rearranged files into one implementation

Author: muratovm

Extractor.py renamed to -Averaged Approach-/Extractor.py

@@ -1,46 +1,53 @@
-import tkinter as tk
-from tkinter import filedialog
-from PIL import Image
-import numpy as numpy
-
-def getImage(filename):
-    """Returns a two dimensional array of a chosen image's pixels"""
-
-    """
-    root = tk.Tk()
-    root.withdraw()
-    file_path = filedialog.askopenfilename()
-    file = open(file_path)
-    filename = file.name
-    print(file.name)
-    """
-    try:
-        image = Image.open(filename, 'r')
-    except Exception as e:
-        print(e)
-    
-    return image
-
-def ImageToArray(image):
-    width, height = image.size
-    pixel_values = list(image.getdata())
-    color_array = []
-    for h in range(height):
-        color_array.append([])
-        for w in range(width):
-            color_array[h].append(pixel_values[h*width+w])
-    return color_array
-
-
-def ImageToMatrix(image):
-    array = numpy.asarray(image)
-    return array
-
-if __name__ == "__main__":
-    image = getImage()
-    #matrix = ImageToMatrix(image)
-    #print(matrix)
-    array = ImageToArray(image)
-    for row in array:
-        print(row)
-    exit = input("Type any enter to exit")
+import tkinter as tk
+from tkinter import filedialog
+from PIL import Image
+import numpy as numpy
+
+def getImage(filename):
+    """Returns a two dimensional array of a chosen image's pixels"""
+    try:
+        image = Image.open(filename, 'r')
+    except Exception as e:
+        print(e)
+    
+    return image
+
+
+def selectImage():
+    root = tk.Tk()
+    root.withdraw()
+    file_path = filedialog.askopenfilename()
+    file = open(file_path)
+    filename = file.name
+    print(file.name)
+    
+    try:
+        image = Image.open(filename, 'r')
+    except Exception as e:
+        print(e)
+    
+    return image
+
+def ImageToArray(image):
+    width, height = image.size
+    pixel_values = list(image.getdata())
+    color_array = []
+    for h in range(height):
+        color_array.append([])
+        for w in range(width):
+            color_array[h].append(pixel_values[h*width+w])
+    return color_array
+
+
+def ImageToMatrix(image):
+    array = numpy.asarray(image)
+    return array
+
+if __name__ == "__main__":
+    image = getImage()
+    #matrix = ImageToMatrix(image)
+    #print(matrix)
+    array = ImageToArray(image)
+    for row in array:
+        print(row)
+    exit = input("Type any enter to exit")

Tester.py renamed to -Averaged Approach-/Tester.py

@@ -1,74 +1,82 @@
-import os
-import numpy as np
-import Extractor
-
-
-def test(answer_array,index,filename):    
-    test_image = Extractor.getImage(filename)
-    test = Extractor.ImageToMatrix(test_image)
-    os.chdir('..')
-    os.chdir(os.getcwd()+"/trained_digits/")
-    
-    scores = []
-    for x in range(10):
-        trained_filename = str(x) + ".tif"    
-        
-        trained_image = Extractor.getImage(trained_filename)
-        trained = Extractor.ImageToMatrix(trained_image)            
-        
-        scores.append(matching_score(test, trained))
-    
-    guess = scores.index(min(scores))
-    confidence = (1 - min(scores)/(255*28*28)) * 100
-    os.chdir('..')
-    os.chdir(os.getcwd()+"/test_images/")
-    #print(scores)
-    #print("==================================================================")
-    #print("Perdict digit: " + str(guess) + ", with confidence " + str(confidence) + "%")
-    if answer_array[index] == guess:
-        return 1
-    return 0
-
-
-    
-def matching_score(test, digit):
-    score = 0
-    for row in range(len(test)):
-        for col in range(len(test[row])):
-            if not test[row][col] == 255 and not digit[row][col] == 255:
-                invert_test = 255 - test[row][col]
-                invert_memory = 255 - digit[row][col]
-                score += abs(invert_memory - invert_test)
-    return score          
-    
-
-
-if __name__ == "__main__":
-
-
-    STOP_AT = 500
-    PERCENTILE = STOP_AT/100
-    
-    answer_array = []
-    answers = open("mnist-test-labels.txt", "r")
-    index = 0
-    for line in answers:
-        answer_array.append(int(line.strip()))
-
-    #print(answer_array)
-    index = 0
-    correct = 0
-    percent = 0
-    os.chdir(os.getcwd()+"/test_images/")
-    for filename in os.listdir(os.getcwd()):
-        correct += test(answer_array, index, filename)
-        index+=1
-        if index % PERCENTILE == 0:
-            print(str(percent) + "%")
-            percent += 1
-        if index == STOP_AT:
-            break
-
-    print(str(correct/index*100)+"% correct")
-
-    
+import os
+import numpy as np
+import Extractor
+
+
+def test(answer_array,index,filename):
+
+    FOLDER_NAME = "-Averaged Approach-"
+
+    
+    test_image = Extractor.getImage(filename)
+    test = Extractor.ImageToMatrix(test_image)
+    os.chdir('..')
+    os.chdir(os.getcwd()+"/"+FOLDER_NAME+"/")
+    os.chdir(os.getcwd()+"/trained_digits/")
+    
+    scores = []
+    for x in range(10):
+        trained_filename = str(x) + ".tif"    
+        
+        trained_image = Extractor.getImage(trained_filename)
+        trained = Extractor.ImageToMatrix(trained_image)            
+        
+        scores.append(matching_score(test, trained))
+    
+    guess = scores.index(min(scores))
+    confidence = (1 - min(scores)/(255*28*28)) * 100
+    os.chdir('..')
+    os.chdir('..')
+    os.chdir(os.getcwd()+"/test_images/")
+    #print(scores)
+    #print("==================================================================")
+    #print("Perdict digit: " + str(guess) + ", with confidence " + str(confidence) + "%")
+    if answer_array[index] == guess:
+        return 1
+    return 0
+
+
+    
+def matching_score(test, digit):
+    score = 0
+    for row in range(len(test)):
+        for col in range(len(test[row])):
+            if not test[row][col] == 255 and not digit[row][col] == 255:
+                invert_test = 255 - test[row][col]
+                invert_memory = 255 - digit[row][col]
+                score += abs(invert_memory - invert_test)
+    return score          
+    
+
+
+if __name__ == "__main__":
+
+
+    STOP_AT = 500
+    PERCENTILE = STOP_AT/100
+    
+    answer_array = []
+    os.chdir('..')
+    answers = open("mnist-test-labels.txt", "r")
+    
+    index = 0
+    for line in answers:
+        answer_array.append(int(line.strip()))
+
+    #print(answer_array)
+    index = 0
+    correct = 0
+    percent = 0
+    os.chdir(os.getcwd()+"/test_images/")
+    for filename in os.listdir(os.getcwd()):
+        correct += test(answer_array, index, filename)
+        index+=1
+        if index % PERCENTILE == 0:
+            print(str(percent) + "%")
+            percent += 1
+        if index == STOP_AT:
+            break
+
+    print(str(correct/index*100)+"% correct")
+
+    

Trainer.py renamed to -Averaged Approach-/Trainer.py

@@ -1,57 +1,66 @@
-import Extractor
-import os
-import numpy as np
-from PIL import Image
-
-
-def loop_images():
-    root = os.getcwd()
-    for x in range(10):
-        os.chdir(root +"/train_images_sorted/" + str(x))
-        i = 0
-        total = []
-        for filename in os.listdir(os.getcwd()):
-            i += 1
-            image = Extractor.getImage(filename)
-            matrix = Extractor.ImageToMatrix(image)
-            data = black_percentage(matrix)
-            if len(total) == len(data):
-                total = add_grayscale(data, total)
-            else:
-                total = data
-        array = average_percentage(total ,i)
-        print("Digit " + str(x) + " is complete")
-        img = Image.fromarray(array.astype(np.uint8))
-        img.save("average.tif")
-        # img.show()
-
-def black_percentage(grayscale):
-    """
-    NumPy 2D array -> same dimension NumPy 2D array
-    
-    returns black % for each pixel, 1 is completely black, 0 is white
-    """
-    
-    r = np.zeros((grayscale.shape[0], grayscale.shape[1]), dtype=int)
-    for row in range(len(grayscale)):
-        for col in range(len(grayscale[row])):
-            r[row][col] = grayscale[row][col]
-    return r
-
-def average_percentage(sum_grayscale, num):
-    r = np.zeros((sum_grayscale.shape[0], sum_grayscale.shape[1]), dtype=int)
-    for row in range(len(sum_grayscale)):
-        for col in range(len(sum_grayscale[row])):
-            r[row][col] = (sum_grayscale[row][col])//num
-    return r    
-
-def add_grayscale(g1, g2):
-    r = np.zeros((g1.shape[0], g1.shape[1]), dtype=int)
-    for row in range(len(g1)):
-        for col in range(len(g1[row])):
-            r[row][col] = g1[row][col] + g2[row][col]
-    return r      
-    
-
-if __name__ == "__main__":
-    loop_images();
+import Extractor
+import os
+import numpy as np
+from PIL import Image
+
+
+def loop_images():
+    FOLDER_NAME = "/-Averaged Approach-"
+    
+    os.chdir('..')
+    root = os.getcwd()
+    try:
+        new_dir = root+FOLDER_NAME+"/trained_digits/"
+        os.makedirs(new_dir)
+        for x in range(10):
+            os.chdir(root +"/train_images_sorted/" + str(x))
+            i = 0
+            total = []
+            for filename in os.listdir(os.getcwd()):
+                i += 1
+                image = Extractor.getImage(filename)
+                matrix = Extractor.ImageToMatrix(image)
+                data = black_percentage(matrix)
+                if len(total) == len(data):
+                    total = add_grayscale(data, total)
+                else:
+                    total = data
+            array = average_percentage(total ,i)
+            print("Digit " + str(x) + " is complete")
+            
+            os.chdir(new_dir)
+            img = Image.fromarray(array.astype(np.uint8))
+            img.save(str(x)+".tif")
+    except Exception as e:
+        print("trained_digits directory already exists")
+
+def black_percentage(grayscale):
+    """
+    NumPy 2D array -> same dimension NumPy 2D array
+    
+    returns black % for each pixel, 1 is completely black, 0 is white
+    """
+    
+    r = np.zeros((grayscale.shape[0], grayscale.shape[1]), dtype=int)
+    for row in range(len(grayscale)):
+        for col in range(len(grayscale[row])):
+            r[row][col] = grayscale[row][col]
+    return r
+
+def average_percentage(sum_grayscale, num):
+    r = np.zeros((sum_grayscale.shape[0], sum_grayscale.shape[1]), dtype=int)
+    for row in range(len(sum_grayscale)):
+        for col in range(len(sum_grayscale[row])):
+            r[row][col] = (sum_grayscale[row][col])//num
+    return r    
+
+def add_grayscale(g1, g2):
+    r = np.zeros((g1.shape[0], g1.shape[1]), dtype=int)
+    for row in range(len(g1)):
+        for col in range(len(g1[row])):
+            r[row][col] = g1[row][col] + g2[row][col]
+    return r      
+    
+
+if __name__ == "__main__":
+    loop_images();