init project

app.py

@@ -496,6 +496,12 @@ def get_reconstructed_scene(outdir, filelist, schedule='linear', niter=300, min_
     then run get_3D_model_from_scene
     """
 
+    if len(filelist) < 2:
+        raise gradio.Error("Please input at least 2 images.")
+    if len(filelist) > 8:
+        raise gradio.Error("Please input less than 8 images.")
+    
+
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
     MAST3R_CKP = 'naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric'
@@ -521,8 +527,7 @@ def get_reconstructed_scene(outdir, filelist, schedule='linear', niter=300, min_
     YOLO8_CKP='./checkpoints/ObjectAwareModel.pt'
     yolov8 = ObjectAwareModel(YOLO8_CKP)
 
-    if len(filelist) < 2:
-        raise gradio.Error("Please input at least 2 images.")
+    
 
     images = Images(filelist=filelist, device=device)
     
@@ -635,9 +640,13 @@ with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 10
     # scene state is save so that you can change conf_thr, cam_size... without rerunning the inference
     scene = gradio.State(None)
 
-    gradio.HTML('<h2 style="text-align: center;">PE3R Demo</h2>')
+    gradio.HTML('<h2 style="text-align: center;">PE3R: Perception-Efficient 3D Reconstruction</h2>')
+    gradio.HTML('<p style="text-align: center; font-size: 16px;">🪄 Take 2~3 photos with your phone, upload them, wait a few (3~5) minutes, and then start exploring your 3D world via text!<br>'
+    '✨ If you like this project, please consider giving us an encouraging star <a href="https://github.com/hujiecpp/PE3R" target="_blank">[github]</a>.</p>')
     with gradio.Column():
-        inputfiles = gradio.File(file_count="multiple")
+        snapshot = gradio.Image(None, visible=False)
+
+        inputfiles = gradio.File(file_count="multiple", label="Input Images")
 
         run_btn = gradio.Button("Reconstruct")
 
@@ -648,6 +657,16 @@ with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 10
         find_btn = gradio.Button("Find")
 
         outmodel = gradio.Model3D()
+
+        examples = gradio.Examples(
+            examples=[
+                ["./examples/1.png", ["./examples/1.png", "./examples/2.png", "./examples/3.png", "./examples/4.png"], "Table", 0.85],
+                ["./examples/5.png", ["./examples/5.png", "./examples/6.png", "./examples/7.png", "./examples/8.png"], "Christmas Tree", 0.96],
+            ],
+            inputs=[snapshot, inputfiles, text_input, threshold],
+            label="Example Inputs"
+        )
+
         # events
 
         run_btn.click(fn=recon_fun,