Update app.py

app.py

@@ -13,242 +13,259 @@ from gradio_imageslider import ImageSlider
 
 import spaces
 
-def process(
-    pipe,
-    path_input,
-    ensemble_size,
-    denoise_steps,
-    processing_res,
-    path_out_16bit=None,
-    path_out_fp32=None,
-    path_out_vis=None,
-):
-
-    if path_out_vis is not None:
-        return (
-            [path_out_16bit, path_out_vis],
-            [path_out_16bit, path_out_fp32, path_out_vis],
-        )
-
-    input_image = Image.open(path_input)
-
-    pipe_out = pipe(
-        input_image,
-        denoising_steps=denoise_steps,
-        ensemble_size=ensemble_size,
-        processing_res=processing_res,
-        batch_size=1 if processing_res == 0 else 0,
-        guidance_scale=3,
-        domain="indoor",
-        show_progress_bar=True,
-    )
-
-    depth_pred = pipe_out.depth_np
-    depth_colored = pipe_out.depth_colored
-    depth_16bit = (depth_pred * 65535.0).astype(np.uint16)
-
-    path_output_dir = os.path.splitext(path_input)[0] + "_output"
-    os.makedirs(path_output_dir, exist_ok=True)
-
-    name_base = os.path.splitext(os.path.basename(path_input))[0]
-    path_out_fp32 = os.path.join(path_output_dir, f"{name_base}_depth_fp32.npy")
-    path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.png")
-    path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.png")
-
-    np.save(path_out_fp32, depth_pred)
-    Image.fromarray(depth_16bit).save(path_out_16bit, mode="I;16")
-    depth_colored.save(path_out_vis)
-
-    return (
-        [path_out_16bit, path_out_vis],
-        [path_out_16bit, path_out_fp32, path_out_vis],
-    )
-
+def identity(img):
+    return img, img
 
 @spaces.GPU
 def run_demo_server(pipe):
-    process_pipe = functools.partial(process, pipe)
-    os.environ["GRADIO_ALLOW_FLAGGING"] = "never"
-
-    with gr.Blocks(
-        analytics_enabled=False,
-        title="GeoWizard Depth and Normal Estimation",
-        css="""
-            #download {
-                height: 118px;
-            }
-            .slider .inner {
-                width: 5px;
-                background: #FFF;
-            }
-            .viewport {
-                aspect-ratio: 4/3;
-            }
-        """,
-    ) as demo:
-        gr.Markdown(
-        """
-            <h1 align="center">Geowizard Depth & Normal Estimation</h1>
-        """
-        )
-
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(
-                    label="Input Image",
-                    type="filepath",
-                )
-                with gr.Accordion("Advanced options", open=False):
-                    domain = gr.Radio(
-                        [
-                            ("Outdoor", "outdoor"),
-                            ("Indoor", "indoor"),
-                            ("Object", "object"),
-                        ],
-                        label="Data Domain",
-                        value="indoor",
-                    )
-                    cfg_scale = gr.Slider(
-                        label="Classifier Free Guidance Scale",
-                        minimum=1,
-                        maximum=5,
-                        step=1,
-                        value=3,
-                    )
-                    denoise_steps = gr.Slider(
-                        label="Number of denoising steps",
-                        minimum=1,
-                        maximum=20,
-                        step=1,
-                        value=2,
-                    )
-                    ensemble_size = gr.Slider(
-                        label="Ensemble size",
-                        minimum=1,
-                        maximum=15,
-                        step=1,
-                        value=1,
-                    )
-                    processing_res = gr.Radio(
-                        [
-                            ("Native", 0),
-                            ("Recommended", 768),
-                        ],
-                        label="Processing resolution",
-                        value=768,
-                    )
-                input_output_16bit = gr.File(
-                    label="Predicted depth (16-bit)",
-                    visible=False,
-                )
-                input_output_fp32 = gr.File(
-                    label="Predicted depth (32-bit)",
-                    visible=False,
-                )
-                input_output_vis = gr.File(
-                    label="Predicted depth (red-near, blue-far)",
-                    visible=False,
-                )
-                with gr.Row():
-                    submit_btn = gr.Button(value="Compute", variant="primary")
-                    clear_btn = gr.Button(value="Clear")
-            with gr.Column():
-                output_slider = ImageSlider(
-                    label="Predicted depth (red-near, blue-far)",
-                    type="filepath",
-                    show_download_button=True,
-                    show_share_button=True,
-                    interactive=False,
-                    elem_classes="slider",
-                    position=0.25,
-                )
-                files = gr.Files(
-                    label="Depth outputs",
-                    elem_id="download",
-                    interactive=False,
-                )
-
-        blocks_settings_depth = [ensemble_size, denoise_steps, processing_res]
-        blocks_settings = blocks_settings_depth
-        map_id_to_default = {b._id: b.value for b in blocks_settings}
-
-        inputs = [
-            input_image,
-            ensemble_size,
-            denoise_steps,
-            processing_res,
-            input_output_16bit,
-            input_output_fp32,
-            input_output_vis,
-        ]
-        outputs = [
-            submit_btn,
-            input_image,
-            output_slider,
-            files,
-        ]
-
-        def submit_depth_fn(*args):
-            out = list(process_pipe(*args))
-            out = [gr.Button(interactive=False), gr.Image(interactive=False)] + out
-            return out
-
-        submit_btn.click(
-            fn=submit_depth_fn,
-            inputs=inputs,
-            outputs=outputs,
-            concurrency_limit=1,
-        )
-
-        gr.Examples(
-            fn=submit_depth_fn,
-            examples=[
-                [
-                    "files/bee.jpg",
-                    10,  # ensemble_size
-                    10,  # denoise_steps
-                    768,  # processing_res
-                    "files/bee_depth_16bit.png",
-                    "files/bee_depth_fp32.npy",
-                    "files/bee_depth_colored.png",
-                ],
-            ],
-            inputs=inputs,
-            outputs=outputs,
-            cache_examples=True,
-        )
-
-        def clear_fn():
-            out = []
-            for b in blocks_settings:
-                out.append(map_id_to_default[b._id])
-            out += [
-                gr.Button(interactive=True),
-                gr.Image(value=None, interactive=True),
-                None, None, None, None, None, None, None,
-            ]
-            return out
-
-        clear_btn.click(
-            fn=clear_fn,
-            inputs=[],
-            outputs=blocks_settings + [
-                submit_btn,
-                input_image,
-                input_output_16bit,
-                input_output_fp32,
-                input_output_vis,
-                output_slider,
-                files,
-            ],
-        )
-
-        demo.queue(
-            api_open=False,
-        ).launch(
-            server_name="0.0.0.0",
-            server_port=7860,
-        )
+    title = "Geowizard"
+    description = "Gradio demo for Geowizard."
+
+    examples = ["files/bee.jpg"]
+    
+    gr.Interface(
+        depth_normal, 
+        inputs=[gr.Image(type='pil', label="Original Image"), 
+        outputs=[gr.Image(type="pil",label="Output Depth"), gr.Image(type="pil",label="Output Normal")], 
+        title=title, description=description, article='1', examples=examples, analytics_enabled=False).launch()
+
+    
+# def process(
+#     pipe,
+#     path_input,
+#     ensemble_size,
+#     denoise_steps,
+#     processing_res,
+#     path_out_16bit=None,
+#     path_out_fp32=None,
+#     path_out_vis=None,
+# ):
+
+#     if path_out_vis is not None:
+#         return (
+#             [path_out_16bit, path_out_vis],
+#             [path_out_16bit, path_out_fp32, path_out_vis],
+#         )
+
+#     input_image = Image.open(path_input)
+
+#     pipe_out = pipe(
+#         input_image,
+#         denoising_steps=denoise_steps,
+#         ensemble_size=ensemble_size,
+#         processing_res=processing_res,
+#         batch_size=1 if processing_res == 0 else 0,
+#         guidance_scale=3,
+#         domain="indoor",
+#         show_progress_bar=True,
+#     )
+
+#     depth_pred = pipe_out.depth_np
+#     depth_colored = pipe_out.depth_colored
+#     depth_16bit = (depth_pred * 65535.0).astype(np.uint16)
+
+#     path_output_dir = os.path.splitext(path_input)[0] + "_output"
+#     os.makedirs(path_output_dir, exist_ok=True)
+
+#     name_base = os.path.splitext(os.path.basename(path_input))[0]
+#     path_out_fp32 = os.path.join(path_output_dir, f"{name_base}_depth_fp32.npy")
+#     path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.png")
+#     path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.png")
+
+#     np.save(path_out_fp32, depth_pred)
+#     Image.fromarray(depth_16bit).save(path_out_16bit, mode="I;16")
+#     depth_colored.save(path_out_vis)
+
+#     return (
+#         [path_out_16bit, path_out_vis],
+#         [path_out_16bit, path_out_fp32, path_out_vis],
+#     )
+
+
+# @spaces.GPU
+# def run_demo_server(pipe):
+#     process_pipe = functools.partial(process, pipe)
+#     os.environ["GRADIO_ALLOW_FLAGGING"] = "never"
+
+#     with gr.Blocks(
+#         analytics_enabled=False,
+#         title="GeoWizard Depth and Normal Estimation",
+#         css="""
+#             #download {
+#                 height: 118px;
+#             }
+#             .slider .inner {
+#                 width: 5px;
+#                 background: #FFF;
+#             }
+#             .viewport {
+#                 aspect-ratio: 4/3;
+#             }
+#         """,
+#     ) as demo:
+#         gr.Markdown(
+#         """
+#             <h1 align="center">Geowizard Depth & Normal Estimation</h1>
+#         """
+#         )
+
+#         with gr.Row():
+#             with gr.Column():
+#                 input_image = gr.Image(
+#                     label="Input Image",
+#                     type="filepath",
+#                 )
+#                 with gr.Accordion("Advanced options", open=False):
+#                     domain = gr.Radio(
+#                         [
+#                             ("Outdoor", "outdoor"),
+#                             ("Indoor", "indoor"),
+#                             ("Object", "object"),
+#                         ],
+#                         label="Data Domain",
+#                         value="indoor",
+#                     )
+#                     cfg_scale = gr.Slider(
+#                         label="Classifier Free Guidance Scale",
+#                         minimum=1,
+#                         maximum=5,
+#                         step=1,
+#                         value=3,
+#                     )
+#                     denoise_steps = gr.Slider(
+#                         label="Number of denoising steps",
+#                         minimum=1,
+#                         maximum=20,
+#                         step=1,
+#                         value=2,
+#                     )
+#                     ensemble_size = gr.Slider(
+#                         label="Ensemble size",
+#                         minimum=1,
+#                         maximum=15,
+#                         step=1,
+#                         value=1,
+#                     )
+#                     processing_res = gr.Radio(
+#                         [
+#                             ("Native", 0),
+#                             ("Recommended", 768),
+#                         ],
+#                         label="Processing resolution",
+#                         value=768,
+#                     )
+#                 input_output_16bit = gr.File(
+#                     label="Predicted depth (16-bit)",
+#                     visible=False,
+#                 )
+#                 input_output_fp32 = gr.File(
+#                     label="Predicted depth (32-bit)",
+#                     visible=False,
+#                 )
+#                 input_output_vis = gr.File(
+#                     label="Predicted depth (red-near, blue-far)",
+#                     visible=False,
+#                 )
+#                 with gr.Row():
+#                     submit_btn = gr.Button(value="Compute", variant="primary")
+#                     clear_btn = gr.Button(value="Clear")
+#             with gr.Column():
+#                 output_slider = ImageSlider(
+#                     label="Predicted depth (red-near, blue-far)",
+#                     type="filepath",
+#                     show_download_button=True,
+#                     show_share_button=True,
+#                     interactive=False,
+#                     elem_classes="slider",
+#                     position=0.25,
+#                 )
+#                 files = gr.Files(
+#                     label="Depth outputs",
+#                     elem_id="download",
+#                     interactive=False,
+#                 )
+
+#         blocks_settings_depth = [ensemble_size, denoise_steps, processing_res]
+#         blocks_settings = blocks_settings_depth
+#         map_id_to_default = {b._id: b.value for b in blocks_settings}
+
+#         inputs = [
+#             input_image,
+#             ensemble_size,
+#             denoise_steps,
+#             processing_res,
+#             input_output_16bit,
+#             input_output_fp32,
+#             input_output_vis,
+#         ]
+#         outputs = [
+#             submit_btn,
+#             input_image,
+#             output_slider,
+#             files,
+#         ]
+
+#         def submit_depth_fn(*args):
+#             out = list(process_pipe(*args))
+#             out = [gr.Button(interactive=False), gr.Image(interactive=False)] + out
+#             return out
+
+#         submit_btn.click(
+#             fn=submit_depth_fn,
+#             inputs=inputs,
+#             outputs=outputs,
+#             concurrency_limit=1,
+#         )
+
+#         gr.Examples(
+#             fn=submit_depth_fn,
+#             examples=[
+#                 [
+#                     "files/bee.jpg",
+#                     10,  # ensemble_size
+#                     10,  # denoise_steps
+#                     768,  # processing_res
+#                     "files/bee_depth_16bit.png",
+#                     "files/bee_depth_fp32.npy",
+#                     "files/bee_depth_colored.png",
+#                 ],
+#             ],
+#             inputs=inputs,
+#             outputs=outputs,
+#             cache_examples=True,
+#         )
+
+#         def clear_fn():
+#             out = []
+#             for b in blocks_settings:
+#                 out.append(map_id_to_default[b._id])
+#             out += [
+#                 gr.Button(interactive=True),
+#                 gr.Image(value=None, interactive=True),
+#                 None, None, None, None, None, None, None,
+#             ]
+#             return out
+
+#         clear_btn.click(
+#             fn=clear_fn,
+#             inputs=[],
+#             outputs=blocks_settings + [
+#                 submit_btn,
+#                 input_image,
+#                 input_output_16bit,
+#                 input_output_fp32,
+#                 input_output_vis,
+#                 output_slider,
+#                 files,
+#             ],
+#         )
+
+#         demo.queue(
+#             api_open=False,
+#         ).launch(
+#             server_name="0.0.0.0",
+#             server_port=7860,
+#         )
 
 
 def main():