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        "description": "\n# ImageDatasetViz \n[![Build Status](https://travis-ci.org/vfdev-5/ImageDatasetViz.svg?branch=master)](https://travis-ci.org/vfdev-5/ImageDatasetViz)\n[![Coverage Status](https://coveralls.io/repos/github/vfdev-5/ImageDatasetViz/badge.svg?branch=master)](https://coveralls.io/github/vfdev-5/ImageDatasetViz?branch=master)\n\nObserve dataset of images and targets in few shots\n\n![VEDAI example](examples/vedai_example.png)\n\n## Descriptions\n\nIdea is to create tools to store images, targets from a dataset as a few large images to observe the dataset \nin few shots.\n\n\n## Installation \n\n#### with pip\n\n```bash\n pip install image-dataset-viz \n```\n\n#### from sources\n```bash\npython setup.py install\n```\nor \n```bash\npip install git+https://github.com/vfdev-5/ImageDatasetViz.git\n```\n\n## Usage\n\n### Render a single datapoint\n\nFirst, we can just take a look on a single data point rendering. Let's assume that we\nhave `img` as, for example, `PIL.Image` and `target` as acceptable target type (`str` or list of points or \n`PIL.Image` mask, etc), thus we can generate a single image with target.\n\n```python\nfrom image_dataset_viz import render_datapoint\n\n# if target is a simple label\nres = render_datapoint(img, \"test label\", text_color=(0, 255, 0), text_size=10)\nplt.imshow(res)\n\n# if target is a mask image (PIL.Image)\nres = render_datapoint(img, target, blend_alpha=0.5)\nplt.imshow(res)\n\n# if target is a bounding box, e.g. np.array([[10, 10], [55, 10], [55, 77], [10, 77]])\nres = render_datapoint(img, target, geom_color=(255, 0, 0))\nplt.imshow(res)\n```\n\n#### Example output on Leaf Segmentation dataset from CVPPP2017\n\n![image with mask](examples/image_mask.png)  ![image with label](examples/image_label.png)  ![image with bbox label](examples/image_bbox_label.png)\n\n### Export complete dataset\nFor example, we have a dataset of image files and annotations files (polygons with labels):\n```python\nimg_files = [\n    '/path/to/image_1.ext',\n    '/path/to/image_2.ext',\n    ...\n    '/path/to/image_1000.ext',\n]\ntarget_files = [\n    '/path/to/target_1.ext2',\n    '/path/to/target_2.ext2',\n    ...\n    '/path/to/target_1000.ext2',\n]\n```\nWe can produce a single image composed of 20x50 small samples with targets to better visualize the whole dataset.\nLet's assume that we do need a particular processing to open the images in RGB 8bits format:\n```python\nfrom PIL import Image\n\ndef read_img_fn(img_filepath):\n    return Image.open(img_filepath).convert('RGB')\n```\nand let's say the annotations are just lines with points and a label, e.g. `12 23 34 45 56 67 car`\n```python\nfrom pathlib import Path\nimport numpy as np\n\ndef read_target_fn(target_filepath):\n    with Path(target_filepath).open('r') as handle:\n        points_labels = []\n        while True:\n            line = handle.readline()\n            if len(line) == 0:\n                break\n            splt = line[:-1].split(' ')  # Split into points and labels\n            label = splt[-1]\n            points = np.array(splt[:-1]).reshape(-1, 2)\n            points_labels.append((points, label))\n    return points_labels\n```\nNow we can export the dataset\n```python\nde = DatasetExporter(read_img_fn=read_img_fn, read_target_fn=read_target_fn,\n                     img_id_fn=lambda fp: Path(fp).stem, n_cols=20)\nde.export(img_files, target_files, output_folder=\"dataset_viz\")\n```\nand thus we should obtain a single png image with composed of 20x50 small samples.\n\n\n## Examples\n\n- [CIFAR10](examples/example_CIFAR10.ipynb)\n- [VEDAI](examples/example_VEDAI.ipynb)\n\n\n",
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