import{S as ks,i as Es,s as Ps,e as n,k as p,w as $,t as h,M as xs,c as o,d as t,m as r,a as l,x as _,h as m,b as c,N as Ct,G as a,g as i,y as w,q as y,o as j,B as b,v as Is}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ds}from"../../chunks/Tip-hf-doc-builder.js";import{I as Ot}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as M}from"../../chunks/CodeBlock-hf-doc-builder.js";function qs(Ie){let u,v,f,k,I,d,L;return{c(){u=h(`The task illustrated in this tutorial is supported by the following model architectures:

`),v=n("p"),f=n("a"),k=h("DPT"),I=h(", "),d=n("a"),L=h("GLPN"),this.h()},l(g){u=m(g,`The task illustrated in this tutorial is supported by the following model architectures:

`),v=o(g,"P",{});var E=l(v);f=o(E,"A",{href:!0});var B=l(f);k=m(B,"DPT"),B.forEach(t),I=m(E,", "),d=o(E,"A",{href:!0});var D=l(d);L=m(D,"GLPN"),D.forEach(t),E.forEach(t),this.h()},h(){c(f,"href","../model_doc/dpt"),c(d,"href","../model_doc/glpn")},m(g,E){i(g,u,E),i(g,v,E),a(v,f),a(f,k),a(v,I),a(v,d),a(d,L)},d(g){g&&t(u),g&&t(v)}}}function zs(Ie){let u,v,f,k,I,d,L,g,E,B,D,ht,De,ie,mt,qe,A,ze,ne,ct,Me,T,_e,ft,ut,we,dt,Ae,oe,gt,Te,F,Ne,q,N,ye,V,vt,je,$t,He,P,_t,le,wt,yt,C,jt,bt,Ge,O,Se,pe,kt,Le,U,Be,J,re,Ut,Fe,he,Et,Ve,R,Ce,x,Pt,be,xt,It,ke,Dt,qt,Oe,me,zt,Ue,W,Je,Z,ce,Jt,Re,z,H,Ee,X,Mt,Pe,At,We,fe,Tt,Ze,G,Nt,Y,Ht,Gt,Xe,K,Ye,S,St,xe,Lt,Bt,Ke,Q,Qe,ue,Ft,et,ee,tt,de,Vt,st,te,at,se,ge,Rt,it;return d=new Ot({}),A=new Ds({props:{$$slots:{default:[qs]},$$scope:{ctx:Ie}}}),F=new M({props:{code:"pip install -q transformers",highlighted:"pip install -q transformers"}}),V=new Ot({}),O=new M({props:{code:`from transformers import pipeline

checkpoint = "vinvino02/glpn-nyu"
depth_estimator = pipeline("depth-estimation", model=checkpoint)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>checkpoint = <span class="hljs-string">&quot;vinvino02/glpn-nyu&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>depth_estimator = pipeline(<span class="hljs-string">&quot;depth-estimation&quot;</span>, model=checkpoint)`}}),U=new M({props:{code:`from PIL import Image
import requests

url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640"
image = Image.open(requests.get(url, stream=True).raw)
image`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&amp;force=true&amp;w=640&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),R=new M({props:{code:"predictions = depth_estimator(image)",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = depth_estimator(image)'}}),W=new M({props:{code:'predictions["depth"]',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>predictions[<span class="hljs-string">&quot;depth&quot;</span>]'}}),X=new Ot({}),K=new M({props:{code:`from transformers import AutoImageProcessor, AutoModelForDepthEstimation

checkpoint = "vinvino02/glpn-nyu"

image_processor = AutoImageProcessor.from_pretrained(checkpoint)
model = AutoModelForDepthEstimation.from_pretrained(checkpoint)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span>checkpoint = <span class="hljs-string">&quot;vinvino02/glpn-nyu&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(checkpoint)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(checkpoint)`}}),Q=new M({props:{code:'pixel_values = image_processor(image, return_tensors="pt").pixel_values',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values'}}),ee=new M({props:{code:`import torch

with torch.no_grad():
    outputs = model(pixel_values)
    predicted_depth = outputs.predicted_depth`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(pixel_values)
<span class="hljs-meta">... </span>    predicted_depth = outputs.predicted_depth`}}),te=new M({props:{code:`import numpy as np

# interpolate to original size
prediction = torch.nn.functional.interpolate(
    predicted_depth.unsqueeze(1),
    size=image.size[::-1],
    mode="bicubic",
    align_corners=False,
).squeeze()
output = prediction.numpy()

formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
depth`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># interpolate to original size</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prediction = torch.nn.functional.interpolate(
<span class="hljs-meta">... </span>    predicted_depth.unsqueeze(<span class="hljs-number">1</span>),
<span class="hljs-meta">... </span>    size=image.size[::-<span class="hljs-number">1</span>],
<span class="hljs-meta">... </span>    mode=<span class="hljs-string">&quot;bicubic&quot;</span>,
<span class="hljs-meta">... </span>    align_corners=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>).squeeze()
<span class="hljs-meta">&gt;&gt;&gt; </span>output = prediction.numpy()

<span class="hljs-meta">&gt;&gt;&gt; </span>formatted = (output * <span class="hljs-number">255</span> / np.<span class="hljs-built_in">max</span>(output)).astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>depth = Image.fromarray(formatted)
<span class="hljs-meta">&gt;&gt;&gt; </span>depth`}}),{c(){u=n("meta"),v=p(),f=n("h1"),k=n("a"),I=n("span"),$(d.$$.fragment),L=p(),g=n("span"),E=h("Monocular depth estimation"),B=p(),D=n("p"),ht=h(`Monocular depth estimation is a computer vision task that involves predicting the depth information of a scene from a
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