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extractNumber.py
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extractNumber.py
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import torch
import os
import cv2
import numpy as np
import torchvision.transforms as transforms
import digit_detector.cnn_model as cnn_model
model = cnn_model.Net()
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081))
# The nos. are global mean and global std for MNIST dataset
])
if os.path.exists('digit_detector/assets/model.pth'):
model.load_state_dict(torch.load('digit_detector/assets/model.pth'))
model.eval()
else:
raise Exception('Model not trained')
def identify_number(image):
prediction = 0
if not model:
return prediction
# pred_img = cv2.imread(image, 0)
# pred_img = image / 255
pred_img = cv2.resize(image, (28, 28))
pred_img_tr = transform(pred_img)
pred_img_tr = torch.reshape(pred_img_tr, shape=(1, 28, 28))
# Predict the digit value using the model
prediction_arr = model(pred_img_tr).cpu().detach().numpy()
prediction = np.argmax(prediction_arr)
return prediction
def extract_number(sudoku):
sudoku = cv2.resize(sudoku, (450,450))
# split sudoku
grid = np.zeros([9,9])
for i in range(9):
for j in range(9):
image = sudoku[i*50:(i+1)*50,j*50:(j+1)*50]
# print(image.sum())
if image.sum() > 80000:
grid[i][j] = identify_number(image)
else:
grid[i][j] = 0
return grid.astype(int)