python 图像归一化作业代码代编程代写图python作业

Posted 2020-11-02 beautyful

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python 图像归一化作业代码代编程代写图python作业
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))
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