1576 lines
56 KiB
Plaintext
1576 lines
56 KiB
Plaintext
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"### Online Colab Demo: https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba\n",
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"### Hugging Face Spaces Demo: https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo"
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]
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},
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"outputs": [],
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"source": [
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"# Imports\n",
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"from PIL import Image\n",
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"import torch\n",
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"from torchvision import transforms\n",
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"from IPython.display import display\n",
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"\n",
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"import sys\n",
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"sys.path.insert(0, \"../\")\n",
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"from models.birefnet import BiRefNet\n",
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"\n",
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"\n",
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"# Load Model\n",
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"# Option 2 and Option 3 is better for local running -- we can modify codes locally.\n",
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"\n",
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"# # # Option 1: loading BiRefNet with weights:\n",
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"# from transformers import AutoModelForImageSegmentation\n",
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"# birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet', trust_remote_code=True)\n",
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"\n",
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"# Option-2: loading weights with BiReNet codes:\n",
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"birefnet = BiRefNet.from_pretrained(\n",
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" [\n",
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" 'zhengpeng7/BiRefNet',\n",
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" 'zhengpeng7/BiRefNet-portrait',\n",
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" 'zhengpeng7/BiRefNet-legacy', 'zhengpeng7/BiRefNet-DIS5K-TR_TEs', 'zhengpeng7/BiRefNet-DIS5K', 'zhengpeng7/BiRefNet-HRSOD', 'zhengpeng7/BiRefNet-COD',\n",
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" 'zhengpeng7/BiRefNet_lite', # Modify the `bb` in `config.py` to `swin_v1_tiny`.\n",
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" ][0]\n",
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")\n",
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"\n",
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"# # Option-3: Loading model and weights from local disk:\n",
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"# from utils import check_state_dict\n",
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"\n",
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"# birefnet = BiRefNet(bb_pretrained=False)\n",
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"# state_dict = torch.load('../BiRefNet-general-epoch_244.pth', map_location='cpu')\n",
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"# state_dict = check_state_dict(state_dict)\n",
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"# birefnet.load_state_dict(state_dict)\n",
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"\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"\n",
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"torch.set_float32_matmul_precision(['high', 'highest'][0])\n",
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"\n",
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"birefnet.to(device)\n",
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"birefnet.eval()\n",
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"print('BiRefNet is ready to use.')\n",
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"\n",
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"# Input Data\n",
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"transform_image = transforms.Compose([\n",
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||
" transforms.Resize((1024, 1024)),\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
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"])"
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"height": 1000
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},
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from glob import glob\n",
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"from image_proc import refine_foreground\n",
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"\n",
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"src_dir = '../images_todo'\n",
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||
"image_paths = glob(os.path.join(src_dir, '*'))\n",
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"dst_dir = '../predictions'\n",
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"os.makedirs(dst_dir, exist_ok=True)\n",
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"for image_path in image_paths:\n",
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" print('Processing {} ...'.format(image_path))\n",
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" image = Image.open(image_path)\n",
|
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" input_images = transform_image(image).unsqueeze(0).to(device)\n",
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"\n",
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" # Prediction\n",
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" with torch.no_grad():\n",
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" preds = birefnet(input_images)[-1].sigmoid().cpu()\n",
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" pred = preds[0].squeeze()\n",
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"\n",
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" # Show Results\n",
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" pred_pil = transforms.ToPILImage()(pred)\n",
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" pred_pil.resize(image.size).save(image_path.replace(src_dir, dst_dir))\n",
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"\n",
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" # Visualize the last sample:\n",
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" # Scale proportionally with max length to 1024 for faster showing\n",
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" scale_ratio = 1024 / max(image.size)\n",
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" scaled_size = (int(image.size[0] * scale_ratio), int(image.size[1] * scale_ratio))\n",
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"\n",
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" image_masked = refine_foreground(image, pred_pil)\n",
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" image_masked.putalpha(pred_pil.resize(image.size))\n",
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"\n",
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"display(image.resize(scaled_size))\n",
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"display(pred_pil.resize(scaled_size))\n",
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"display(image_masked.resize(scaled_size))"
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]
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},
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