def test_one(image):
FOLDER_NAME = "-KNN Approach-"
test = Extractor.ImageToMatrix(image)
Hop.go_to_approach("/" + FOLDER_NAME)
best_score = 100
optimal_number = -1
grayscale = Extractor.ImageToMatrix(image)
r = np.zeros((grayscale.shape[0], grayscale.shape[1]), dtype=int)
lr = KNN_Trainer.read_image(grayscale)
lean = KNN_Trainer.record_left_right(lr)
segments = KNN_Trainer.record_segment(lr)
outside = KNN_Trainer.inside_outside(lr, grayscale)
neighbors = open("save.txt")
for line in neighbors:
match = "line ([0-9]*): lean\(([0-9].[0-9]*)\) segment\(([0-9].[0-9]*)\) outside\(([0-9].[0-9]*)\) class\(([0-9])\)"
string = re.match(match, line)
train_line, train_lean, train_segments, train_outside, train_number = string.group(
1), string.group(2), string.group(3), string.group(
4), string.group(5)
score = abs(lean - float(train_lean)) + abs(segments -
float(train_segments))
if score < best_score:
best_score = score
optimal_number = train_number
return optimal_number
def test(answer_array, index, filename):
NN = 3
count = 0
STOP = 100
test_image = Extractor.getImage(filename)
test_image = Manipulate_Image.crop_image(test_image)
test = Extractor.ImageToMatrix(test_image)
os.chdir('..')
os.chdir(os.getcwd() + "/" + "train_images_sorted" + "/")
scores = {
0: [],
1: [],
2: [],
3: [],
4: [],
5: [],
6: [],
7: [],
8: [],
9: []
}
for x in range(10):
os.chdir(os.getcwd() + "/" + str(x) + "/")
for filename in os.listdir(os.getcwd()):
if count == STOP: break
trained_image = Extractor.getImage(filename)
trained_image = Manipulate_Image.crop_image(trained_image)
trained_image = ImageOps.fit(trained_image,
(len(test[0]), len(test)),
Image.ANTIALIAS)
trained = Extractor.ImageToMatrix(trained_image)
scores = add_score(scores, x, matching_score(test, trained), NN)
#print("done a file")
count += 1
count = 0
os.chdir('..')
# print(scores)
os.chdir('..')
os.chdir(os.getcwd() + "/test_images/")
guess = predict(scores)
#print("Actual: "+str(answer_array[index])+" Best: "+str(guess))
if answer_array[index] == guess:
return 1
return 0
def darken(img):
matrix = Extractor.ImageToMatrix(img)
#print(matrix)
#print("===========================")
matrix.setflags(write=1)
darkest = 255
for row in range(len(matrix)):
for col in range(len(matrix[row])):
if matrix[row][col] < darkest:
darkest = matrix[row][col]
# enhace the image
constant = 255//darkest
for row in range(len(matrix)):
for col in range(len(matrix[row])):
if matrix[row][col]!=255:
# Linear Fit
matrix[row][col] = 255 - matrix[row][col] * constant
# root Fit
#a,b = root_fit(darkest)[0], root_fit(darkest)[1]
#matrix[row][col] = int(math.sqrt((matrix[row][col] + b)/ a))
#print(matrix)
new_img = Image.fromarray(matrix.astype(np.uint8))
new_img.save("enhanced.tif")
return new_img
def train_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 crop_image(image):
matrix = Extractor.ImageToMatrix(image)
# print(matrix)
top,bottom,left,right = get_image_bounds(matrix)
# print(get_image_bounds(matrix))
img = image.crop((left, top, right + 1, bottom + 1))
return img
def test_one(img):
FOLDER_NAME = "-Averaged Approach-"
test = Extractor.ImageToMatrix(img)
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('..')
return guess
def load_image(network, s):
"""
Generates a random number between 1 and 60000 and set's the network's input
"""
img_num = random.randint(1, 60000)
num_zeros = 5 - len(str(img_num))
zeros = '0' * num_zeros
filename = zeros + str(img_num) + ".tif"
#img_num = 11534
#filename = str(img_num)+".tif"
#print(filename)
image = Extractor.getImage(filename)
matrix = Extractor.ImageToMatrix(image)
data = np.asarray(matrix).flatten()
"""
count = 0
for item in data:
if count>=28:
count = 0
print()
print(str(item)+(3-len(str(item)))*" "+" ",end="")
count+=1
print()
print()
"""
data = scale_data(data)
#print()
"""
count = 0
for item in data:
if count>=28:
count = 0
print()
print(str(round(item,1))+(3-len(str(item)))*" "+" ",end="")
count+=1
print()
print()
"""
#exit()
network.set_inputs(data)
return (network, img_num)
pickle.dump((network, batches), open("network.p", "wb"))
if test:
correct = 0
total = 0
percent = 0
os.chdir('..')
answer_array = []
answers = open("mnist-test-labels.txt", "r")
index = 0
for line in answers:
answer_array.append(int(line.strip()))
os.chdir(os.getcwd() + '/test_images')
for filename in os.listdir(os.getcwd()):
image = Extractor.getImage(filename)
matrix = Extractor.ImageToMatrix(image)
data = np.asarray(matrix).flatten()
data = scale_data(data)
#print(answer_array[total])
if run_test(network, data) == answer_array[total]:
correct += 1
total += 1
if total % ((10000) / 100) == 0:
percent += 1
print(str(percent) + "%")
#network.show(2,2)
if total == stop_at:
break
print(str(correct) + "/" + str(total))
