Now let's cover how to visually show the results, try some more examples, and then talk about some ideas for moving forward.
Here is the modified script to include matplotlib, and I will also copy and paste everything, so this is the full script for this series:
from PIL import Image import numpy as np import matplotlib.pyplot as plt import time from collections import Counter from matplotlib import style style.use("ggplot") def createExamples(): numberArrayExamples = open('numArEx.txt','a') numbersWeHave = range(1,10) for eachNum in numbersWeHave: for furtherNum in numbersWeHave: imgFilePath = 'images/numbers/'+str(eachNum)+'.'+str(furtherNum)+'.png' ei = Image.open(imgFilePath) eiar = np.array(ei) eiarl = str(eiar.tolist()) lineToWrite = str(eachNum)+'::'+eiarl+'\n' numberArrayExamples.write(lineToWrite) def threshold(imageArray): balanceAr =  newAr = imageArray for eachPart in imageArray: for theParts in eachPart: # for the reduce(lambda x, y: x + y, theParts[:3]) / len(theParts[:3]) # in Python 3, just use: from statistics import mean # then do avgNum = mean(theParts[:3]) avgNum = reduce(lambda x, y: x + y, theParts[:3]) / len(theParts[:3]) balanceAr.append(avgNum) balance = reduce(lambda x, y: x + y, balanceAr) / len(balanceAr) for eachRow in newAr: for eachPix in eachRow: if reduce(lambda x, y: x + y, eachPix[:3]) / len(eachPix[:3]) > balance: eachPix = 255 eachPix = 255 eachPix = 255 eachPix = 255 else: eachPix = 0 eachPix = 0 eachPix = 0 eachPix = 255 return newAr def whatNumIsThis(filePath): matchedAr =  loadExamps = open('numArEx.txt','r').read() loadExamps = loadExamps.split('\n') i = Image.open(filePath) iar = np.array(i) iarl = iar.tolist() inQuestion = str(iarl) for eachExample in loadExamps: try: splitEx = eachExample.split('::') currentNum = splitEx currentAr = splitEx eachPixEx = currentAr.split('],') eachPixInQ = inQuestion.split('],') x = 0 while x < len(eachPixEx): if eachPixEx[x] == eachPixInQ[x]: matchedAr.append(int(currentNum)) x+=1 except Exception as e: print(str(e)) x = Counter(matchedAr) print(x) graphX =  graphY =  ylimi = 0 for eachThing in x: graphX.append(eachThing) graphY.append(x[eachThing]) ylimi = x[eachThing] fig = plt.figure() ax1 = plt.subplot2grid((4,4),(0,0), rowspan=1, colspan=4) ax2 = plt.subplot2grid((4,4),(1,0), rowspan=3,colspan=4) ax1.imshow(iar) ax2.bar(graphX,graphY,align='center') plt.ylim(400) xloc = plt.MaxNLocator(12) ax2.xaxis.set_major_locator(xloc) plt.show() whatNumIsThis('images/test.png')
I encourage you to open up paint or something similar, create an 8 x 8 square, and draw your own numbers. You could use some of the training set, but this is highly improper.
Try drawing numbers and then shifting them a bit left or right, or up and down.
You should find some pretty decent success here, though there are obviously many problems moving forward from here. So far, we normalized images to be either white or black, but everything was 8x8. You wont always be this lucky, but you can indeed resize. The thickness of our characters was also very standard.
The main objective of this series was to teach you that, while image recognition is a somewhat complex topic in layers, each problem can be broken down and solved with very simple, easily understood, code. I hope you enjoyed.