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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")
@spaces.GPU
def depth_anything_inference(image_path):
return depth_anything(image_path)["depth"]
@spaces.GPU
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) |