Python'da Görüntü İşleme Örnekleri

import csv
import random
import math

def loadCsv(filename):
   lines=csv.reader(open(filename,"r"))
   dataset=list(lines)
   for i in range(len(dataset)):
       dataset[i]=[float(x) for x in dataset[i]]
   return dataset

def splitDataset(dataset, splitRatio):
   trainSize=int(len(dataset)*splitRatio)
   trainSet=[]
   copy=list(dataset)
   while len(trainSet)<trainSize:
       index=random.randrange(len(copy))
       trainSet.append(copy.pop(index))
   return[trainSet, copy]

def separateByClass(dataset):
   separated={}
   for i in range(len(dataset)):
       vector=dataset[i]
       if(vector[-1] not in separated):
           separated[vector[-1]]=[]
       separated[vector[-1]].append(vector)
   return separated

def mean(numbers):
   return sum(numbers)/float(len(numbers))

def stdev(numbers):
   avg=mean(numbers)
   variance=sum([pow(x-avg,2)for x in numbers])/float(len(numbers)-1)
   return math.sqrt(variance)

def summarize(dataset):
   summaries=[(mean(attribute), stdev(attribute))for attribute in zip(*dataset)]
   del summaries[-1]
   return summaries

def summarizeByClass(dataset):
   separated=separateByClass(dataset)
   summaries={}
   for classValue, instances in separated.items():
       summaries[classValue]=summarize(instances)
   return summaries

def calculateProbability(x, mean, stdev):
   exponent=math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
   return(1/(math.sqrt(2*math.pi)*stdev))*exponent

def calculateClassProbabilities(summaries, inputVector):
   probabilities={}
   for classValue, classSummaries in summaries.items():
       probabilities[classValue]=1
       for i in range(len(classSummaries)):
           mean, stdev=classSummaries[i]
           x=inputVector[i]
           probabilities[classValue]*=calculateProbability(x, mean, stdev)
   return probabilities

def predict(summaries, inputVector):
   probabilities=calculateClassProbabilities(summaries, inputVector)
   bestLabel, bestProb=None,-1
   for classValue, probability in probabilities.items():
       if bestLabel is None or probability>bestProb:
           bestProb=probability
           bestLabel=classValue
   return bestLabel

def getPredictions(summaries, testSet):
   predictions=[]
   for i in range(len(testSet)):
       result=predict(summaries, testSet[i])
       predictions.append(result)
   return predictions

def getAccuracy(testSet, predictions):
   correct=0
   for i in range(len(testSet)):
       if testSet[i][-1]==predictions[i]:
           correct+=1
   return(correct/float(len(testSet)))*100.0

def main():
   filename='pima-indians-diabetes.data.csv'
   splitRatio=0.67
   dataset=loadCsv(filename)
   trainingSet, testSet=splitDataset(dataset, splitRatio)
   #print('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet))
   # prepare model
   summaries=summarizeByClass(trainingSet)
   # test model
   predictions=getPredictions(summaries, testSet)
   accuracy=getAccuracy(testSet, predictions)
   print('Accuracy: '+str(accuracy))
   
main()
________________________________

Accuracy: 74.40944881889764




pima-indians-diabetes.data uzantılı datayı mutlaka indirin