import pickle
import numpy as np
from shutil import copyfile
import solver
import computer_vision.helper as cv
import neural_net.digits as nn
from neural_net.Dataset import Dataset
from Sudoku import classification_mode
from Sudoku import Sudoku
# Static variables, mostly file locations
DIGITS = '0123456789'
IMAGE_DIR = os.path.join('data', 'images')
STAGE_DIR = os.path.abspath(os.path.join(IMAGE_DIR, 'stage'))
CLASSIFIED_DIR = os.path.abspath(os.path.join(IMAGE_DIR, 'classified', classification_mode()))
CL_TRAIN_DIR = os.path.join(CLASSIFIED_DIR, 'train')
CL_TEST_DIR = os.path.join(CLASSIFIED_DIR, 'test')
DIGITS_DIR = os.path.abspath(os.path.join(IMAGE_DIR, 'raw', 'digits'))
GRID_DIR = os.path.abspath(os.path.join(IMAGE_DIR, 'grid', 'all'))
TRAIN_DIR = os.path.abspath(os.path.join(IMAGE_DIR, 'grid', 'train'))
TEST_DIR = os.path.abspath(os.path.join(IMAGE_DIR, 'grid', 'test'))
DATA_DIR = os.path.abspath(os.path.join('data', 'datasets'))
DATA_FILE = os.path.join(DATA_DIR, classification_mode())
MODEL_DIR = os.path.join('data', 'models', classification_mode())
DIGIT_MODEL = os.path.join(MODEL_DIR, 'model.ckpt')
def mkdir(dir_):
if not os.path.exists(dir_):
os.mkdir(dir_)
import os
import pickle
import unittest
import neural_net.digits as nn
from neural_net.Dataset import Dataset
from Sudoku import classification_mode
DATA_DIR = os.path.abspath(os.path.join('..', 'data', 'datasets'))
DATA_FILE = os.path.join(DATA_DIR, classification_mode())
MODEL = os.path.join('..', 'data', 'models', classification_mode(),
'model.ckpt')
def load_data(file_name):
"""Loads Python object from disk."""
with open(file_name, 'rb') as f:
data = pickle.load(f)
return data
class NNTrainer(unittest.TestCase):
def test_training(self):
try:
digits = load_data(DATA_FILE)
nn.train(digits, MODEL, test_only=True, steps=100)
self.assertTrue(
True
) # Simply testing if the training can proceed without error
except Exception as err:
self.assertRaises(err)
import os
import pickle
import unittest
import neural_net.digits as nn
from Sudoku import classification_mode
DATA_DIR = os.path.abspath(os.path.join('..', 'data', 'datasets'))
DATA_FILE = os.path.join(DATA_DIR, classification_mode())
MODEL = os.path.join('..', 'data', 'best-model', 'model.ckpt')
def load_data(file_name):
"""Loads Python object from disk."""
with open(file_name, 'rb') as f:
data = pickle.load(f)
return data
class NNTrainer(unittest.TestCase):
def test_training(self):
try:
digits = load_data(DATA_FILE)
nn.train(digits, MODEL, test_only=True, steps=100)
self.assertTrue(
True
) # Simply testing if the training can proceed without error
except Exception as err:
self.assertRaises(err)
import os
import cv2
import unittest
from neural_net.DigitRecogniser import DigitRecogniser
from Sudoku import classification_mode
IMG_DIR = os.path.join('..', 'data', 'images', 'classified',
classification_mode(), 'test')
DIGIT_MODEL = os.path.join('..', 'data', 'models', classification_mode(),
'model.ckpt')
TOLERANCE = 0.98 # Percentage of successful recognition to constitute a pass
DIGIT_RECOGNISER = DigitRecogniser(DIGIT_MODEL)
def test_all_digits(digit):
"""Tests all images of a digit in the classified directory using the neural network for recognising digits."""
digit_dir = os.path.join(IMG_DIR, str(digit))
all_images = []
for img_path in os.listdir(digit_dir):
img = cv2.imread(os.path.join(digit_dir, img_path),
cv2.IMREAD_GRAYSCALE)
if img is not None: # Skips any hidden files that are not images
# Resize image if it isn't square
height, width = img.shape[:2]
if height != 28 or width != 28:
img = cv2.resize(img, (28, 28))
all_images.append(img)
guesses = DIGIT_RECOGNISER.predict_digit(all_images)
import os
import cv2
import unittest
from neural_net.DigitRecogniser import DigitRecogniser
from Sudoku import classification_mode
IMG_DIR = os.path.join('..', 'data', 'images', 'classified', classification_mode(), 'test')
DIGIT_MODEL = os.path.join('..', 'data', 'models', classification_mode(), 'model.ckpt')
TOLERANCE = 0.98 # Percentage of successful recognition to constitute a pass
DIGIT_RECOGNISER = DigitRecogniser(DIGIT_MODEL)
def test_all_digits(digit):
"""Tests all images of a digit in the classified directory using the neural network for recognising digits."""
digit_dir = os.path.join(IMG_DIR, str(digit))
all_images = []
for img_path in os.listdir(digit_dir):
img = cv2.imread(os.path.join(digit_dir, img_path), cv2.IMREAD_GRAYSCALE)
if img is not None: # Skips any hidden files that are not images
# Resize image if it isn't square
height, width = img.shape[:2]
if height != 28 or width != 28:
img = cv2.resize(img, (28, 28))
all_images.append(img)
guesses = DIGIT_RECOGNISER.predict_digit(all_images)
wrong = len(list(filter(lambda x: int(x) != int(digit), guesses)))
errors = wrong / len(all_images)
return errors