app.py

from spaces import GPU
import torch

def dummy_warmup():
    if torch.cuda.is_available():
        print("[INFO] CUDA is available. Running warmup.")
        # Run any GPU warm-up or dummy CUDA calls here
        x = torch.tensor([1.0]).cuda()
    else:
        print("[WARNING] CUDA not available. Skipping warmup.")


# import numpy as np
# import gradio as gr
# import rembg
# import trimesh
# from moge.model.v1 import MoGeModel
# from utils.geometry import compute_pointmap
# import os, shutil
# import cv2
# from huggingface_hub import hf_hub_download
# from PIL import Image
# import matplotlib.pyplot as plt
# from eval_wrapper.eval import EvalWrapper, eval_scene


# device = 'cuda' if torch.cuda.is_available() else 'cpu'

# @GPU(duration = 180)
# def dummy_warmup():
#     import torch
#     if torch.cuda.is_available():
#         print("Warmup: GPU is available!")
#         _ = torch.tensor([0.0]).to(device)
# dummy_warmup()


# outdir = "/tmp/rayst3r"

# # loading all necessary models

# print("Loading MoGe model")
# # Load the model from huggingface hub (or load from local).


# def depth2uint16(depth):
#     return depth * torch.iinfo(torch.uint16).max / 10.0 # threshold is in m, convert to uint16 value

# def save_tensor_as_png(tensor: torch.Tensor, path: str, dtype: torch.dtype | None = None):
#     if dtype is None:
#         dtype = tensor.dtype
#     Image.fromarray(tensor.to(dtype).cpu().numpy()).save(path)

# def colorize_points_with_turbo_all_dims(points, method='norm',cmap='turbo'):
#     """
#     Assigns colors to 3D points using the 'turbo' colormap based on a scalar computed from all 3 dimensions.

#     Args:
#         points (np.ndarray): (N, 3) array of 3D points.
#         method (str): Method for reducing 3D point to scalar. Options: 'norm', 'pca'.

#     Returns:
#         np.ndarray: (N, 3) RGB colors in [0, 1].
#     """
#     assert points.shape[1] == 3, "Input must be of shape (N, 3)"

#     if method == 'norm':
#         scalar = np.linalg.norm(points, axis=1)
#     elif method == 'pca':
#         # Project onto first principal component
#         mean = points.mean(axis=0)
#         centered = points - mean
#         u, s, vh = np.linalg.svd(centered, full_matrices=False)
#         scalar = centered @ vh[0]  # Project onto first principal axis
#     else:
#         raise ValueError(f"Unknown method '{method}'")

#     # Normalize scalar to [0, 1]
#     scalar_min, scalar_max = scalar.min(), scalar.max()
#     normalized = (scalar - scalar_min) / (scalar_max - scalar_min + 1e-8)

#     # Apply turbo colormap
#     cmap = plt.colormaps.get_cmap(cmap)
#     colors = cmap(normalized)[:, :3]  # Drop alpha

#     return colors

# def prep_for_rayst3r(img,depth_dict,mask):
#     H, W = img.shape[:2]
#     intrinsics = depth_dict["intrinsics"].detach().cpu()
#     intrinsics[0] *= W
#     intrinsics[1] *= H

#     input_dir = os.path.join(outdir, "input")
#     if os.path.exists(input_dir):
#         shutil.rmtree(input_dir)
#     os.makedirs(input_dir, exist_ok=True)
#     # save intrinsics
#     torch.save(intrinsics, os.path.join(input_dir, "intrinsics.pt"))

#     # save depth
#     depth = depth_dict["depth"].cpu()
#     depth = depth2uint16(depth)
#     save_tensor_as_png(depth, os.path.join(input_dir, "depth.png"),dtype=torch.uint16)

#     # save mask as bool 
#     save_tensor_as_png(torch.from_numpy(mask).bool(), os.path.join(input_dir, "mask.png"),dtype=torch.bool)
#     # save image
#     save_tensor_as_png(torch.from_numpy(img), os.path.join(input_dir, "rgb.png"))

# @GPU(duration = 180)
# def rayst3r_to_glb(img,depth_dict,mask,max_total_points=10e6,rotated=False):
#     prep_for_rayst3r(img,depth_dict,mask)
    
#     dino_model = torch.hub.load('facebookresearch/dinov2', "dinov2_vitl14_reg")
#     dino_model.eval()
#     dino_model.to(device)
    
#     print("Loading RaySt3R model")
#     rayst3r_checkpoint = hf_hub_download("bartduis/rayst3r", "rayst3r.pth")
#     rayst3r_model = EvalWrapper(rayst3r_checkpoint,device='cpu')
#     rayst3r_model = rayst3r_model.to(device)
    
#     rayst3r_points = eval_scene(rayst3r_model,os.path.join(outdir, "input"),do_filter_all_masks=True,dino_model=dino_model, device = device).cpu()

#     # subsample points
#     n_points = min(max_total_points,rayst3r_points.shape[0])
#     rayst3r_points = rayst3r_points[torch.randperm(rayst3r_points.shape[0])[:n_points]].numpy()
    
#     rayst3r_points[:,1] = -rayst3r_points[:,1]
#     rayst3r_points[:,2] = -rayst3r_points[:,2]
    
#     # make all points red
#     colors = colorize_points_with_turbo_all_dims(rayst3r_points)

#     # load the input glb
#     scene = trimesh.Scene()
#     pct = trimesh.PointCloud(rayst3r_points, colors=colors, radius=0.01)
#     scene.add_geometry(pct)
    
#     outfile = os.path.join(outdir, "rayst3r.glb")
#     scene.export(outfile)
#     return outfile


# def input_to_glb(outdir,img,depth_dict,mask,rotated=False):
#     H, W = img.shape[:2]
#     intrinsics = depth_dict["intrinsics"].cpu().numpy()
#     intrinsics[0] *= W
#     intrinsics[1] *= H
    
#     depth = depth_dict["depth"].cpu().numpy()
#     cam2world = np.eye(4)
#     points_world = compute_pointmap(depth, cam2world, intrinsics)

#     scene = trimesh.Scene()
#     pts = np.concatenate([p[m] for p,m in zip(points_world,mask)])
#     col = np.concatenate([c[m] for c,m in zip(img,mask)])

#     pts = pts.reshape(-1,3)
#     pts[:,1] = -pts[:,1]
#     pts[:,2] = -pts[:,2]


#     pct = trimesh.PointCloud(pts, colors=col.reshape(-1,3))
#     scene.add_geometry(pct)
    
#     outfile = os.path.join(outdir, "input.glb")
#     scene.export(outfile)
#     return outfile

# @GPU(duration = 180)
# def depth_moge(input_img):
#     moge_model = MoGeModel.from_pretrained("Ruicheng/moge-vitl")
#     moge_model.to(device)
#     input_img_torch = torch.tensor(input_img / 255, dtype=torch.float32, device=device).permute(2, 0, 1)
#     output = moge_model.infer(input_img_torch).cpu()
#     return output 

# @GPU(duration = 180)
# def mask_rembg(input_img):
#     #masked_img = rembg.remove(input_img,)
#     output_img = rembg.remove(input_img, alpha_matting=False, post_process_mask=True)

#     # Convert to NumPy array
#     output_np = np.array(output_img)
#     alpha = output_np[..., 3]

#     # Step 2: Erode the alpha mask to shrink object slightly
#     kernel = np.ones((3, 3), np.uint8)  # Adjust size for aggressiveness
#     eroded_alpha = cv2.erode(alpha, kernel, iterations=1)
#     # Step 3: Replace alpha channel
#     output_np[..., 3] = eroded_alpha
  
#     mask = output_np[:,:,-1] >= 128
#     rgb = output_np[:,:,:3]
#     return mask, rgb 

def process_image(input_img):
    return input_img, input_img
# @GPU(duration = 180)
# def process_image(input_img):
#     # resize the input image
#     rotated = False
#     #if input_img.shape[0] > input_img.shape[1]:
#         #input_img = cv2.rotate(input_img, cv2.ROTATE_90_COUNTERCLOCKWISE)
#         #rotated = True
#     input_img = cv2.resize(input_img, (640, 480))
#     # mask, rgb = mask_rembg(input_img)
#     # depth_dict = depth_moge(input_img)

#     # if os.path.exists(outdir):
#     #     shutil.rmtree(outdir)
#     # os.makedirs(outdir)
    
#     # input_glb = input_to_glb(outdir,input_img,depth_dict,mask,rotated=rotated)
    
#     # # visualize the input points in 3D in gradio
#     # inference_glb = rayst3r_to_glb(input_img,depth_dict,mask,rotated=rotated)

#     return input_img, input_img

demo = gr.Interface(
    process_image,
    gr.Image(),
    [gr.Model3D(label="Input"), gr.Model3D(label="RaySt3R",)]
)

if __name__ == "__main__":
    dummy_warmup()
    demo.launch()