python 图像归一化代码代编程代写图python

日期：2018-05-04 04:40

from PIL import Image

import os

import sys

import numpy as np

import time

from sklearn import svm

# 获取指定路径下的所有 .png 文件

def get_file_list(path):

return [os.path.join(path, f) for f in os.listdir(path) if f.endswith(".png")]

# 解析出 .png 图件文件的名称

def get_img_name_str(imgPath):

return imgPath.split(os.path.sep)[-1]

# 将 20px * 20px 的图像数据转换成 1*400 的 numpy 向量

# 参数：imgFile--图像名 如：0_1.png

# 返回：1*400 的 numpy 向量

def img2vector(imgFile):

#print("in img2vector func--para:{}".format(imgFile))

img = Image.open(imgFile).convert('L')

img_arr = np.array(img, 'i') # 20px * 20px 灰度图像

img_normalization = np.round(img_arr/255) # 对灰度值进行归一化

img_arr2 = np.reshape(img_normalization, (1,-1)) # 1 * 400 矩阵

return img_arr2

# 读取一个类别的所有数据并转换成矩阵

# 参数：

# basePath: 图像数据所在的基本路径

# Mnist-image/train/

# Mnist-image/test/

# cla：类别名称

# 0,1,2,...,9

# 返回：某一类别的所有数据----[样本数量*(图像宽x图像高)] 矩阵

def read_and_convert(imgFileList):

dataLabel = [] # 存放类标签

dataNum = len(imgFileList)

dataMat = np.zeros((dataNum, 400)) # dataNum * 400 的矩阵

for i in range(dataNum):

imgNameStr = imgFileList[i]

imgName = get_img_name_str(imgNameStr) # 得到 数字_实例编号.png

#print("imgName: {}".format(imgName))

classTag = imgName.split(".")[0].split("_")[0] # 得到 类标签(数字)

#print("classTag: {}".format(classTag))

dataLabel.append(classTag)

dataMat[i,:] = img2vector(imgNameStr)

return dataMat, dataLabel

# 读取训练数据

def read_all_data():

cName = ['1', '2', '3', '4', '5', '6', '7', '8', '9']

train_data_path = "Mnist-image\\train\\0"

flist = get_file_list(train_data_path)

dataMat, dataLabel = read_and_convert(flist)

for c in cName:

train_data_path_ = "Mnist-image\\train\\" + c

flist_ = get_file_list(train_data_path_)

dataMat_, dataLabel_ = read_and_convert(flist_)

dataMat = np.concatenate((dataMat, dataMat_), axis=0)

dataLabel = np.concatenate((dataLabel, dataLabel_), axis=0)

#print(dataMat.shape)

#print(len(dataLabel))

return dataMat, dataLabel

# create model

def create_svm(dataMat, dataLabel, decision='ovr'):

clf = svm.SVC(decision_function_shape=decision)

clf.fit(dataMat, dataLabel)

return clf

#clf = svm.SVC(decision_function_shape='ovr')

st = time.clock()

clf = create_svm(dataMat, dataLabel, decision='ovr')

et = time.clock()

print("Training spent {:.4f}s.".format((et-st)))

# 对10个数字进行分类测试

def main():

tbasePath = "Mnist-image\\test\\"

tcName = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

tst = time.clock()

allErrCount = 0

allErrorRate = 0.0

allScore = 0.0

for tcn in tcName:

testPath = "Mnist-image\\test\\" + tcn

#print("class " + tcn + " path is: {}.".format(testPath))

tflist = get_file_list(testPath)

#tflist

tdataMat, tdataLabel = read_and_convert(tflist)

print("test dataMat shape: {0}, test dataLabel len: {1} ".format(tdataMat.shape, len(tdataLabel)))

#print("test dataLabel: {}".format(len(tdataLabel)))

pre_st = time.clock()

preResult = clf.predict(tdataMat)

pre_et = time.clock()

print("Recognition " + tcn + " spent {:.4f}s.".format((pre_et-pre_st)))

#print("predict result: {}".format(len(preResult)))

errCount = len([x for x in preResult if x!=tcn])

print("errorCount: {}.".format(errCount))

allErrCount += errCount

score_st = time.clock()

score = clf.score(tdataMat, tdataLabel)

score_et = time.clock()

print("computing score spent {:.6f}s.".format(score_et-score_st))

allScore += score

print("score: {:.6f}.".format(score))

print("error rate is {:.6f}.".format((1-score)))

print("---------------------------------------------------------")

tet = time.clock()

print("Testing All class total spent {:.6f}s.".format(tet-tst))

print("All error Count is: {}.".format(allErrCount))

avgAccuracy = allScore/10.0

print("Average accuracy is: {:.6f}.".format(avgAccuracy))

print("Average error rate is: {:.6f}.".format(1-avgScore))