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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import numpy as np\n",
"from PIL import Image\n",
"from transformers import MaskFormerForInstanceSegmentation, MaskFormerImageProcessor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_id = f\"facebook/maskformer-swin-large-coco\"\n",
"\n",
"feature_extractor = MaskFormerImageProcessor.from_pretrained(model_id)\n",
"model = MaskFormerForInstanceSegmentation.from_pretrained(model_id)\n",
"\n",
"with Image.open(\"../color-filter-calculator/assets/Artshack_screen.jpg\") as img:\n",
" img_size = (img.height, img.width)\n",
" inputs = feature_extractor(images=img, return_tensors=\"pt\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"outputs = model(**inputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results = feature_extractor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[img_size])[0]\n",
"results = results.numpy()\n",
"\n",
"labels = np.unique(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for label_id in labels:\n",
" print(model.config.id2label[label_id])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.15 ('hf-gradio')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.15"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "4888b226c77b860705e4be316b14a092026f41c3585ee0ddb38f3008c0cb495e"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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