Spaces:
Running
on
Zero
Running
on
Zero
| # MIT License | |
| # Copyright (c) 2022 Intelligent Systems Lab Org | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # File author: Shariq Farooq Bhat | |
| import gradio as gr | |
| from zoedepth.utils.misc import colorize | |
| from PIL import Image | |
| import tempfile | |
| def predict_depth(model, image): | |
| depth = model.infer_pil(image) | |
| return depth | |
| def create_demo(model): | |
| gr.Markdown("### Depth Prediction demo") | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input Image", type='pil', elem_id='img-display-input').style(height="auto") | |
| depth_image = gr.Image(label="Depth Map", elem_id='img-display-output') | |
| raw_file = gr.File(label="16-bit raw depth, multiplier:256") | |
| submit = gr.Button("Submit") | |
| def on_submit(image): | |
| depth = predict_depth(model, image) | |
| colored_depth = colorize(depth, cmap='gray_r') | |
| tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False) | |
| raw_depth = Image.fromarray((depth*256).astype('uint16')) | |
| raw_depth.save(tmp.name) | |
| return [colored_depth, tmp.name] | |
| submit.click(on_submit, inputs=[input_image], outputs=[depth_image, raw_file]) | |
| # examples = gr.Examples(examples=["examples/person_1.jpeg", "examples/person_2.jpeg", "examples/person-leaves.png", "examples/living-room.jpeg"], | |
| # inputs=[input_image]) |