Commit
·
8dddd4b
0
Parent(s):
Duplicate from capjamesg/roboflow
Browse files- .gitattributes +34 -0
- README.md +11 -0
- app.js +200 -0
- index.html +69 -0
- style.css +28 -0
- styles.css +75 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: Roboflow
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emoji: 🌍
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colorFrom: green
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colorTo: indigo
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sdk: static
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pinned: false
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duplicated_from: capjamesg/roboflow
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.js
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// To display predictions, this app has:
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// 1. A video that shows a feed from the user's webcam
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// 2. A canvas that appears over the video and shows predictions
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// When the page loads, a user is asked to give webcam permission.
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// After this happens, the model initializes and starts to make predictions
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// On the first prediction, an initialiation step happens in detectFrame()
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// to prepare the canvas on which predictions are displayed.
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var bounding_box_colors = {};
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var user_confidence = 0.6;
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// Update the colors in this list to set the bounding box colors
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var color_choices = [
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"#C7FC00",
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"#FF00FF",
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"#8622FF",
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"#FE0056",
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"#00FFCE",
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"#FF8000",
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"#00B7EB",
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"#FFFF00",
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"#0E7AFE",
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"#FFABAB",
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"#0000FF",
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"#CCCCCC",
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];
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var canvas_painted = false;
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var canvas = document.getElementById("video_canvas");
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var ctx = canvas.getContext("2d");
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var model = null;
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function detectFrame() {
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// On first run, initialize a canvas
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// On all runs, run inference using a video frame
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// For each video frame, draw bounding boxes on the canvas
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if (!model) return requestAnimationFrame(detectFrame);
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model.detect(video).then(function(predictions) {
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if (!canvas_painted) {
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var video_start = document.getElementById("video1");
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canvas.style.width = video_start.width + "px";
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canvas.style.height = video_start.height + "px";
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canvas.width = video_start.width;
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canvas.height = video_start.height;
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// adjust top to margin position of video
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canvas.top = video_start.top;
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canvas.left = video_start.left;
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canvas.style.top = video_start.top + "px";
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canvas.style.left = video_start.left + "px";
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canvas.style.position = "absolute";
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video_start.style.display = "block";
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canvas.style.display = "absolute";
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canvas_painted = true;
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var loading = document.getElementById("loading");
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loading.style.display = "none";
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}
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requestAnimationFrame(detectFrame);
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ctx.clearRect(0, 0, canvas.width, canvas.height);
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if (video) {
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drawBoundingBoxes(predictions, ctx)
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}
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});
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}
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function drawBoundingBoxes(predictions, ctx) {
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// For each prediction, choose or assign a bounding box color choice,
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// then apply the requisite scaling so bounding boxes appear exactly
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// around a prediction.
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// If you want to do anything with predictions, start from this function.
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// For example, you could display them on the web page, check off items on a list,
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// or store predictions somewhere.
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for (var i = 0; i < predictions.length; i++) {
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var confidence = predictions[i].confidence;
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console.log(user_confidence)
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if (confidence < user_confidence) {
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continue
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}
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if (predictions[i].class in bounding_box_colors) {
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ctx.strokeStyle = bounding_box_colors[predictions[i].class];
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} else {
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var color =
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color_choices[Math.floor(Math.random() * color_choices.length)];
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ctx.strokeStyle = color;
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// remove color from choices
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color_choices.splice(color_choices.indexOf(color), 1);
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bounding_box_colors[predictions[i].class] = color;
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}
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var prediction = predictions[i];
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var x = prediction.bbox.x - prediction.bbox.width / 2;
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var y = prediction.bbox.y - prediction.bbox.height / 2;
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var width = prediction.bbox.width;
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var height = prediction.bbox.height;
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ctx.rect(x, y, width, height);
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ctx.fillStyle = "rgba(0, 0, 0, 0)";
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ctx.fill();
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ctx.fillStyle = ctx.strokeStyle;
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ctx.lineWidth = "4";
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ctx.strokeRect(x, y, width, height);
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ctx.font = "25px Arial";
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ctx.fillText(prediction.class + " " + Math.round(confidence * 100) + "%", x, y - 10);
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}
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}
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function webcamInference() {
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// Ask for webcam permissions, then run main application.
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var loading = document.getElementById("loading");
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loading.style.display = "block";
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navigator.mediaDevices
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.getUserMedia({ video: { facingMode: "environment" } })
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.then(function(stream) {
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video = document.createElement("video");
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video.srcObject = stream;
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video.id = "video1";
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// hide video until the web stream is ready
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video.style.display = "none";
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video.setAttribute("playsinline", "");
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document.getElementById("video_canvas").after(video);
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video.onloadedmetadata = function() {
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video.play();
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}
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// on full load, set the video height and width
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video.onplay = function() {
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height = video.videoHeight;
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width = video.videoWidth;
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// scale down video by 0.75
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height = height * 0.75;
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width = width * 0.75;
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width = Math.round(width);
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height = Math.round(height);
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video.setAttribute("width", width);
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video.setAttribute("height", height);
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video.style.width = width + "px";
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video.style.height = height + "px";
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canvas.style.width = width + "px";
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canvas.style.height = height + "px";
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canvas.width = width;
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canvas.height = height;
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document.getElementById("video_canvas").style.display = "block";
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};
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ctx.scale(1, 1);
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// Load the Roboflow model using the publishable_key set in index.html
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// and the model name and version set at the top of this file
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roboflow
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.auth({
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publishable_key: publishable_key,
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})
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.load({
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model: MODEL_NAME,
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version: MODEL_VERSION,
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})
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.then(function(m) {
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model = m;
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// Images must have confidence > CONFIDENCE_THRESHOLD to be returned by the model
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m.configure({ threshold: CONFIDENCE_THRESHOLD });
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// Start inference
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detectFrame();
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});
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})
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.catch(function(err) {
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console.log(err);
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});
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}
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function changeConfidence () {
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user_confidence = document.getElementById("confidence").value / 100;
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}
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document.getElementById("confidence").addEventListener("input", changeConfidence);
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webcamInference();
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index.html
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<!DOCTYPE html>
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<html>
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<head>
|
5 |
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<meta charset="UTF-8">
|
6 |
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|
7 |
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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|
9 |
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<title>Roboflow + Hugging Face Example</title>
|
10 |
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|
11 |
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<!-- Load roboflow.js, the script that will handle loading and running our computer vision model in the browser -->
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12 |
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<script src="https://cdn.roboflow.com/0.2.25/roboflow.js"></script>
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|
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<link rel="stylesheet" href="/styles.css" />
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<script>
|
17 |
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// Replace the key below with your Roboflow publishable key
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18 |
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var publishable_key = "rf_U7AD2Mxh39N7jQ3B6cP8xAyufLH3";
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19 |
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|
20 |
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// Change these values to set your model name, version, and the
|
21 |
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// confidence threshold that must be met for the model to return
|
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// a prediction.
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|
24 |
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// We recommend setting a low CONFIDENCE_THRESHOLD value while testing
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25 |
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// and filtering out predictions by confidence level using the
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26 |
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//"Prediction Confidence %" filter
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27 |
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const CONFIDENCE_THRESHOLD = 0.1;
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const MODEL_NAME = "microsoft-coco";
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const MODEL_VERSION = 9;
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</script>
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</head>
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<body>
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35 |
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<main>
|
36 |
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<h1>Roboflow + Hugging Face Example 🚀</h1>
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37 |
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<p>Roboflow enables you to build and run custom computer vision models in your browser, on your device, and via API.
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38 |
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</p>
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<p>The below example runs <a href="https://universe.roboflow.com/jacob-solawetz/microsoft-coco">Microsoft's
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COCO</a> model to identify common objects, which is one of 50,000 open source models ready to use on <a
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href="https://universe.roboflow.com">Roboflow Universe</a></p>
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<p>Using the demo below, you can identify <a href="https://universe.roboflow.com/jacob-solawetz/microsoft-coco">80
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different objects</a> using your webcam, from people to chairs to cups.</p>
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45 |
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46 |
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<p id="loading">Loading...</p>
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47 |
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48 |
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<!-- The canvas below is required -->
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49 |
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<section class="infer-widget">
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50 |
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<canvas width="640" height="480" id="video_canvas"></canvas>
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51 |
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</section>
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52 |
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53 |
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<section id="settings">
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54 |
+
<label for="confidence">Prediction Confidence %</label><br>
|
55 |
+
<input type="range" min="1" max="100" value="60" class="slider" id="confidence">
|
56 |
+
</section>
|
57 |
+
|
58 |
+
<section class="links">
|
59 |
+
<a href="https://blog.roboflow.com/getting-started-with-roboflow/" class="styled-button">Build a Custom Model</a>
|
60 |
+
<a href="https://universe.roboflow.com" class="styled-button styled-button-turqouise">Explore 50k+
|
61 |
+
Models and Datasets</a>
|
62 |
+
</section>
|
63 |
+
|
64 |
+
<!-- Load our application logic -->
|
65 |
+
<script src="/app.js"></script>
|
66 |
+
</main>
|
67 |
+
</body>
|
68 |
+
|
69 |
+
</html>
|
style.css
ADDED
@@ -0,0 +1,28 @@
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|
1 |
+
body {
|
2 |
+
padding: 2rem;
|
3 |
+
font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
|
4 |
+
}
|
5 |
+
|
6 |
+
h1 {
|
7 |
+
font-size: 16px;
|
8 |
+
margin-top: 0;
|
9 |
+
}
|
10 |
+
|
11 |
+
p {
|
12 |
+
color: rgb(107, 114, 128);
|
13 |
+
font-size: 15px;
|
14 |
+
margin-bottom: 10px;
|
15 |
+
margin-top: 5px;
|
16 |
+
}
|
17 |
+
|
18 |
+
.card {
|
19 |
+
max-width: 620px;
|
20 |
+
margin: 0 auto;
|
21 |
+
padding: 16px;
|
22 |
+
border: 1px solid lightgray;
|
23 |
+
border-radius: 16px;
|
24 |
+
}
|
25 |
+
|
26 |
+
.card p:last-child {
|
27 |
+
margin-bottom: 0;
|
28 |
+
}
|
styles.css
ADDED
@@ -0,0 +1,75 @@
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|
1 |
+
html, body {
|
2 |
+
height: 100%;
|
3 |
+
width: 100%;
|
4 |
+
font-family: Arial, Helvetica, sans-serif;
|
5 |
+
}
|
6 |
+
|
7 |
+
p, a {
|
8 |
+
line-height: 1.5em;
|
9 |
+
}
|
10 |
+
|
11 |
+
#loading {
|
12 |
+
display: none;
|
13 |
+
}
|
14 |
+
|
15 |
+
#settings {
|
16 |
+
margin-top: 30px;
|
17 |
+
margin-bottom: 30px;
|
18 |
+
}
|
19 |
+
|
20 |
+
h1, a {
|
21 |
+
color: #6706ce;
|
22 |
+
}
|
23 |
+
|
24 |
+
label {
|
25 |
+
font-weight: 50px;
|
26 |
+
}
|
27 |
+
|
28 |
+
a {
|
29 |
+
text-decoration: none;
|
30 |
+
}
|
31 |
+
|
32 |
+
main {
|
33 |
+
width: 40em;
|
34 |
+
margin: auto;
|
35 |
+
text-align: center;
|
36 |
+
}
|
37 |
+
|
38 |
+
.styled-button {
|
39 |
+
color: #fff;
|
40 |
+
text-align: center;
|
41 |
+
background-color: #6706ce;
|
42 |
+
border: 1px solid #6706ce;
|
43 |
+
border-radius: 4px;
|
44 |
+
padding: 13px 20px;
|
45 |
+
line-height: 1.5em;
|
46 |
+
text-decoration: none;
|
47 |
+
display: block;
|
48 |
+
margin: 10px;
|
49 |
+
margin-bottom: 25px;
|
50 |
+
width: 100%;
|
51 |
+
}
|
52 |
+
|
53 |
+
.styled-button-turqouise {
|
54 |
+
border: none;
|
55 |
+
color: black;
|
56 |
+
background-color: #00ffce;
|
57 |
+
}
|
58 |
+
|
59 |
+
.infer-widget {
|
60 |
+
width: 480px;
|
61 |
+
margin: auto;
|
62 |
+
}
|
63 |
+
|
64 |
+
.links {
|
65 |
+
display: flex;
|
66 |
+
text-align: center;
|
67 |
+
}
|
68 |
+
|
69 |
+
.links:first-child {
|
70 |
+
flex: 0 50%;
|
71 |
+
}
|
72 |
+
|
73 |
+
.links:last-child {
|
74 |
+
flex: 1 50%;
|
75 |
+
}
|