import{s as Te,o as Ue,n as je}from"../chunks/scheduler.889ebb13.js";import{S as be,i as Ce,g as p,s as n,u as c,A as xe,h as i,f as s,c as a,j as M,v as h,y as o,k as m,o as d,a as l,w as u,d as f,t as g,x as y,m as ke,n as Ze}from"../chunks/index.8980e686.js";import{T as _e}from"../chunks/Tip.7191df11.js";import{I as qt}from"../chunks/IconCopyLink.357761cd.js";import{C as U}from"../chunks/CodeBlock.25fc3430.js";function We(dt){let r,v,j='<a href="../model_doc/dpt">DPT</a>, <a href="../model_doc/glpn">GLPN</a>';return{c(){r=ke(`이 튜토리얼에서 다루는 작업은 다음 모델 아키텍처에서 지원됩니다:

`),v=p("p"),v.innerHTML=j},l(w){r=Ze(w,`이 튜토리얼에서 다루는 작업은 다음 모델 아키텍처에서 지원됩니다:

`),v=i(w,"P",{"data-svelte-h":!0}),o(v)!=="svelte-940txo"&&(v.innerHTML=j)},m(w,J){l(w,r,J),l(w,v,J)},p:je,d(w){w&&(s(r),s(v))}}}function Ge(dt){let r,v,j,w,J,b,mt,G,Yt,pt,Dt="단일 영상 기반 깊이 추정",ut,I,Kt=`단일 영상 기반 깊이 추정은 한 장면의 단일 이미지에서 장면의 깊이 정보를 예측하는 컴퓨터 비전 작업입니다.
즉, 단일 카메라 시점의 장면에 있는 물체의 거리를 예측하는 과정입니다.`,ft,B,Ot=`단일 영상 기반 깊이 추정은 3D 재구성, 증강 현실, 자율 주행, 로봇 공학 등 다양한 분야에서 응용됩니다.
조명 조건, 가려짐, 텍스처와 같은 요소의 영향을 받을 수 있는 장면 내 물체와 해당 깊이 정보 간의 복잡한 관계를 모델이 이해해야 하므로 까다로운 작업입니다.`,gt,C,yt,H,te="이번 가이드에서 배울 내용은 다음과 같습니다:",vt,R,ee="<li>깊이 추정 파이프라인 만들기</li> <li>직접 깊이 추정 추론하기</li>",wt,V,se="시작하기 전에, 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:",Jt,E,Mt,$,x,rt,N,Qt,it,le="깊이 추정 파이프라인",$t,z,ne=`깊이 추정을 추론하는 가장 간단한 방법은 해당 기능을 제공하는 <code>pipeline()</code>을 사용하는 것입니다.
<a href="https://huggingface.co/models?pipeline_tag=depth-estimation&amp;sort=downloads" rel="nofollow">Hugging Face Hub 체크포인트</a>에서 파이프라인을 초기화합니다:`,Tt,S,Ut,F,ae="다음으로, 분석할 이미지를 한 장 선택하세요:",jt,X,bt,k,pe='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-estimation-example.jpg" alt="Photo of a busy street"/>',Ct,A,ie="이미지를 파이프라인으로 전달합니다.",xt,P,kt,Y,oe=`파이프라인은 두 개의 항목을 가지는 딕셔너리를 반환합니다.
첫 번째는 <code>predicted_depth</code>로 각 픽셀의 깊이를 미터로 표현한 값을 가지는 텐서입니다.
두 번째는 <code>depth</code>로 깊이 추정 결과를 시각화하는 PIL 이미지입니다.`,Zt,Q,me="이제 시각화한 결과를 살펴보겠습니다:",_t,L,Wt,Z,re='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>',Gt,T,_,ct,q,Lt,ot,ce="직접 깊이 추정 추론하기",It,D,he=`이제 깊이 추정 파이프라인 사용법을 살펴보았으니 동일한 결과를 복제하는 방법을 살펴보겠습니다.
<a href="https://huggingface.co/models?pipeline_tag=depth-estimation&amp;sort=downloads" rel="nofollow">Hugging Face Hub 체크포인트</a>에서 모델과 관련 프로세서를 가져오는 것부터 시작합니다.
여기서 이전에 사용한 체크포인트와 동일한 것을 사용합니다:`,Bt,K,Ht,O,de=`필요한 이미지 변환을 처리하는 <code>image_processor</code>를 사용하여 모델에 대한 이미지 입력을 준비합니다.
<code>image_processor</code>는 크기 조정 및 정규화 등 필요한 이미지 변환을 처리합니다:`,Rt,tt,Vt,et,ue="준비한 입력을 모델로 전달합니다:",Et,st,Nt,lt,fe="결과를 시각화합니다:",zt,nt,St,W,ge='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>',Ft,ht,Xt;return G=new qt({}),C=new _e({props:{$$slots:{default:[We]},$$scope:{ctx:dt}}}),E=new U({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwdHJhbnNmb3JtZXJz",highlighted:"pip install -q transformers"}}),N=new qt({}),S=new U({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)`}}),X=new U({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`}}),P=new U({props:{code:"cHJlZGljdGlvbnMlMjAlM0QlMjBkZXB0aF9lc3RpbWF0b3IoaW1hZ2Up",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = depth_estimator(image)'}}),L=new U({props:{code:"cHJlZGljdGlvbnMlNUIlMjJkZXB0aCUyMiU1RA==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>predictions[<span class="hljs-string">&quot;depth&quot;</span>]'}}),q=new qt({}),K=new U({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)`}}),tt=new U({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'}}),st=new U({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`}}),nt=new U({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"># 원본 사이즈로 복원</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; 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