update gradio theme

app.py

@@ -114,8 +114,7 @@ def run_video(image_path: str,
 
 def launch(port):
     with gr.Blocks(
-        title="TGS - Demo",
-        theme=gr.themes.Monochrome()
+        title="TGS - Demo"
     ) as demo:
         with gr.Row(variant='panel'):
             gr.Markdown(HEADER)
@@ -134,7 +133,13 @@ def launch(port):
                         "example_images/a_pikachu_with_smily_face.webp",
                         "example_images/an_otter_wearing_sunglasses.webp",
                         "example_images/lumberjack_axe.webp",
-                        "example_images/medieval_shield.webp"
+                        "example_images/medieval_shield.webp",
+                        "example_images/a_cat_dressed_as_the_pope.webp",
+                        "example_images/a_cute_little_frog_comicbook_style.webp",
+                        "example_images/a_purple_winter_jacket.webp",
+                        "example_images/MP5,_high_quality,_ultra_realistic.webp",
+                        "example_images/retro_pc_photorealistic_high_detailed.webp",
+                        "example_images/stratocaster_guitar_pixar_style.webp"
                     ],
                     inputs=[input_image],
                     cache_examples=False,
@@ -145,7 +150,7 @@ def launch(port):
             with gr.Column(scale=1):
                 with gr.Row(variant='panel'):
                     seg_image = gr.Image(value=None, type="filepath", height=256, width=256, image_mode="RGBA", label="Segmented Image", interactive=False)
-                    output_video = gr.Video(value=None, label="Video", height=256, autoplay=True)
+                    output_video = gr.Video(value=None, label="Rendered Video", height=256, autoplay=True)
                 output_3dgs = Model3DGS(value=None, label="3DGS")
         
         img_run_btn.click(

utils.py

@@ -12,7 +12,6 @@ from tqdm import tqdm
 
 
 def sam_init(sam_checkpoint, device_id=0):
-    # sam_checkpoint = os.path.join(os.path.dirname(__file__), "./sam_vit_h_4b8939.pth")
     model_type = "vit_h"
 
     device = "cuda:{}".format(device_id) if torch.cuda.is_available() else "cpu"
@@ -26,7 +25,6 @@ def sam_out_nosave(predictor, input_image, *bbox_sliders):
     bbox = np.array(bbox_sliders)
     image = np.asarray(input_image)
 
-    start_time = time.time()
     predictor.set_image(image)
 
     masks_bbox, scores_bbox, logits_bbox = predictor.predict(
@@ -73,11 +71,6 @@ def image_preprocess(input_image, save_path, lower_contrast=True, rescale=True):
     rgba = Image.fromarray(padded_image).resize((256, 256), Image.LANCZOS)
     rgba.save(save_path)
 
-    # rgba_arr = np.array(rgba) / 255.0
-    # rgb = rgba_arr[...,:3] * rgba_arr[...,-1:] + (1 - rgba_arr[...,-1:])
-    # return Image.fromarray((rgb * 255).astype(np.uint8))
-
-
 def pred_bbox(image):
     image_nobg = remove(image.convert("RGBA"), alpha_matting=True)
     alpha = np.asarray(image_nobg)[:, :, -1]
@@ -117,19 +110,3 @@ def todevice(vars, device="cuda"):
         return vars
     else:
         raise NotImplementedError("invalid input type {} for tensor2numpy".format(type(vars)))
-
-def download_checkpoint(url, save_path):
-    try:
-        with urllib.request.urlopen(url) as response, open(save_path, 'wb') as file:
-            file_size = int(response.info().get('Content-Length', -1))
-            chunk_size = 8192
-            num_chunks = file_size // chunk_size if file_size > chunk_size else 1
-
-            with tqdm(total=file_size, unit='B', unit_scale=True, desc='Downloading', ncols=100) as pbar:
-                for chunk in iter(lambda: response.read(chunk_size), b''):
-                    file.write(chunk)
-                    pbar.update(len(chunk))
-        
-        print(f"Checkpoint downloaded and saved to: {save_path}")
-    except Exception as e:
-        print(f"Error downloading checkpoint: {e}")