update

app.py

@@ -66,429 +66,31 @@ def end_session(req: gr.Request) -> None:
 with gr.Blocks(
     delete_cache=(43200, 43200)
 ) as demo:
-    gr.Markdown(
-        f"""
-        ## Image to 3D Asset Pipeline \n
-        version: {VERSION} \n
-        The service is temporarily deployed on `dev015-10.34.8.82: CUDA 4`.
-    """
-    )
-    with gr.Row():
-        with gr.Column(scale=2):
-            with gr.Tabs() as input_tabs:
-                with gr.Tab(
-                    label="Image(auto seg)", id=0
-                ) as single_image_input_tab:
-                    image_prompt = gr.Image(
-                        label="Input Image",
-                        format="png",
-                        image_mode="RGBA",
-                        type="pil",
-                        height=300,
-                    )
-                    gr.Markdown(
-                        """
-                        If you are not satisfied with the auto segmentation
-                        result, please switch to the `Image(SAM seg)` tab."""
-                    )
-                with gr.Tab(
-                    label="Image(SAM seg)", id=1
-                ) as samimage_input_tab:
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            image_prompt_sam = gr.Image(
-                                label="Input Image", type="numpy", height=400
-                            )
-                            image_seg_sam = gr.Image(
-                                label="SAM Seg Image",
-                                image_mode="RGBA",
-                                type="pil",
-                                height=400,
-                                visible=False,
-                            )
-                        with gr.Column(scale=1):
-                            image_mask_sam = gr.AnnotatedImage()
-
-                    fg_bg_radio = gr.Radio(
-                        ["foreground_point", "background_point"],
-                        label="Select foreground(green) or background(red) points, by default foreground",  # noqa
-                        value="foreground_point",
-                    )
-                    gr.Markdown(
-                        """ Click the `Input Image` to select SAM points,
-                        after get the satisified segmentation, click `Generate`
-                         button to generate the 3D asset. \n
-                        Note: If the segmented foreground is too small relative
-                         to the entire image area, the generation will fail.
-                    """
-                    )
-
-            with gr.Accordion(label="Generation Settings", open=False):
-                with gr.Row():
-                    seed = gr.Slider(
-                        0, MAX_SEED, label="Seed", value=0, step=1
-                    )
-                with gr.Row():
-                    randomize_seed = gr.Checkbox(
-                        label="Randomize Seed", value=False
-                    )
-                    project_delight = gr.Checkbox(
-                        label="Backproject delighting",
-                        value=True,
-                    )
-                gr.Markdown("Geo Structure Generation")
-                with gr.Row():
-                    ss_guidance_strength = gr.Slider(
-                        0.0,
-                        10.0,
-                        label="Guidance Strength",
-                        value=7.5,
-                        step=0.1,
-                    )
-                    ss_sampling_steps = gr.Slider(
-                        1, 50, label="Sampling Steps", value=12, step=1
-                    )
-                gr.Markdown("Visual Appearance Generation")
-                with gr.Row():
-                    slat_guidance_strength = gr.Slider(
-                        0.0,
-                        10.0,
-                        label="Guidance Strength",
-                        value=3.0,
-                        step=0.1,
-                    )
-                    slat_sampling_steps = gr.Slider(
-                        1, 50, label="Sampling Steps", value=12, step=1
-                    )
-
-            generate_btn = gr.Button(
-                "Generate(~0.5 mins)", variant="primary", interactive=False
-            )
-            model_output_obj = gr.Textbox(label="raw mesh .obj", visible=False)
-            with gr.Row():
-                extract_rep3d_btn = gr.Button(
-                    "Extract 3D Representation(~2 mins)",
-                    variant="primary",
-                    interactive=False,
-                )
-            with gr.Accordion(
-                label="Enter Asset Attributes(optional)", open=False
-            ):
-                asset_cat_text = gr.Textbox(
-                    label="Enter Asset Category (e.g., chair)"
-                )
-                height_range_text = gr.Textbox(
-                    label="Enter Height Range in meter (e.g., 0.5-0.6)"
-                )
-                mass_range_text = gr.Textbox(
-                    label="Enter Mass Range in kg (e.g., 1.1-1.2)"
-                )
-                asset_version_text = gr.Textbox(
-                    label=f"Enter version (e.g., {VERSION})"
-                )
-            with gr.Row():
-                extract_urdf_btn = gr.Button(
-                    "Extract URDF(~1 mins)",
-                    variant="primary",
-                    interactive=False,
-                )
-            with gr.Row():
-                gr.Markdown(
-                    "#### Estimated Asset 3D Attributes(No input required)"
-                )
-            with gr.Row():
-                est_type_text = gr.Textbox(
-                    label="Asset category", interactive=False
-                )
-                est_height_text = gr.Textbox(
-                    label="Real height(.m)", interactive=False
-                )
-                est_mass_text = gr.Textbox(
-                    label="Mass(.kg)", interactive=False
-                )
-                est_mu_text = gr.Textbox(
-                    label="Friction coefficient", interactive=False
-                )
-            with gr.Row():
-                download_urdf = gr.DownloadButton(
-                    label="Download URDF", variant="primary", interactive=False
-                )
-
-            gr.Markdown(
-                """ NOTE: If `Asset Attributes` are provided, the provided
-                properties will be used; otherwise, the GPT-preset properties
-                will be applied. \n
-                The `Download URDF` file is restored to the real scale and
-                has quality inspection, open with an editor to view details.
-            """
-            )
-
-            with gr.Row() as single_image_example:
-                examples = gr.Examples(
-                    label="Image Gallery",
-                    examples=[
-                        [f"assets/example_image/{image}"]
-                        for image in os.listdir(
-                            "assets/example_image"
-                        )
-                    ],
-                    inputs=[image_prompt],
-                    # fn=partial(
-                    #     preprocess_image_fn,
-                    #     model=RBG_REMOVER,
-                    #     buffer=IMAGE_BUFFER,
-                    # ),
-                    outputs=[image_prompt],
-                    # run_on_click=True,
-                    examples_per_page=32,
-                )
-
-            with gr.Row(visible=False) as single_sam_image_example:
-                examples = gr.Examples(
-                    label="Image Gallery",
-                    examples=[
-                        f"assets/example_image/{image}"
-                        for image in os.listdir(
-                            "assets/example_image"
-                        )
-                    ],
-                    inputs=[image_prompt_sam],
-                    # fn=partial(
-                    #     preprocess_sam_image_fn,
-                    #     buffer=IMAGE_BUFFER,
-                    #     model=SAM_PREDICTOR,
-                    # ),
-                    outputs=[image_prompt_sam],
-                    # run_on_click=True,
-                    examples_per_page=32,
-                )
-        with gr.Column(scale=1):
-            video_output = gr.Video(
-                label="Generated 3D Asset",
-                autoplay=True,
-                loop=True,
+    with gr.Column(scale=1):
+        video_output = gr.Video(
+            label="Generated 3D Asset",
+            autoplay=True,
+            loop=True,
+            height=300,
+        )
+        model_output_gs = gr.Model3D(
+            label="Gaussian Representation", height=300, interactive=False
+        )
+        aligned_gs = gr.Textbox(visible=False)
+        with gr.Row():
+            model_output_mesh = LitModel3D(
+                label="Mesh Representation",
                 height=300,
+                exposure=10,
+                interactive=False
             )
-            model_output_gs = gr.Model3D(
-                label="Gaussian Representation", height=300, interactive=False
-            )
-            aligned_gs = gr.Textbox(visible=False)
-            with gr.Row():
-                model_output_mesh = LitModel3D(
-                    label="Mesh Representation",
-                    height=300,
-                    exposure=10,
-                    interactive=False
-                )
-            gr.Markdown(
-                """ The rendering of `Gaussian Representation` takes additional 10s. """  # noqa
-            )
-
-    is_samimage = gr.State(False)
-    output_buf = gr.State()
-    selected_points = gr.State(value=[])
+        gr.Markdown(
+            """ The rendering of `Gaussian Representation` takes additional 10s. """  # noqa
+        )
 
     demo.load(start_session)
     demo.unload(end_session)
 
-    single_image_input_tab.select(
-        lambda: tuple(
-            [False, gr.Row.update(visible=True), gr.Row.update(visible=False)]
-        ),
-        outputs=[is_samimage, single_image_example, single_sam_image_example],
-    )
-    samimage_input_tab.select(
-        lambda: tuple(
-            [True, gr.Row.update(visible=True), gr.Row.update(visible=False)]
-        ),
-        outputs=[is_samimage, single_sam_image_example, single_image_example],
-    )
-
-    image_prompt.upload(
-        # partial(preprocess_image_fn, model=RBG_REMOVER, buffer=IMAGE_BUFFER),
-        inputs=[image_prompt],
-        outputs=[image_prompt],
-    )
-    image_prompt.change(
-        lambda: tuple(
-            [
-                gr.Button(interactive=False),
-                gr.Button(interactive=False),
-                gr.Button(interactive=False),
-                None,
-                "",
-                None,
-                None,
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-            ]
-        ),
-        outputs=[
-            extract_rep3d_btn,
-            extract_urdf_btn,
-            download_urdf,
-            model_output_gs,
-            aligned_gs,
-            model_output_mesh,
-            video_output,
-            asset_cat_text,
-            height_range_text,
-            mass_range_text,
-            asset_version_text,
-            est_type_text,
-            est_height_text,
-            est_mass_text,
-            est_mu_text,
-        ],
-    )
-    image_prompt.change(
-        active_btn_by_content,
-        inputs=image_prompt,
-        outputs=generate_btn,
-    )
-
-    image_prompt_sam.upload(
-        # partial(
-        #     preprocess_sam_image_fn, buffer=IMAGE_BUFFER, model=SAM_PREDICTOR
-        # ),
-        inputs=[image_prompt_sam],
-        outputs=[image_prompt_sam],
-    )
-    image_prompt_sam.change(
-        lambda: tuple(
-            [
-                gr.Button(interactive=False),
-                gr.Button(interactive=False),
-                gr.Button(interactive=False),
-                None,
-                None,
-                None,
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                None,
-                [],
-            ]
-        ),
-        outputs=[
-            extract_rep3d_btn,
-            extract_urdf_btn,
-            download_urdf,
-            model_output_gs,
-            model_output_mesh,
-            video_output,
-            asset_cat_text,
-            height_range_text,
-            mass_range_text,
-            asset_version_text,
-            est_type_text,
-            est_height_text,
-            est_mass_text,
-            est_mu_text,
-            image_mask_sam,
-            selected_points,
-        ],
-    )
-
-    image_prompt_sam.select(
-        select_point,
-        [
-            image_prompt_sam,
-            selected_points,
-            fg_bg_radio,
-            # gr.State(lambda: SAM_PREDICTOR),
-        ],
-        [image_mask_sam, image_seg_sam],
-    )
-    image_seg_sam.change(
-        active_btn_by_content,
-        inputs=image_seg_sam,
-        outputs=generate_btn,
-    )
-
-    generate_btn.click(
-        get_seed,
-        inputs=[randomize_seed, seed],
-        outputs=[seed],
-    ).success(
-        image_to_3d,
-        inputs=[
-            image_prompt,
-            seed,
-            ss_guidance_strength,
-            ss_sampling_steps,
-            slat_guidance_strength,
-            slat_sampling_steps,
-            # gr.State(lambda: IMAGE_BUFFER),
-            # gr.State(lambda: PIPELINE),
-            gr.State(lambda: TMP_DIR),
-            image_seg_sam,
-            is_samimage,
-        ],
-        outputs=[output_buf, video_output],
-    ).success(
-        lambda: gr.Button(interactive=True),
-        outputs=[extract_rep3d_btn],
-    )
-
-    extract_rep3d_btn.click(
-        extract_3d_representations_v2,
-        inputs=[
-            output_buf,
-            project_delight,
-            gr.State(lambda: TMP_DIR),
-            # gr.State(lambda: DELIGHT),
-            # gr.State(lambda: IMAGESR_MODEL),
-        ],
-        outputs=[
-            model_output_mesh,
-            model_output_gs,
-            model_output_obj,
-            aligned_gs,
-        ],
-    ).success(
-        lambda: gr.Button(interactive=True),
-        outputs=[extract_urdf_btn],
-    )
-
-    extract_urdf_btn.click(
-        extract_urdf,
-        inputs=[
-            aligned_gs,
-            model_output_obj,
-            asset_cat_text,
-            height_range_text,
-            mass_range_text,
-            asset_version_text,
-            gr.State(lambda: TMP_DIR),
-            # gr.State(lambda: URDF_CONVERTOR),
-            # gr.State(lambda: IMAGE_BUFFER),
-            # gr.State(lambda: CHECKERS),
-        ],
-        outputs=[
-            download_urdf,
-            est_type_text,
-            est_height_text,
-            est_mass_text,
-            est_mu_text,
-        ],
-        queue=True,
-        show_progress="full",
-    ).success(
-        lambda: gr.Button(interactive=True),
-        outputs=[download_urdf],
-    )
 
 
 if __name__ == "__main__":