Merge branch 'main' of https://git.wmi.amu.edu.pl/s452639/psi

.vscode/launch.json

@@ -0,0 +1,17 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        
+
+        {
+            "type": "pwa-chrome",
+            "request": "launch",
+            "name": "Launch Chrome against localhost",
+            "url": "http://localhost:8080",
+            "webRoot": "${workspaceFolder}"
+        }
+    ]
+}

src/neural_network.py

@@ -1,96 +1,78 @@
-
-from emnist import list_datasets
 from emnist import extract_test_samples
 from emnist import extract_training_samples
 import numpy as np
-import torch
-from torch import nn
-from torch import optim
 import scipy.special
-from matplotlib.pyplot import imshow
 import glob
 import imageio
 
+""" pobranie obrazów cyfr i liter z biblioteki """
 dig_train_images, dig_train_labels = extract_training_samples('digits')
 dig_test_images, dig_test_labels = extract_test_samples('digits')
 let_train_images, let_train_labels = extract_training_samples('letters')
 let_test_images, let_test_labels = extract_test_samples('letters')
 
-
-
-
-#print(dig_train_images[0])
+""" przekształcenie tablic """
 dig_train_images = dig_train_images.reshape(len(dig_train_images),28*28)
-d_train = dig_train_images[:1000]
-d_labels = dig_train_labels[:1000]
-
 dig_test_images = dig_test_images.reshape(len(dig_test_images),28*28)
-d_test = dig_test_images[:600]
-d_labelstest = dig_test_labels[:600]
 
-print(d_test.shape)
-print(d_labelstest)
-#print(dig_train_images[0])
-#print(dig_train_images.shape)
+let_train_images = let_train_images.reshape(len(let_train_images),28*28)
+let_test_images = let_test_images.reshape(len(let_test_images),28*28)
 
 
 class NeuralNetwork:
+    """ inicjalizacja sieci neuronowej """
     def __init__(self, inputNodes, hiddenNodes, outputNodes, learningGrade, fileWeight, fileHidden):
         self.inodes = inputNodes
         self.hnodes = hiddenNodes
         self.onodes = outputNodes
 
-        """te pierwsze dwa użyj przy nauce, potem zostaw cały czas te 2"""
-        #self.weights = (np.random.rand(self.hnodes, self.inodes) - 0.5)
-        #self.hidden = (np.random.rand(self.onodes, self.hnodes) - 0.5)
-        self.weights = np.load(fileWeight)
-        self.hidden = np.load(fileHidden)
-
-        #print( 'Matrix1 \n', self.weights)
-        #print( 'Matrix2 \n', self.hidden)
+        """ używane przy uczeniu sieci """
+        self.weights = (np.random.rand(self.hnodes, self.inodes) - 0.5)
+        self.hidden = (np.random.rand(self.onodes, self.hnodes) - 0.5)
+        """ używane przy pobieraniu danych o nauczonej sieci, z pliku """
+        # self.weights = np.load(fileWeight)
+        # self.hidden = np.load(fileHidden)
 
         self.lr = learningGrade
 
+        """ funkcja aktywacji """
         self.activationFunction = lambda x: scipy.special.expit(x)
 
         pass
     
+    """trening sieci neuronowej"""
     def train(self, inputsList, targetsList):
-
+        """ konwersja list na tablice 2d """
         inputs = np.array(inputsList,ndmin=2).T
         targets = np.array(targetsList,ndmin=2).T
 
-        #forward pass
-        hiddenInputs = np.dot(self.weights, inputs) + 2
+        """ forward pass """
+        hiddenInputs = np.dot(self.weights, inputs) # input -> hidden layer
         hiddenOutputs = self.activationFunction(hiddenInputs)
 
-        finalInputs = np.dot(self.hidden, hiddenOutputs) + 1
+        finalInputs = np.dot(self.hidden, hiddenOutputs)
         finalOutputs = self.activationFunction(finalInputs)
         
+        """ backward pass """
         outputErrors = targets - finalOutputs
-        #print(outputErrors.shape)
         x =self.weights.T
-        #print(x.shape)
         hiddenErrors = np.dot(self.hidden.T, outputErrors)
 
-        #print('OutputErrors', outputErrors.shape)
-        #print('finalOutputs',finalOutputs.shape)
-        #print(x.shape)
         self.hidden += self.lr * np.dot((outputErrors * finalOutputs * (1.0 - finalOutputs)) , np.transpose(hiddenOutputs))
         self.weights += self.lr * np.dot((hiddenErrors * hiddenOutputs * (1.0 - hiddenOutputs)) , np.transpose(inputs))
 
-
         pass
 
+    """ zapisywanie wytrenowanej sieci do pliku """
     def saveTraining(self, fileWeight, fileHidden):
         np.save(fileWeight, self.weights)
         np.save(fileHidden, self.hidden)
 
+    """ wykorzystanie sieci """
     def query(self, inputsList):
-        
+        """ konwersja listy na tablicę 2d """
         inputs = np.array(inputsList, ndmin=2).T
         
-
         hiddenInputs = np.dot(self.weights, inputs)
         hiddenOutputs = self.activationFunction(hiddenInputs)
 
@@ -98,27 +80,28 @@ class NeuralNetwork:
         finalOutputs = self.activationFunction(finalInputs)
 
         return finalOutputs
-    
    
 
-""" dodaj tablicę literek"""   
-#n = NeuralNetwork(inputNodes=3, hiddenNodes=5, outputNodes=2, learningGrade=0.2)
+""" tablice sieci neuronowych """
 digitNetwork = NeuralNetwork(inputNodes=784, hiddenNodes=200, outputNodes=10, learningGrade=0.1, fileWeight="Dweights.npy", fileHidden="Dhidden.npy")
+letterNetwork = NeuralNetwork(inputNodes=784, hiddenNodes=200, outputNodes=27, learningGrade=0.1, fileWeight="Lweights.npy", fileHidden="Lhidden.npy")
 
-def trainNetwork(n, fWeight, fHidden, trainingSamples):
+
+
+# trainNetwork(digitNetwork, "Dweights_test.npy", "Dhidden_test.npy", let_train_images, let_train_labels)
+def trainNetwork(n, fWeight, fHidden, trainingSamples, trainingLabels):
     epochs = 10
-    outputNodes = 10
+    outputNodes = 27
     for e in range(epochs):
         m=0
         print('Epoch', e+1)
 
         for record in trainingSamples:
+            """ zmiana wartości przedziału z [0,255] na [0,1] """
             inputs = (np.asfarray(record[0:])/255 * 0.99) + 0.01
-            #print(inputs.shape)
 
             targets = np.zeros(outputNodes) + 0.01
-            targets[d_labels[m]] = 0.99
-            #print(targets)
+            targets[trainingLabels[m]] = 0.99
             n.train(inputs,targets)
 
             m+=1 
@@ -127,14 +110,135 @@ def trainNetwork(n, fWeight, fHidden, trainingSamples):
     n.saveTraining(fileWeight=fWeight, fileHidden=fHidden)
 
 
+def testing(n, testingSamples, testingLabels):
+    scorecard = []
+    k = 0
+    for record in testingSamples:
+        inputs = (np.asfarray(record[0:])/255 * 0.99) + 0.01
+        correctLabels  = testingLabels[k]
 
-#####################################      ODPALANIE TRAINING
-#trainNetwork(digitNetwork, "Dweights.npy", "Dhidden.npy", d_train)
+        outputs = n.query(inputs)
+        label = np.argmax(outputs)
 
-#record = d_test[0]
-#print('Label', d_labelstest[0])
-#inputs = np.asfarray(record[0:])/ 255 * 0.99 + 0.01
-#print(n.query(inputs))
+        if(label == correctLabels):
+            scorecard.append(1)
+        else:
+            scorecard.append(0)
+        k+=1
+
+    scorecardArray = np.asfarray(scorecard)
+    print('Performance', scorecardArray.sum() / scorecardArray.size)
+
+testing(digitNetwork,dig_test_images,dig_test_labels)
+testing(letterNetwork,let_test_images,let_test_labels)
+
+li = []
+ourOwnDataset = []
+record_cache = None
+def testCase(inputWord):
+    len = len(inputWord)
+    
+    word = ""
+    for i in range(0,len-2):
+        imgArray = imageio.imread(imageFileName, as_gray=True)
+        imgData = 255 - imgArray.reshape(784)
+        imgData = (imgData/255 * 0.99) + 0.01
+        #inputWord[i]
+        word = word + recognizeLet(letterNetwork ,imgData)
+
+    i=len-2
+    for i in range(i,len):
+        imgArray = imageio.imread(imageFileName, as_gray=True)
+        imgData = 255 - imgArray.reshape(784)
+        imgData = (imgData/255 * 0.99) + 0.01
+        #inputWord[i]
+        word = word + recognizeNum(digitNetwork, imgData)
 
 
-#testing
+    #assert record_cache.shape == ourOwnDataset[0].shape
+    #labelInput = np.asfarray(li)
+    #print(labelInput)
+    print('slowo: ', word)
+    pass
+
+
+
+def recognizeLet(n,imgData):
+    letters=['','a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
+    #record = np.append(label,imgData)
+    outputs = n.query(imgData)
+    label = np.argmax(outputs)
+    return letters[int(label)]
+
+def recognizeNum(n, imgData):
+    pass 
+
+    #record = np.append(label,imgData)
+    outputs = n.query(imgData)
+    #print('Record: ',record)
+    #ourOwnDataset.append(record)
+    #if record_cache is None:
+    #    record_cache = record
+    #print(ood[0])
+    #li.append(label)
+    label = np.argmax(outputs)
+    return str(label)
+    pass
+
+
+"""
+li = []
+#ourOwnDataset = np.asfarray(ood)
+ourOwnDataset = []
+
+record_cache = None
+for imageFileName in glob.glob('cyfry/?.png'):
+    label = int(imageFileName[-5:-4])
+    print('loading...', imageFileName)
+
+    imgArray = imageio.imread(imageFileName, as_gray=True)
+    #print(' imgArray: ', imgArray)
+    imgData = 255 - imgArray.reshape(784)
+    #print('imgData1: ',imgData)
+    imgData = (imgData/255 * 0.99) + 0.01
+    #print('imgData2: ',imgData)
+
+    #print(np.min(imgData))
+    #print(np.max(imgData))
+
+    record = np.append(label,imgData)
+    #print('Record: ',record)
+    ourOwnDataset.append(record)
+    if record_cache is None:
+        record_cache = record
+    #print(ood[0])
+    li.append(label)
+    pass
+
+assert record_cache.shape == ourOwnDataset[0].shape
+labelInput = np.asfarray(li)
+#print(labelInput)
+
+
+
+word = ""
+for item in range(0,9):
+    correctLabels = labelInput[item]
+    outputs = n.query(ourOwnDataset[item][1:])
+    print(outputs)
+
+    label = np.argmax(outputs)
+    #print('Network says: ', label)
+    #labelString = np.array_str(label)
+    word = word + str(label)
+
+print('slowo: ', word)
+
+"""
+
+
+
+
+#####################################      URUCHOMIENIE TRENINGU
+#trainNetwork(letterNetwork, "Lweights_test.npy", "Lhidden_test.npy", let_train_images, let_train_labels)
+# trainNetwork(digitNetwork, "Dweights_test.npy", "Dhidden_test.npy", let_train_images, let_train_labels)

src/utilities.js

@@ -5,7 +5,7 @@
  * @param {...any[]} rows 
  * @returns 
  */
-function fromTable(header, ...rows) {
+ function fromTable(header, ...rows) {
   function tupleToRecord(names, array) {
     return names.reduce((p, v, i) => ({ ...p, [v]: array[i] }), {})
   }
@@ -64,6 +64,12 @@ function randomFromSet(set) {
 	unreachable();
 }
 
+function mapToJson(map) {
+  return JSON.stringify([...map]);
+}
+function jsonToMap(jsonStr) {
+  return new Map(JSON.parse(jsonStr));
+}
 
 function nice(v) { return  `${(v * 100).toFixed(1)}%` }
 
@@ -75,7 +81,9 @@ async function requestJSONCached(url, params = {}) {
   const response = await fetch(url, params);
   const json = await response.json();
   requestJSONCached.cache.set(key, json);
+  const cache = mapToJson(requestJSONCached.cache);
+  localStorage.setItem("cache", cache);
   return json;
 }
 
-requestJSONCached.cache = new Map();
+requestJSONCached.cache = jsonToMap(localStorage.getItem("cache"));

src/view/ordersView.js

@@ -12,8 +12,25 @@ class OrdersView {
       [...document.querySelectorAll('.orders-window .canvases canvas')].map((canv, index) => {
         return new Promise((resolve, reject) => {
           canv.toBlob(blob => {
-            formData.append(`file-${index}`, blob,`file-${index}.png`);
-            resolve();
+            let blobUrl = URL.createObjectURL(blob);
+
+            const img = new Image();
+            img.src = blobUrl;
+
+            img.onload = () => {
+              const canvas = document.createElement('canvas');
+              canvas.width = 28;
+              canvas.height = 28;
+
+              const ctx = canvas.getContext('2d');
+              ctx.drawImage(img, 0, 0, 28, 28);
+
+              canvas.toBlob((blob) => {
+                // blobUrl = URL.createObjectURL(blob);
+                formData.append(`file-${index}`, blob,`file-${index}.png`);
+                resolve();
+              }, 'image/png');
+            }
           });
         });
       })