.App()
frm = HelloFrame(None, title='基于卷积神经网络的花卉图像识别', size=(600,600))
frm.Show()
app.MainLoop()
所有的代码就都列出来了。原本github上的源代码准确率只有70左右，经过数据增强后（也就是训练集由2500张变成7500张后），准确率提高了很多，我的是到了80了。
源码的dropout的代码注释了，也就是没用到，我是用到了，还调了最合适的也就是0.45.这里要注意训练的时候是0.45.但是验证集和测试集上都是1.用了这个准确率到了85.左右了。
其实还可以再高的，如果你还想提升，可以考虑调参，加几层网络，毕竟我的网络结构太简单了。数据增强有很多方式，你也可以试试别的。
运行代码的顺序是先运行数据增强的代码----裁剪大小的代码（两次）------训练的代码----重命名代码（也可以不要）----测试代码
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