pil example neural v06 (snippet)

Python pil example 'neural v06'

Functions in program:

• def clearAll_action(sender):
• def clear_action(sender):
• def guess_action(sender):
• def showLearning(i,v):
• def train_action(sender):
• def pil2ui(pil_image):
• def ui2pil(ui_img):
• def snapshot(view):
• def vector2img(v):
• def getVector(v,dx=0,dy=0, theta=0):

Modules used in program:

• import threading
• import console, math
• import matplotlib.pyplot as plt
• import numpy as np
• import ui, io

python neural v06

Python pil example: neural v06

import ui, io
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image as PILImage
from PIL import ImageChops as chops
import console, math
import threading

###########################################################################
# history
# v01: 1/format output. 2/Landscape view.
# v02: 1/format output in % certainty. 2/ move templates by -a/0/+a in x and y, a =5
#      3/adjusted learning rate by x0.02 and learning epochs to 200
#       https://gist.github.com/d87a0833a64f0128a12c59547984ad2f
# v03: 1/put 2 neurons in output, to compare reliabilities 
#      2/show the bestloss (check bad learning)
#      3/random seed before weight initilizalization (to have another chance when learning is wrong)
#      4/added rotation by -th/0/+th in learning
#      5/learning is getting long: limit to 100 epoch, and stop when bestloss<0.002
#      https://gist.github.com/e373904d3ccba03803d80173f44b5eee
# v04: 1/ introducing a Layer class
#      2/ modified NN class to work with various layer numbers
#      https://gist.github.com/aea7738590793eefcd786be8657fa88b
# v05: 1/ made vector2img for cleaner image mngt
#      2/ change the learning order and the trace image creation
# v06: 1/ 6 channels: many changes to make code easier to manage, results easier to view
###########################################################################

tracesOn = True # True for debug and illustration of learning process

# Simple Neuron layer
class Layer(object):
  def __init__(self, outputSize, inputLayer=False): 
    self.outputSize = outputSize
    if inputLayer != False:
      self.hasInputLayer = True
      self.input = inputLayer
      self.inputSize = inputLayer.outputSize
      self.weights = np.random.randn(self.inputSize, self.outputSize) 
    else:
      self.hasInputLayer = False
    self.states = []

  def forward(self):
    #forward propagation through 1 network layer
    z = np.dot(self.input.states, self.weights) # dot product of input and set of weights
    self.states = self.sigmoid(z) # activation function

  def backward(self, err):
    #backward propagation through 1 network layer
    delta = err*self.sigmoidPrime( self.states ) # applying derivative of sigmoid to error
    newErr = delta.dot( self.weights.T ) # back-propagate error through the layer
    self.weights += self.input.states.T.dot(delta)*0.02 # adjusting weights
    return newErr

  def sigmoid(self, s):
    # activation function
    return 1/(1+np.exp(-s))

  def sigmoidPrime(self, s):
    #derivative of sigmoid
    return s * (1 - s)

  def weights2img(self,i,h):
    # that is for the fun!
    v = self.weights.T[i]
    w = math.ceil(len(v)/h)
    maxi = max(v)
    mini = min(v)
    r = maxi-mini
    if r == 0: r = 1
    tempPil = PILImage.new('L',[w,h])
    for k in range(len(v)):
      x1 = int(math.fmod(k,w))
      y1 = int(math.floor(k/w))
      val = v[k]
      val = int(255 - (val-mini)/r*250)
      tempPil.putpixel([x1,y1],val)
    return tempPil

  def allWeights2img(self,h):
    img = self.weights2img(0,h)
    w = img.width + 1
    n = len(self.weights[0])
    wtot = w*n-1
    temp = PILImage.new('L',[wtot,10],250)
    for j in range(n):
      img = self.weights2img(j,h)
      temp.paste(img,(j*w,0))
    zoom = 3
    temp.resize((wtot*zoom,10*zoom)).show()

# Simple Neural Network
class Neural_Network(object):
  def __init__(self):
    #create layers
    np.random.seed()
    self.layer = [] # now create layers from input to output:
    self.addLayer(100)
    self.addLayer(25)
    self.addLayer(6)

  def addLayer(self, nbr):
    n = len(self.layer)
    if n == 0:
      self.layer.append(Layer(nbr))
    else:
      self.layer.append(Layer(nbr, self.layer[n-1]))

  def forward(self, X):
    #forward propagation through our network
    n = len(self.layer)
    self.layer[0].states = X     # update input layer 
    for i in range(1,n):
      self.layer[i].forward()      # propagate through other layers
    return self.layer[n-1].states

  def backward(self, err):
    # backward propagate through the network
    n = len(self.layer)
    for i in range(1,n):
      err = self.layer[n-i].backward(err)

  def train(self, X, y):
    o = self.forward(X)
    self.backward(y - o)

  def predict(self, predict):
    o = self.forward(predict)
    #self.layer[1].weights2img(0,10).resize((30,30)).show()
    decision = ''
    if o[0]>o[1]:
      decision = 'Top'
    else:
      decision = 'Bot'
    reliability0 = 'Top: {:d}%'.format(int(100*float(o[0])))
    reliability1 = 'Bot: {:d}%'.format(int(100*float(o[1])))
    output = decision + ' (' + reliability0 + ', ' + reliability1 + ')'
    if tracesOn:
      print(o)
    return o

  def trainAll(self, iterations):
    self.lossArray = []
    for i in range(iterations):
      self.lossArray.append(np.mean(np.square(y - NN.forward(X))))
      self.train(X, y)
      finalLoss = self.lossArray[i]
      showLearning(i,finalLoss)
      if finalLoss<0.002: # stop learning when it is good enough
        break

    if tracesOn: 
      plt.plot(self.lossArray)
      plt.grid(1)
      plt.xlabel('Iterations')
      plt.ylabel('Cost')
      plt.show()
      finalLoss = 'final loss: {:5.3f}%'.format(finalLoss)
      console.hud_alert(finalLoss)
      self.layer[1].allWeights2img(10)



###########################################################################
# The PathView class is responsible for tracking
# touches and drawing the current stroke.
# It is used by SketchView.

class PathView (ui.View):
  def __init__(self, frame):
    self.frame = frame
    self.flex = ''
    self.path = None
    self.action = None

  def touch_began(self, touch):
    x, y = touch.location
    self.path = ui.Path()
    self.path.line_width = 8.0
    self.path.line_join_style = ui.LINE_JOIN_ROUND
    self.path.line_cap_style = ui.LINE_CAP_ROUND
    self.path.move_to(x, y)

  def touch_moved(self, touch):
    x, y = touch.location
    self.path.line_to(x, y)
    self.set_needs_display()

  def touch_ended(self, touch):
    # Send the current path to the SketchView:
    if callable(self.action):
      self.action(self)
    # Clear the view (the path has now been rendered
    # into the SketchView's image view):
    self.path = None
    self.set_needs_display()

  def draw(self):
    if self.path:
      self.path.stroke()

###########################################################################
# The main SketchView contains a PathView for the current
# line and an ImageView for rendering completed strokes.

# We use a square canvas, so that the same image can be used in portrait and landscape orientation.
w, h = ui.get_screen_size()
canvas_size = max(w, h)
mv = ui.View(canvas_size, canvas_size)
mv.bg_color = 'white'
sketch = []  # global to handle the sketch views

class SketchView (ui.View):
  def __init__(self, x, y, width=200, height=200):
    # the sketch region
    self.bg_color = 'lightgrey'
    iv = ui.ImageView(frame=(0, 0, width, height)) #, border_width=1, border_color='black')
    pv = PathView(iv.bounds)
    pv.action = self.path_action
    self.add_subview(iv)
    self.add_subview(pv)
    self.image_view = iv
    self.bounds = iv.bounds
    self.x = x
    self.y = y
    mv.add_subview(self)
    sketch.append(self)
    # some info
    lb = ui.Label()
    self.text='sample ' + str(len(sketch))
    lb.text=self.text
    lb.flex = ''
    lb.x = x+50
    lb.y = y+205
    lb.widht = 100
    lb.height = 20
    lb.alignment = ui.ALIGN_CENTER
    mv.add_subview(lb)
    self.label = lb

  def resetImage(self):
    self.image_view.image = None

  def resetText(self,newText=None):
    if newText != None:
      self.text = newText
    self.label.text = self.text
    self.label.bg_color = 'white'

  def showResult(self,v):
    txt = '{:d}%'.format(int(100*float(v)))
    self.label.text = txt
    if   v > 0.90: c = 'lightgreen'
    elif v > 0.75: c = 'lightblue'
    elif v > 0.50: c = 'yellow'
    elif v > 0.25: c = 'orange'
    else : c = 'red'
    self.label.bg_color = c

  def path_action(self, sender):
    path = sender.path
    old_img = self.image_view.image
    width, height = self.image_view.width, self.image_view.height
    with ui.ImageContext(width, height) as ctx:
      if old_img:
        old_img.draw()
      path.stroke()
      self.image_view.image = ctx.get_image()

###########################################################################
# Various helper functions

def getVector(v,dx=0,dy=0, theta=0):
  pil_image = ui2pil(snapshot(v.subviews[0]))
  pil_image = pil_image.resize((200,200))
  pil_image = chops.offset(pil_image, dx, dy)
  pil_image = pil_image.rotate(theta)
  w, h = int(v.image_view.width), int(v.image_view.height)
  px = 20
  p = int(w / px)
  xStep = int(w / p)
  yStep = int(h / p)
  vector = []
  for x in range(0, w, xStep):
    for y in range(0, h, yStep):
      crop_area = (x, y, xStep + x, yStep + y)
      cropped_pil = pil_image.crop(crop_area)
      crop_arr = cropped_pil.load()
      nonEmptyPixelsCount = 0
      for x1 in range(xStep):
        for y1 in range(yStep):
          isEmpty = (crop_arr[x1,y1][3] == 0)
          if not isEmpty:
            nonEmptyPixelsCount += 1
      if nonEmptyPixelsCount > 0:
         nonEmptyPixelsCount = 1
      vector.append(nonEmptyPixelsCount)
  return vector

def vector2img(v):
  w,h = 10,10
  maxi = max(v)
  mini = min(v)
  r = maxi-mini
  if r == 0: r = 1
  tempPil = PILImage.new('L',[w,h])
  k=0
  for x1 in range(w):
    for y1 in range(h):
      val = v[k]
      val = 255 - (val-mini)/r*250
      tempPil.putpixel([x1,y1],val)
      k+=1
  return tempPil

def snapshot(view):
  with ui.ImageContext(view.width, view.height) as ctx:
    view.draw_snapshot()
    return ctx.get_image()

def ui2pil(ui_img):
  return PILImage.open(io.BytesIO(ui_img.to_png()))

def pil2ui(pil_image):
  buffer = io.BytesIO()
  pil_image.save(buffer, format='PNG')
  return ui.Image.from_data(buffer.getvalue())

def train_action(sender):
  global NN, X, y
  X = []
  y = []
  y0 = [  [1,0,0,0,0,0], 
          [0,1,0,0,0,0],
          [0,0,1,0,0,0],
          [0,0,0,1,0,0],
          [0,0,0,0,1,0],
          [0,0,0,0,0,1]]
  a = 10
  th = 5
  w = (10+1)*27-1
  h = (10+1)*6-1
  if tracesOn:
    temp = PILImage.new('L',[w,h],250)
  j= 0
  for dx in(-a, 0, a):
      for dy in(-a, 0, a):
        for th in(-th, 0, th):
          for k in range(6):
            y.append(y0[k])
            v = getVector(sketch[k], dx, dy, th)
            X.append(v)
            if tracesOn:
              img = vector2img(v)
              temp.paste(img,(j*11,k*11))
          j+=1
  if tracesOn:
      zoom = 3
      temp.resize((w*zoom,h*zoom)).show()
  X = np.array(X, dtype=float)
  y = np.array(y, dtype=float)
  NN.trainAll(200)

def showLearning(i,v):
  if   v > 0.1: c = 'red'
  elif v > 0.02: c = 'orange'
  elif v > 0.005: c = 'yellow'
  elif v > 0.001: c = 'lightblue'
  else : c = 'lightgreen'
  trainInfo.bg_color = c
  txt = 'step {:d} : {:5.2f}%'.format(i, int(10000*float(1-v))/100)
  trainInfo.text = txt

def guess_action(sender):
  global NN, X, y
  if len(X) == 0:
    console.hud_alert('You need to do Steps 1 and 2 first.', 'error')
  else:
    p = getVector(newSketch)
    if tracesOn:
      img = vector2img(p)
      zoom = 3
      img.resize((10*zoom,10*zoom)).show()
    p = np.array(p, dtype=float)
    result = NN.predict(p)
    #console.hud_alert('done')
    for i in range(len(sketch)):
      sketch[i].showResult(result[i])

def clear_action(sender):
  newSketch.resetImage()
  for sv in sketch:
      sv.resetText()

def clearAll_action(sender):
  for sv in sketch:
    sv.resetImage()
    sv.resetText()
  newSketch.resetImage()
  showLearning(0,1)


##############################################

NN = Neural_Network()

clearAll_button = ui.ButtonItem()
clearAll_button.title = 'Reset !!'
clearAll_button.tint_color = 'red'
clearAll_button.action = clearAll_action
mv.right_button_items = [clearAll_button]

lb = ui.Label()
lb.text='First, prepare the data'
lb.flex = 'W'
lb.x = 290
lb.y = 0
mv.add_subview(lb)

lb = ui.Label()
lb.text='Draw 6 different images (ex: A, B, C, D, E, F)'
lb.flex = 'W'
lb.alignment = ui.ALIGN_CENTER
lb.x = -150
lb.y = 20
mv.add_subview(lb)

sv = SketchView( 30, 100)
sv = SketchView(260, 100)
sv = SketchView(490, 100)
sv = SketchView( 30, 340)
sv = SketchView(260, 340)
sv = SketchView(490, 340)

lb = ui.Label()
lb.text='Once you have images, Train the Model'
lb.flex = 'W'
lb.x = 690
lb.y = 50
lb.height = 20
mv.add_subview(lb)

train_button = ui.Button(frame = (800, 80, 80, 32))
train_button.border_width = 2
train_button.corner_radius = 4
train_button.title = '1/ Train'
train_button.action = train_action
mv.add_subview(train_button)

trainInfo = ui.Label()
lb = trainInfo
lb.text='0%'
lb.flex = ''
lb.x = 750
lb.y = 120
lb.height = 20
lb.width = 200
lb.alignment = ui.ALIGN_CENTER
mv.add_subview(trainInfo)
showLearning(0, 1.0)

lb = ui.Label()
lb.text='OK now lets see if it can Guess right'
lb.flex = 'w'
lb.x = 700
lb.y = 200
mv.add_subview(lb)

sv = SketchView(740, 280)
sketch = sketch[:-1] # this last view is not part of the example set => remove it
sv.resetText('')
mv.add_subview(sv)
newSketch = sv

guess_button = ui.Button(frame = (750, 530, 80, 32))
guess_button.border_width = 2
guess_button.corner_radius = 4
guess_button.title = '2/ Guess'
guess_button.action = guess_action
mv.add_subview(guess_button)

clear_button = ui.Button(frame = (850, 530, 80, 32))
clear_button.border_width = 2
clear_button.corner_radius = 4
clear_button.title = 'Clear'
clear_button.action = clear_action
mv.add_subview(clear_button)

mv.name = 'Image Recognition'
mv.present('full_screen', orientations='landscape')

Python links

• Learn Python: https://pythonbasics.org/
• Python Tutorial: https://pythonprogramminglanguage.com