Create prob-index.js

prob-index.js

@@ -0,0 +1,82 @@
+import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';
+
+// Since we will download the model from the Hugging Face Hub, we can skip the local model check
+env.allowLocalModels = false;
+
+// Reference the elements that we will need
+const status = document.getElementById('status');
+const fileUpload = document.getElementById('upload');
+const imageContainer = document.getElementById('container');
+const example = document.getElementById('example');
+
+const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
+
+// Create a new object detection pipeline
+status.textContent = 'Loading model...';
+// To-Do #1 pipeline API를 사용하여 detr-resnet-50 object detection 모델의 instance를 detector라는 이름을 붙여 생성하십시오.
+// DETR 모델 참고 문서 https://huggingface.co/facebook/detr-resnet-50
+const detector = await '???';
+status.textContent = 'Ready';
+
+example.addEventListener('click', (e) => {
+    e.preventDefault();
+    detect(EXAMPLE_URL);
+});
+
+fileUpload.addEventListener('change', function (e) {
+    const file = e.target.files[0];
+    if (!file) {
+        return;
+    }
+
+    const reader = new FileReader();
+
+    // Set up a callback when the file is loaded
+    reader.onload = e2 => detect(e2.target.result);
+
+    reader.readAsDataURL(file);
+});
+
+
+// Detect objects in the image
+async function detect(img) {
+    imageContainer.innerHTML = '';
+    imageContainer.style.backgroundImage = `url(${img})`;
+
+    status.textContent = 'Analysing...';
+    // To-Do #2  객체 탐지를 위한 오브젝트에 threshold를 0.5, percentage를 true로 지정하고 그 결과를 output에 저장하십시오
+    const output = ???( 
+      // threshold 값을 지정하고 쉼표를 붙이시오
+      // percentage 지정
+    );
+    status.textContent = '';
+    output.forEach(renderBox);
+}
+
+// Render a bounding box and label on the image
+function renderBox({ box, label }) {
+    const { xmax, xmin, ymax, ymin } = box;
+
+    // Generate a random color for the box
+    const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);
+
+    // Draw the box
+    const boxElement = document.createElement('div');
+    boxElement.className = 'bounding-box';
+    Object.assign(boxElement.style, {
+        borderColor: color,
+        left: 100 * xmin + '%',
+        top: 100 * ymin + '%',
+        width: 100 * (xmax - xmin) + '%',
+        height: 100 * (ymax - ymin) + '%',
+    })
+
+    // Draw label
+    const labelElement = document.createElement('span');
+    labelElement.textContent = label;
+    labelElement.className = 'bounding-box-label';
+    labelElement.style.backgroundColor = color;
+
+    boxElement.appendChild(labelElement);
+    imageContainer.appendChild(boxElement);
+}