Update app.py

app.py

@@ -31,6 +31,12 @@ moge_model = MoGeModel.from_pretrained("Ruicheng/moge-vitl").to(device)
 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)
+print("Loaded rayst3r_model")
 
 
 
@@ -106,13 +112,6 @@ def prep_for_rayst3r(img,depth_dict,mask):
 @GPU(duration = 180)
 def rayst3r_to_glb(img,depth_dict,mask,max_total_points=10e6,rotated=False):
     prep_for_rayst3r(img,depth_dict,mask)
-    print('Doneneee')
-    
-    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)
-    print("Loaded rayst3r_model")
     
     rayst3r_points = eval_scene(rayst3r_model,os.path.join(outdir, "input"),do_filter_all_masks=True,dino_model=dino_model, device = device).cpu()
 
@@ -202,33 +201,10 @@ def process_image(input_img):
         shutil.rmtree(outdir)
     os.makedirs(outdir)
     input_glb = input_to_glb(outdir,input_img,depth_dict,mask,rotated=rotated)
-    print('Input done')
-    print('calling Ray')
     inference_glb = rayst3r_to_glb(input_img,depth_dict,mask,rotated=rotated)
     # print(input_glb)
     return input_glb, inference_glb
 
-# 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(),