import{S as _e,i as be,s as je,e as p,k as m,w as J,t as r,M as Te,c as n,d as s,m as f,a as i,x as v,h as o,b as c,N as Xs,G as l,g as a,y as w,q as $,o as M,B as U,v as Ee}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ze}from"../../chunks/Tip-hf-doc-builder.js";import{I as zs}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as W}from"../../chunks/CodeBlock-hf-doc-builder.js";function ke(Zt){let u,g,h,_,Z,d,H;return{c(){u=r(`\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC5D0\uC11C \uB2E4\uB8E8\uB294 \uC791\uC5C5\uC740 \uB2E4\uC74C \uBAA8\uB378 \uC544\uD0A4\uD14D\uCC98\uC5D0\uC11C \uC9C0\uC6D0\uB429\uB2C8\uB2E4:

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`),g=n(y,"P",{});var b=i(g);h=n(b,"A",{href:!0});var F=i(h);_=o(F,"DPT"),F.forEach(s),Z=o(b,", "),d=n(b,"A",{href:!0});var k=i(d);H=o(k,"GLPN"),k.forEach(s),b.forEach(s),this.h()},h(){c(h,"href","../model_doc/dpt"),c(d,"href","../model_doc/glpn")},m(y,b){a(y,u,b),a(y,g,b),l(g,h),l(h,_),l(g,Z),l(g,d),l(d,H)},d(y){y&&s(u),y&&s(g)}}}function Ge(Zt){let u,g,h,_,Z,d,H,y,b,F,k,os,kt,at,ms,Gt,B,It,pt,fs,Wt,R,Jt,cs,hs,vt,us,Bt,nt,ds,Rt,S,Vt,G,V,wt,A,ys,$t,gs,Nt,j,Js,Mt,vs,ws,X,$s,Ms,Ct,z,Ht,it,Us,Ft,P,St,Y,rt,Ps,At,ot,_s,Xt,D,zt,T,bs,Ut,js,Ts,_t,Es,Zs,Pt,mt,ks,Yt,Q,Dt,x,ft,Ys,Qt,I,N,bt,q,Gs,jt,Is,xt,C,Ws,L,Bs,Rs,qt,K,Lt,E,Vs,Tt,Ns,Cs,Et,Hs,Fs,Kt,O,Ot,ct,Ss,ts,tt,ss,ht,As,es,st,ls,et,ut,Ds,as;return d=new zs({}),B=new Ze({props:{$$slots:{default:[ke]},$$scope:{ctx:Zt}}}),S=new W({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwdHJhbnNmb3JtZXJz",highlighted:"pip install -q transformers"}}),A=new zs({}),z=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBY2hlY2twb2ludCUyMCUzRCUyMCUyMnZpbnZpbm8wMiUyRmdscG4tbnl1JTIyJTBBZGVwdGhfZXN0aW1hdG9yJTIwJTNEJTIwcGlwZWxpbmUoJTIyZGVwdGgtZXN0aW1hdGlvbiUyMiUyQyUyMG1vZGVsJTNEY2hlY2twb2ludCk=",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)`}}),P=new W({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRnVuc3BsYXNoLmNvbSUyRnBob3RvcyUyRkh3QkFzU2JQQkRVJTJGZG93bmxvYWQlM0ZpeGlkJTNETW53eE1qQTNmREI4TVh4elpXRnlZMmg4TXpSOGZHTmhjaVV5TUdsdUpUSXdkR2hsSlRJd2MzUnlaV1YwZkdWdWZEQjhNSHg4ZkRFMk56ZzVNREV3T0RnJTI2Zm9yY2UlM0R0cnVlJTI2dyUzRDY0MCUyMiUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihyZXF1ZXN0cy5nZXQodXJsJTJDJTIwc3RyZWFtJTNEVHJ1ZSkucmF3KSUwQWltYWdl",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`}}),D=new W({props:{code:"cHJlZGljdGlvbnMlMjAlM0QlMjBkZXB0aF9lc3RpbWF0b3IoaW1hZ2Up",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = depth_estimator(image)'}}),Q=new W({props:{code:"cHJlZGljdGlvbnMlNUIlMjJkZXB0aCUyMiU1RA==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>predictions[<span class="hljs-string">&quot;depth&quot;</span>]'}}),q=new zs({}),K=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMEF1dG9Nb2RlbEZvckRlcHRoRXN0aW1hdGlvbiUwQSUwQWNoZWNrcG9pbnQlMjAlM0QlMjAlMjJ2aW52aW5vMDIlMkZnbHBuLW55dSUyMiUwQSUwQWltYWdlX3Byb2Nlc3NvciUyMCUzRCUyMEF1dG9JbWFnZVByb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckRlcHRoRXN0aW1hdGlvbi5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCk=",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)`}}),O=new W({props:{code:"cGl4ZWxfdmFsdWVzJTIwJTNEJTIwaW1hZ2VfcHJvY2Vzc29yKGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMikucGl4ZWxfdmFsdWVz",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'}}),tt=new W({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEF3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwJTIwJTIwb3V0cHV0cyUyMCUzRCUyMG1vZGVsKHBpeGVsX3ZhbHVlcyklMEElMjAlMjAlMjAlMjBwcmVkaWN0ZWRfZGVwdGglMjAlM0QlMjBvdXRwdXRzLnByZWRpY3RlZF9kZXB0aA==",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`}}),st=new W({props:{code:"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",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"># \uC6D0\uBCF8 \uC0AC\uC774\uC988\uB85C \uBCF5\uC6D0</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)
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