Question about heatmap

[haochen-wang409]

Question about heatmap

[Haochen-Wang409]

Thanks a lot for your attention!
Here is the pseudo-code for generating an RGB heatmap using the predicted reconstruction loss:

def show_loss_pred(model, inputs, img, num_patches=196, h=14, w=14, input_size=224):
    # generate a full visible mask
    mask = np.zeros((num_patches)).astype(bool)
    mask = torch.from_numpy(mask).unsqueeze(0).bool()

    with torch.no_grad():
        outs = model(inputs, mask)

    loss_show = outs['loss_pred'][0].cpu()

    # postprocessing for predicted loss
    loss_show = loss_show - loss_show.mean()
    loss_show = np.exp(loss_show) / (1 + np.exp(loss_show))
    loss_show = loss_show / loss_show.max()
    loss_show = (loss_show * 255).numpy().astype(np.uint8)

    reconstruct = loss_show.copy()

    reconstruct = reconstruct.reshape((h, w)).astype(np.uint8)
    reconstruct_color = cv2.applyColorMap(reconstruct, cv2.COLORMAP_JET)
    reconstruct_color = np.array(cv2.cvtColor(reconstruct_color, cv2.COLOR_BGR2RGB))

    rec_big = np.zeros((input_size, input_size, 3)).astype(np.uint8)
    for i in range(h):
        for j in range(w):
            rec_big[i * 16: i * 16 + 16, j * 16: j * 16 + 16, :] = reconstruct_color[i, j]

    img = np.array(img)
    img_show = (0.5 * img + 0.5 * rec_big).astype(np.uint8)

    return img_show

[Lrbomchz]

Thank you very much, it‘s work!

[Haochen-Wang409]

Happy to hear that :)
I hope our work brings inspiration to you.