Python utils.combine_images() Examples

The following are 7 code examples of utils.combine_images(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: 3leveldcnet.py    From Multi-level-DCNet with GNU General Public License v3.0 6 votes vote down vote up 
def test(model, data, args):
    x_test, y_test = data
    print('Testing the model...')
    y_pred, y_pred0, y_pred1, y_pred2, y_pred3, x_recon = model.predict(x_test, batch_size=100)

    print('Test Accuracy (All DigitCaps): ', 100.0*np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1))/(1.0*y_test.shape[0]))

    print('Test Accuracy (Merged DigitCaps): ', 100.0*np.sum(np.argmax(y_pred0, 1) == np.argmax(y_test, 1))/(1.0*y_test.shape[0]))

    print('Test Accuracy (Level 1 DigitCaps): ', 100.0*np.sum(np.argmax(y_pred1, 1) == np.argmax(y_test, 1))/(1.0*y_test.shape[0]))

    print('Test Accuracy (Level 2 DigitCaps): ', 100.0*np.sum(np.argmax(y_pred2, 1) == np.argmax(y_test, 1))/(1.0*y_test.shape[0]))

    print('Test Accuracy (Level 3 DigitCaps): ', 100.0*np.sum(np.argmax(y_pred3, 1) == np.argmax(y_test, 1))/(1.0*y_test.shape[0]))

    img = combine_images(np.concatenate([x_test[:50],x_recon[:50]]))
    image = img * 255
    Image.fromarray(image.astype(np.uint8)).save(args.save_dir + "/real_and_recon.png")
    print()
    print('Reconstructed images are saved to %s/real_and_recon.png' % args.save_dir)
    plt.imshow(plt.imread(args.save_dir + "/real_and_recon.png"))
    plt.show()
Example #2
Source File: capsulenet.py    From CapsNet-Fashion-MNIST with MIT License 6 votes vote down vote up 
def test(model, data):
    x_test, y_test = data
    y_pred, x_recon = model.predict(x_test, batch_size=100)
    print('-'*50)
    print('Test acc:', np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1))/y_test.shape[0])

    import matplotlib.pyplot as plt
    from utils import combine_images
    from PIL import Image

    img = combine_images(np.concatenate([x_test[:50],x_recon[:50]]))
    image = img * 255
    Image.fromarray(image.astype(np.uint8)).save("real_and_recon.png")
    print()
    print('Reconstructed images are saved to ./real_and_recon.png')
    print('-'*50)
    plt.imshow(plt.imread("real_and_recon.png", ))
    plt.show()
Example #3
Source File: capsulenet.py    From CapsNet-Keras with MIT License 6 votes vote down vote up 
def manipulate_latent(model, data, args):
    print('-'*30 + 'Begin: manipulate' + '-'*30)
    x_test, y_test = data
    index = np.argmax(y_test, 1) == args.digit
    number = np.random.randint(low=0, high=sum(index) - 1)
    x, y = x_test[index][number], y_test[index][number]
    x, y = np.expand_dims(x, 0), np.expand_dims(y, 0)
    noise = np.zeros([1, 10, 16])
    x_recons = []
    for dim in range(16):
        for r in [-0.25, -0.2, -0.15, -0.1, -0.05, 0, 0.05, 0.1, 0.15, 0.2, 0.25]:
            tmp = np.copy(noise)
            tmp[:,:,dim] = r
            x_recon = model.predict([x, y, tmp])
            x_recons.append(x_recon)

    x_recons = np.concatenate(x_recons)

    img = combine_images(x_recons, height=16)
    image = img*255
    Image.fromarray(image.astype(np.uint8)).save(args.save_dir + '/manipulate-%d.png' % args.digit)
    print('manipulated result saved to %s/manipulate-%d.png' % (args.save_dir, args.digit))
    print('-' * 30 + 'End: manipulate' + '-' * 30)
Example #4
Source File: capsulenet.py    From CapsNet-Pytorch with MIT License 6 votes vote down vote up 
def show_reconstruction(model, test_loader, n_images, args):
    import matplotlib.pyplot as plt
    from utils import combine_images
    from PIL import Image
    import numpy as np

    model.eval()
    for x, _ in test_loader:
        x = Variable(x[:min(n_images, x.size(0))].cuda(), volatile=True)
        _, x_recon = model(x)
        data = np.concatenate([x.data, x_recon.data])
        img = combine_images(np.transpose(data, [0, 2, 3, 1]))
        image = img * 255
        Image.fromarray(image.astype(np.uint8)).save(args.save_dir + "/real_and_recon.png")
        print()
        print('Reconstructed images are saved to %s/real_and_recon.png' % args.save_dir)
        print('-' * 70)
        plt.imshow(plt.imread(args.save_dir + "/real_and_recon.png", ))
        plt.show()
        break
Example #5
Source File: dcnet.py    From Multi-level-DCNet with GNU General Public License v3.0 5 votes vote down vote up 
def test(model, data, args):
    x_test, y_test = data
    print('Testing the model...')
    y_pred, x_recon = model.predict(x_test, batch_size=100)

    print('Test Accuracy: ', 100.0*np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1))/(1.0*y_test.shape[0]))

    img = combine_images(np.concatenate([x_test[:50],x_recon[:50]]))
    image = img * 255
    Image.fromarray(image.astype(np.uint8)).save(args.save_dir + "/real_and_recon.png")
    print()
    print('Reconstructed images are saved to %s/real_and_recon.png' % args.save_dir)
    plt.imshow(plt.imread(args.save_dir + "/real_and_recon.png"))
    plt.show()
Example #6
Source File: textcaps_emnist_bal.py    From textcaps with MIT License 5 votes vote down vote up 
def save_output_image(self,samples,image_name):
        """
        Visualizing and saving images in the .png format 
        :param samples: images to be visualized
        :param image_name: name of the saved .png file
        """
        if not os.path.exists(args.save_dir+"/images"):
            os.makedirs(args.save_dir+"/images")
        img = combine_images(samples)
        img = img * 255
        Image.fromarray(img.astype(np.uint8)).save(args.save_dir + "/images/"+image_name+".png")
        print(image_name, "Image saved.")
Example #7
Source File: capsulenet.py    From CapsNet-Keras with MIT License 5 votes vote down vote up 
def test(model, data, args):
    x_test, y_test = data
    y_pred, x_recon = model.predict(x_test, batch_size=100)
    print('-'*30 + 'Begin: test' + '-'*30)
    print('Test acc:', np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1))/y_test.shape[0])

    img = combine_images(np.concatenate([x_test[:50],x_recon[:50]]))
    image = img * 255
    Image.fromarray(image.astype(np.uint8)).save(args.save_dir + "/real_and_recon.png")
    print()
    print('Reconstructed images are saved to %s/real_and_recon.png' % args.save_dir)
    print('-' * 30 + 'End: test' + '-' * 30)
    plt.imshow(plt.imread(args.save_dir + "/real_and_recon.png"))
    plt.show()