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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 json | |
import spaces | |
dpt_beit = pipeline(task = "depth-estimation", model="Intel/dpt-beit-base-384") | |
depth_anything = pipeline(task = "depth-estimation", model="nielsr/depth-anything-small") | |
dpt_large = pipeline(task = "depth-estimation", model="intel/dpt-large") | |
def depth_anything_inference(image_path): | |
return depth_anything(image_path)["depth"] | |
def dpt_beit_inference(image): | |
return dpt_beit(image)["depth"] | |
def dpt_large_inference(image): | |
return dpt_large(image)["depth"] | |
def infer(image): | |
return dpt_large_inference(image), dpt_beit_inference(image), depth_anything_inference(image) | |
iface = gr.Interface(fn=infer, | |
inputs=gr.Image(type="pil"), | |
outputs=[gr.Image(type="pil", label="DPT-Large"), | |
gr.Image(type="pil", label="DPT with BeiT Backbone"), | |
gr.Image(type="pil", label="Depth Anything")], | |
title="Compare Depth Estimation Models", | |
description="In this Space you can compare various depth estimation models.", | |
examples=[["bee.JPG"]]) | |
iface.launch(debug=True) |