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Running
on
Zero
Running
on
Zero
| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import gradio as gr | |
| from gradio_client import Client | |
| import os | |
| import spaces | |
| import json | |
| dpt_beit = pipeline(task = "depth-estimation", model="Intel/dpt-beit-base-384", device=0) | |
| depth_anything = pipeline(task = "depth-estimation", model="nielsr/depth-anything-small", device=0) | |
| dpt_large = pipeline(task = "depth-estimation", model="intel/dpt-large", device=0) | |
| def depth_anything_inference(img): | |
| return depth_anything(img)["depth"] | |
| def dpt_beit_inference(img): | |
| return dpt_beit(img)["depth"] | |
| def dpt_large_inference(img): | |
| return dpt_large(img)["depth"] | |
| def infer(img): | |
| if img is None: | |
| return None, None, None | |
| else: | |
| return dpt_large_inference(img), dpt_beit_inference(img), depth_anything_inference(img) | |
| css = """ | |
| #mkd { | |
| height: 500px; | |
| overflow: auto; | |
| border: 1px solid #ccc; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML("<h1><center>Compare Depth Estimation Models<center><h1>") | |
| gr.Markdown("In this Space, you can compare different depth estimation models: [DPT-Large](https://huggingface.co/Intel/dpt-large), [DPT with BeiT backbone](https://huggingface.co/Intel/dpt-beit-large-512) and the recent [Depth Anything Model small checkpoint](https://huggingface.co/LiheYoung/depth-anything-small-hf). 🤩") | |
| gr.Markdown("You can also see how they compare in terms of speed [here](https://huggingface2.notion.site/DPT-Benchmarks-1e516b0ba193460e865c47b3a5681efb?pvs=4).") | |
| gr.Markdown("Simply upload an image or try one of the examples to see the outputs.") | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_img = gr.Image(label="Input Image", type="pil") | |
| with gr.Row(): | |
| output_1 = gr.Image(type="pil", label="DPT-Large") | |
| output_2 = gr.Image(type="pil", label="DPT with BeiT Backbone") | |
| output_3 = gr.Image(type="pil", label="Depth Anything") | |
| gr.Examples([["bee.jpg"], ["cat.png"], ["cats.png"]], | |
| inputs = input_img, | |
| outputs = [output_1, output_2, output_3], | |
| fn=infer, | |
| cache_examples=True, | |
| label='Click on any Examples below to get depth estimation results quickly 👇' | |
| ) | |
| input_img.change(infer, [input_img], [output_1, output_2, output_3]) | |
| demo.launch(debug=True) |