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supported\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\nSynthetic Data generation with Flip! Generate thousands of new 2D images from a small batch of objects and backgrounds.\n\n## Installation\n\nInstall Flip using pip:\n\n```bash\npip install flip-data\n```\n\n### Dependencies\n\nFlip requires:\n- Python (\u003e= 3.7)\n- Opencv (\u003e= 4.3.0)\n- Numpy (\u003e= 1.19.1)\n\n## Quick Start ([Example](https://github.com/linkedai/flip/blob/master/examples/README.md))\n\nTo try Flip library you can run ```examples/data_generator.py```. \nYou will need to add background images and objects to compose your new training dataset, then\nplace them in the following directories:\n```python\nBACKGROUNDS_PATTERN = \"examples/data/backgrounds/*\"\nOBJECTS_PATTERN = \"examples/data/objects/**/*\"\n```\n\nThe main workflow in Flip is to create transformers and then execute them as follows: \n\n```python\n## Import Flip transformers\nimport flip.transformers as tr\n\nOUT_DIR = \"examples/result\"\n\n...\n\n## Create Child transformers\ntransform_objects = [\n        tr.data_augmentation.Rotate(mode='random'),\n        tr.data_augmentation.Flip(mode='y'),\n        tr.data_augmentation.RandomResize(\n            mode='symmetric_w',\n            relation='parent',\n            w_percentage_min=0.2,\n            w_percentage_max=0.5\n        )\n    ]\n\n## Create main transformer\ntransform = tr.Compose([\n    tr.ApplyToObjects(transform_objects),\n    tr.domain_randomization.ObjectsRandomPosition(\n        x_min=0, y_min=0.4, x_max=0.7, y_max=0.7, mode='percentage'\n    ),\n    tr.data_augmentation.Flip('x'),\n    tr.domain_randomization.Draw(),\n    tr.labeler.CreateBoundingBoxes(),\n    tr.io.CreateJson(out_dir=OUT_DIR, name='img_generate.jpg'),\n    tr.io.CreateJson(out_dir=OUT_DIR, name='json_generated.jpg')\n])\n\n## Execute transformations\nel = tr.Element(image=..., objects=...)\n[el] = transform(el)\n```\n\n![Object](https://github.com/linkedai/flip/blob/master/docs/images/generated.jpg)\n\n## Transformers \n\nThe main transformers are:\n\n- Transformer\n- Compose\n- ApplyToObjects\n- ApplyToBackground\n- ApplyToCreatedImage\n\nBy the way, all Transformers will be executed over objects of class Element and will return a new _transformed_ Element.\n\n### Data Augmentation\n\n- Flip: Flip the Element in x or y axis.\n- RandomResize: Change the size of an Element randomly.\n- Rotate: Rotate Element randomly.\n- Color: Change color space or the element color.\n- Brightness: Changes the brightness in the image.\n- Contrast: Changes the contrast in the image.\n- Saturation: Changes the saturation in the image.\n- Noise: Add noise to the element image.\n- CutOut: Remove a section of the element in the desired area.\n- RandomCrop: Cut the image randomly.\n\n### Random Domain\n\n- Draw: Draw objects over background Element to merge them into a new image.\n- ObjectsRandomPosition: Set Random positions to objects over background Element.\n\n### Labeler\n\n- CreateBoundingBoxes: Draw bounding boxes around the objects contained by a background Element.\n- CreateMasks: Creates the segmentation mask for the objects contained in a background element.\n\n### IO\n\n- SaveImage: Save a .jpg File with the new generated image.\n- SaveMask: Save a .jpg File with the new generated mask.\n- Json: Save generated Labels as a Json.\n- Csv: Save generated Labels as a CSV.\n\n\n## Want to Contribute or have any doubts or feedback?\n\nIf you want extra info, email me at flip@linkedai.co\n\n## Report Issues\n\nPlease help us by [reporting any issues](https://github.com/linkedai/flip/issues/new/choose) you may have while using Flip.\n\n## License\n\n* [Flip License](https://github.com/linkedai/flip/blob/master/LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flinkedai%2Fflip","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flinkedai%2Fflip","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flinkedai%2Fflip/lists"}