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<!doctype html> |
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<html style="height:100%;"> |
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<head> |
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<title>Download Your Annotations</title> |
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> |
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<link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet"> |
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<script>hljs.initHighlightingOnLoad();</script> |
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<style> |
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.python-code-block { |
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border: 1px solid #ddd; |
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padding: 15px; |
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margin-left: 20px; |
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text-align: left; |
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font-family: monospace; |
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white-space: pre-wrap; |
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} |
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</style> |
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</head> |
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<body> |
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<header class="flex items-center justify-between bg-white p-4 sm:p-6 rounded-lg shadow-md mb-8 flex-wrap"> |
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<div class="flex items-center space-x-4 mb-4 sm:mb-0 w-full sm:w-auto justify-center sm:justify-start"> |
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<img src="{{ url_for('static', filename='images/logo_black.png') }}" alt="KrongAI" class="squared-full shadow-sm" height="150cqh"> |
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</div> |
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<div class="flex-grow text-center mb-4 sm:mb-0 order-first sm:order-none w-full sm:w-auto"> |
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<h1 class="text-3xl sm:text-4xl font-bold text-gray-800 leading-tight"> |
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🇰🇭 For the development of AI 🇰🇭 |
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</h1> |
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</div> |
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<div class="w-full sm:w-auto flex justify-center sm:justify-end"> |
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<div class="w-[128px] h-0 sm:h-[60px]"></div> |
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</div> |
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</header> |
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<div class="container" style="margin-top:20px;"> |
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<center> |
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<div class="jumbotron"> |
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<h2>Download the annotations as jsonl file</h2> |
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<h3>The annotations are in Pascal VOC format</h3> |
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<button id="downloadButton" style="margin-top:10px;" class="btn btn-success">Download</button> |
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<div id="messageBox" class="mt-8 p-4 bg-blue-100 border border-blue-400 text-blue-700 rounded-lg hidden" role="alert"> |
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<p id="messageText" class="font-medium"></p> |
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</div> |
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<h5>You can use <code>datasets</code> package of huggingface to load the downloaded dataset. See demo code below.</h5> |
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<div class="python-code-block"> |
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<pre><code class="language-python"> |
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from datasets import load_dataset |
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your_data = load_dataset('imagefolder', data_dir='annotated_data') # this will use metadata.jsonl in annotated_data to create your_data |
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print(your_data) |
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# optional: you can easily plot one instance of your_data in jupyter notebook |
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import torch |
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from torchvision.utils import draw_bounding_boxes |
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from torchvision.transforms.functional import pil_to_tensor, to_pil_image |
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example = your_data['train'][0] |
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boxes_xyxy = torch.tensor(example['objects']['bbox']) |
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labels = [x for x in example['objects']['names']] |
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to_pil_image( |
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draw_bounding_boxes( |
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pil_to_tensor(example['image'].convert('RGB')), |
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boxes_xyxy, |
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colors="red", |
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labels=labels, |
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) |
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) |
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</code></pre> |
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</div> |
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<script> |
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document.getElementById('downloadButton').addEventListener('click', function() { |
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var messageBox = document.getElementById('messageBox'); |
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var messageText = document.getElementById('messageText'); |
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messageText.textContent = 'Good luck with your project!'; |
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messageBox.classList.remove('hidden', 'bg-red-100', 'border-red-400', 'text-red-700', 'bg-green-100', 'border-green-400', 'text-green-700', 'bg-blue-100', 'border-blue-400', 'text-blue-700'); |
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messageBox.classList.add('bg-green-100', 'border-green-400', 'text-green-700'); |
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messageBox.classList.remove('hidden'); |
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var link = document.createElement('a'); |
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link.href = "{{ url_for('download', download='annotations_pascal_voc.csv') }}"; |
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link.download = 'my_file.txt'; |
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document.body.appendChild(link); |
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link.click(); |
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document.body.removeChild(link); |
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setTimeout(function() { |
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window.location.href = "{{ url_for('index') }}"; |
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}, 3000); |
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}); |
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</script> |
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</div> |
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</center> |
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</div> |
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<footer class="mt-8 mb-4 text-center text-gray-500 text-sm w-full max-w-4xl"> |
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Thanks Huggingface Spaces 🤗 |
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</footer> |
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</body> |
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</html> |
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