SamratSahoo

if __name__ == '__main__':
    model = Autoencoder()
    model.trainModel()
    tensor = model.testImage(7777)
    # Reshape
    tensor = torch.reshape(tensor, (28, 28))
    # toImage function
    toImage = torchvision.transforms.ToPILImage()
    # Convert to image
    image = toImage(tensor)

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    def testImage(self, indexSample):
        sample, _ = self.data[indexSample]
        sample = sample.view(sample.size(0), -1)
        sample = Variable(sample)
        return self(sample)

SamratSahoo

    def trainModel(self):
        for epoch in range(self.epochs):
            for data in self.dataLoader:
                image, _ = data
                image = image.view(image.size(0), -1)
                image = Variable(image)
                # Predictions
                output = self(image)
                # Calculate Loss
                loss = self.criterion(output, image)

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    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

SamratSahoo

class Autoencoder(nn.Module):
    def __init__(self, epochs=100, batchSize=128, learningRate=1e-3):
        super(Autoencoder, self).__init__()
        # Encoder Network
        self.encoder = nn.Sequential(nn.Linear(784, 128),
                                     nn.ReLU(True),
                                     nn.Linear(128, 64),
                                     nn.ReLU(True),
                                     nn.Linear(64, 12),

SamratSahoo

if __name__ == '__main__':
    # Grayscale Image
    image = processImage('Image.jpeg')

    # Edge Detection Kernel
    kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])

    # Convolve and Save Output
    output = convolve2D(image, kernel, padding=2)
    cv2.imwrite('2DConvolved.jpg', output)

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def convolve2D(image, kernel, padding=0, strides=1):
    # Cross Correlation
    kernel = np.flipud(np.fliplr(kernel))

    # Gather Shapes of Kernel + Image + Padding
    xKernShape = kernel.shape[0]
    yKernShape = kernel.shape[1]
    xImgShape = image.shape[0]
    yImgShape = image.shape[1]

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def processImage(image): 
  image = cv2.imread(image) 
  image = cv2.cvtColor(src=image, code=cv2.COLOR_BGR2GRAY) 
  return image