dulucas / displacement_field Goto Github PK

Do you have other ways to download this dataset?Thank you.

In both the RGB guided version and the depth only version, you divide the UNet output by 320, could you please explain how you came up with this number, and why this step?

Many thanks!

Hello, thanks for sharing your great work.
Can you be so kind and send me the pre-trained model to test?

Regards

Hello, want now to test a different group of images.How can I do that ? Should I rewrite the code ?Thanks

I do not know the reason

Is this method supervised？ Do we need real ground truth of depth as the surveillance information?

I train the data as you describe in readme file I trained but now how to see out put <- this my question

plz help me

Hi, I write a demo code to do a test. The model is trained using the code in this repo. However, I find the visulization of the result is not as good as imagined. I was wondering if I did something wrong. Here are the demo code and testing images.

Code:

import numpy as np
import torch
import cv2
import matplotlib.pyplot as plt

from df import Displacement_Field


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = Displacement_Field()
pth_path = 'xxx/Displacement_Field/log/nyu/df_nyu_rgb_guidance/snapshot/epoch-19.pth'
model.load_state_dict(torch.load(pth_path)['model'])
model.to(device)
model.eval()

image = cv2.imread('./image.png', cv2.IMREAD_GRAYSCALE)
depth = cv2.imread('./depth.png', cv2.IMREAD_GRAYSCALE)

image = torch.from_numpy(image.astype(np.float32) / 255.0).unsqueeze(0).unsqueeze(0).to(device)
depth = torch.from_numpy(depth.astype(np.float32) / 255.0).unsqueeze(0).unsqueeze(0).to(device)
depth = (depth - depth.min()) / (depth.max() - depth.min())

pred = model(image, depth)

pred = pred.detach().cpu().numpy()[0, 0]
plt.imsave('./result.png', pred, cmap='gray')

Image:

Depth:

Result:

Hi. Can you provide already trained model weights?

Thanks for your work.
I faced some problem when executing "download.sh". Terminal reports:
"Connecting to horatio.cs.nyu.edu (horatio.cs.nyu.edu)|216.165.22.17|:80... failed: Connection refused".

Hi, in your paper, is the refinement model trained on different depth estimation methods separately, or done in the same way with this repo?

why is batch size set to 1? I tried a bigger value than 1, and the output is very strange.

Thanks for your work.
I have trained the model, is there any testing code? for example, How can I test my trained model on the NYUv2OC++ dataset ?

Hi! I have a question about how to visualize the displacement field! Thanks!