def pixelwise():
    msrc = MSRCDataset()
    train = msrc.get_split('train')
    predictions = []
    for filename in train:
        probs = load_kraehenbuehl(filename)
        prediction = np.argmax(probs, axis=-1)
        predictions.append(prediction)

    msrc.eval_pixel_performance(train, predictions)
Esempio n. 2
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def pixelwise():
    msrc = MSRCDataset()
    train = msrc.get_split('train')
    predictions = []
    for filename in train:
        probs = load_kraehenbuehl(filename)
        prediction = np.argmax(probs, axis=-1)
        predictions.append(prediction)

    msrc.eval_pixel_performance(train, predictions)
def main():
    msrc = MSRCDataset()
    images = msrc.get_split()
    for image_name in images:
        image = msrc.get_image(image_name)
        fig, axes = plt.subplots(1, 4, figsize=(12, 6))
        axes[0].imshow(image)
        axes[1].set_title("ground truth")
        axes[1].imshow(image)
        gt = msrc.get_ground_truth(image_name)
        axes[1].imshow(colors[gt], alpha=.7)
        axes[2].set_title("new ground_truth")
        gt_new = msrc.get_ground_truth(image_name, ds="new")
        axes[2].imshow(image)
        axes[2].imshow(colors[gt_new], vmin=0, vmax=23, alpha=.7)
        present_y = np.unique(np.hstack([gt.ravel(), gt_new.ravel()]))
        axes[3].imshow(colors[present_y, :][:, np.newaxis, :],
                       interpolation='nearest')
        for i, c in enumerate(present_y):
            axes[3].text(1, i, classes[c])
        for ax in axes.ravel():
            ax.set_xticks(())
            ax.set_yticks(())
        fig.savefig("new_gt/%s.png" % image_name, bbox_inches="tight")
        plt.close(fig)