inital version

app.py

@@ -1,222 +1,12 @@
-import os
-import time
-import shutil
-from pathlib import Path
-from typing import Union
-import atexit
 import spaces
-from concurrent.futures import ThreadPoolExecutor
-import open3d as o3d
-import trimesh
-
 import gradio as gr
-from gradio_imageslider import ImageSlider
-import cv2
-import numpy as np
-import click
-import imageio
-from promptda.promptda import PromptDA
-from promptda.utils.io_wrapper import load_image, load_depth
-from promptda.utils.depth_utils import visualize_depth, unproject_depth
-# import torch
-DEVICE = 'cuda' 
-# if torch.cuda.is_available(
-# ) else 'mps' if torch.backends.mps.is_available() else 'cpu'
-# model = PromptDA.from_pretrained('depth-anything/promptda_vitl').to(DEVICE).eval()
-model = PromptDA.from_pretrained('depth-anything/promptda_vitl').eval()
-thread_pool_executor = ThreadPoolExecutor(max_workers=1)
-
-def delete_later(path: Union[str, os.PathLike], delay: int = 300):
-    print(f"Deleting file: {path}")
-    def _delete():
-        try: 
-            if os.path.isfile(path):
-                os.remove(path) 
-                print(f"Deleted file: {path}")
-            elif os.path.isdir(path):
-                shutil.rmtree(path)
-                print(f"Deleted directory: {path}")
-        except: 
-            pass
-    def _wait_and_delete():
-        time.sleep(delay)
-        _delete(path)
-    thread_pool_executor.submit(_wait_and_delete)
-    atexit.register(_delete)
-
 
 @spaces.GPU
-def run_with_gpu(image, prompt_depth):
-    image = image.to(DEVICE)
-    prompt_depth = prompt_depth.to(DEVICE)
-    model.to(DEVICE)
-    depth = model.predict(image, prompt_depth)
-    depth = depth[0, 0].detach().cpu().numpy()
-    return depth
-
-def check_is_stray_scanner_app_capture(input_dir):
-    assert os.path.exists(os.path.join(input_dir, 'rgb.mp4')), 'rgb.mp4 not found'
-    pass
-
-def run(input_file, resolution):
-    # unzip zip file
-    input_file = input_file.name
-    root_dir = os.path.dirname(input_file)
-    scene_name = input_file.split('/')[-1].split('.')[0]
-    input_dir = os.path.join(root_dir, scene_name)
-    cmd = f'unzip -o {input_file} -d {root_dir}'
-    os.system(cmd)
-    check_is_stray_scanner_app_capture(input_dir)
-
-    # extract rgb images
-    os.makedirs(os.path.join(input_dir, 'rgb'), exist_ok=True)
-    cmd = f'ffmpeg -i {input_dir}/rgb.mp4 -start_number 0 -frames:v 10 -q:v 2 {input_dir}/rgb/%06d.jpg'
-    os.system(cmd)
-
-    # Loading & Inference
-    image_path = os.path.join(input_dir, 'rgb', '000000.jpg')
-    image = load_image(image_path)
-    prompt_depth_path = os.path.join(input_dir, 'depth/000000.png')
-    prompt_depth = load_depth(prompt_depth_path)
-    depth = run_with_gpu(image, prompt_depth)
-
-
-    color = (image[0].permute(1,2,0).cpu().numpy() * 255.).astype(np.uint8)
-
-    # Visualization file
-    vis_depth, depth_min, depth_max = visualize_depth(depth, ret_minmax=True)
-    vis_prompt_depth = visualize_depth(prompt_depth[0, 0].detach().cpu().numpy(), depth_min=depth_min, depth_max=depth_max)
-    vis_prompt_depth = cv2.resize(vis_prompt_depth, (vis_depth.shape[1], vis_depth.shape[0]), interpolation=cv2.INTER_NEAREST)
-
-    # PLY File
-    ixt_path = os.path.join(input_dir, f'camera_matrix.csv')
-    ixt = np.loadtxt(ixt_path, delimiter=',')
-    orig_max = 1920
-    now_max = max(color.shape[1], color.shape[0])
-    scale = orig_max / now_max
-    ixt[:2] = ixt[:2] / scale
-    pcd = unproject_depth(depth, ixt=ixt, color=color, ret_pcd=True)
-    ply_path = os.path.join(input_dir, f'pointcloud.ply')
-    o3d.io.write_point_cloud(ply_path, pcd)
-
-    glb_path = os.path.join(input_dir, f'pointcloud.glb')
-    scene_3d = trimesh.Scene()
-    glb_colors = np.asarray(pcd.colors).astype(np.float32)
-    glb_colors = np.concatenate([glb_colors, np.ones_like(glb_colors[:, :1])], axis=1)
-    # glb_colors = (np.asarray(pcd.colors) * 255).astype(np.uint8)
-    pcd_data = trimesh.PointCloud(
-        vertices=np.asarray(pcd.points) * np.array([[1, -1, -1]]),
-        colors=glb_colors.astype(np.float64),
-    )
-    scene_3d.add_geometry(pcd_data)
-    scene_3d.export(file_obj=glb_path)
-    # o3d.io.write_point_cloud(glb_path, pcd)
-
-    # Depth Map Original Value
-    depth_path = os.path.join(input_dir, f'depth.png')
-    output_depth = (depth * 1000).astype(np.uint16)
-    imageio.imwrite(depth_path, output_depth)
-
-
-    delete_later(Path(input_dir))
-    delete_later(Path(input_file))
-
-    return color, (vis_depth, vis_prompt_depth), Path(glb_path), Path(ply_path).as_posix(), Path(depth_path).as_posix()
-
-DESCRIPTION = """
-# Estimate accurate and high-resolution depth maps from your iPhone capture.
-
-Project Page: [Prompt Depth Anything](https://promptda.github.io/)
-
-## Requirements:
-1. iPhone 12 Pro or later Pro models, iPad 2020 Pro or later Pro models
-2. Free iOS App: [Stray Scanner App](https://apps.apple.com/us/app/stray-scanner/id1557051662)
-
-## Testing Steps:
-1. Capture a scene with the Stray Scanner App.
-2. Use the iPhone [Files App](https://apps.apple.com/us/app/files/id1232058109) to compress it into a zip file and transfer it to your computer. (Long press the capture folder to compress)
-3. Upload the zip file and click "Submit" to get the depth map of the first frame.
-
-Note:
-- Currently, this demo only supports inference for the first frame. If you need to obtain all depth frames, please refer to our [GitHub repo](https://github.com/DepthAnything/PromptDA).
-- The depth map is stored as uint16, with a unit of millimeters.
-"""
-
-@click.command()
-@click.option('--share', is_flag=True, help='Whether to run the app in shared mode.')
-def main(share: bool):
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        gr.Markdown(DESCRIPTION)
-
-        with gr.Row():
-            input_file = gr.File(type="filepath", label="Stray scanner app capture zip file")
-            resolution = gr.Dropdown(choices=['756x1008', '1428x1904'], value='756x1008', label="Inference resolution")
-            submit_btn = gr.Button("Submit")
-        
-        gr.Examples(examples=[
-                ["data/assets/example0_chair.zip", "756x1008"]
-            ],
-            inputs=[input_file, resolution],
-            label="Examples",
-        ) 
-
-        with gr.Row():
-            with gr.Column():
-                output_rgb = gr.Image(type="numpy", label="RGB Image")
-            with gr.Column():
-                output_depths = ImageSlider(label="Output depth / prompt depth", position=0.5)
-        
-        with gr.Row():
-            with gr.Column():
-                output_3d_model = gr.Model3D(label="3D Viewer", display_mode='solid', clear_color=[1.0, 1.0, 1.0, 1.0])
-            with gr.Column():
-                output_ply = gr.File(type="filepath", label="Download the unprojected point cloud as .ply file", height=30)
-                output_depth_map = gr.File(type="filepath", label="Download the depth map as .png file", height=30)
-        outputs = [
-            output_rgb,
-            output_depths,
-            output_3d_model,
-            output_ply,
-            output_depth_map,
-        ]
-        # gr.Examples(examples=[
-        #         ["data/assets/example0_chair.zip", "756x1008"]
-        #     ],
-        #     fn=run,
-        #     inputs=[input_file, resolution],
-        #     outputs=outputs,
-        #     label="Examples",
-        #     cache_examples=True,
-        # ) 
-        submit_btn.click(run, 
-                         inputs=[input_file, resolution], 
-                         outputs=outputs)
-
-    demo.launch(share=share)
-# def main(share: bool):
-#     gr.Interface(
-#         fn=run,
-#         inputs=[
-#             gr.File(type="filepath", label="Upload a stray scanner app capture zip file"),
-#             gr.Dropdown(choices=['756x1008', '1428x1904'], value='756x1008', label="Inference resolution")
-#         ],
-#         outputs=[
-#             gr.Image(type="numpy", label="RGB Image"),
-#             ImageSlider(label="Depth map / prompt depth", position=0.5),
-#             gr.Model3D(label="3D Viewer", display_mode='solid', clear_color=[1.0, 1.0, 1.0, 1.0]),
-#             gr.File(type="filepath", label="Download the unprojected point cloud as .ply file"),
-#             gr.File(type="filepath", label="Download the depth map as .png file"),
-#         ],
-#         title=None,
-#         description=DESCRIPTION,
-#         clear_btn=None,
-#         allow_flagging="never",
-#         theme=gr.themes.Soft(),
-#         examples=[
-#             ["data/assets/8b98276b0a.zip"]
-#         ]
-#     ).launch(share=True)
-
-
-if __name__ == '__main__':
-    main()
+def generate(prompt):
+    return 'hello, world'
+
+gr.Interface(
+    fn=generate,
+    inputs=gr.Text(),
+    outputs=gr.Gallery(),
+).launch()