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        "description": "# purrsong\n\n\n## Installation\n```console\npip install purrsong\n```\n\n## Start\n```python\nimport purrsong as ps\nps.__version__\n>>> 0.1.6\n```\n\n## Create Anaconda Environment(Optional)\n```console\nconda create -n purrsong python=3.6\nconda activate purrsong\n```\n\n## Requirments\n* tensorflow\n* opencv-python\n* tqdm\n* requests\n* pandas\n* matplotlib\n\n## Auto-download-extract-load of datasets, modelsets\nBelow function automatically download data or models and save locally\nIf data is already exists, returns data directory or model filepath\n\n```python\nimport purrsong as ps\n\nmodel_list = ps.list_models() # or ps.list_models(fresh=True)\nprint(model_list)\nps.load_model('bbs') # or ps.load_model('bbs', fresh=True)\n\ndataset_list = ps.list_datasets() # or ps.list_datasets(fresh=True)\nprint(dataset_list)\nps.load_dataset('cat') # or ps.load_dataset('cat', fresh=True)\n```\n\n## Manipulating cats dataset\nYou can play with auto downloaded `cats` dataset by below example.\nTry changing `factor` arg, which will return different size of bounding boxes.\n\n#### load cats dataset\n```python\nimport purrsong as ps\nimport matplotlib.pyplot as plt\ncats = ps.load_cats()\n```\n\n#### showing cat image\n```python\nimg = cats[0]['image']\nplt.imshow(img)\nplt.show()\n```\n\n#### showing cat image with landmark\n```python\nimg = cats[0]['image']\nlmk = cats[0]['landmark']\nx, y = lmk.T\nplt.imshow(img)\nplt.scatter(x, y)\nplt.show()\n```\n\n#### showing cat face image\n```python\nimg = cats.face_img(44)  # or img = cats.face_img(idx=44, factor=1.7)\nplt.imshow(img)\nplt.show()\n```\n\n#### showing cat left eye image\n```python\nimg = cats.left_eye_img(44)  # or img = cats.face_img(idx=44, factor=1.7)\nplt.imshow(img)\nplt.show()\n```\n\n#### available data features\n```python\nimport purrsong as ps\ncats = ps.load_cats()\n\ncat = cats(0)         # or cats(idx=0, factor=1.7)\n\ncat['image']          # cats.image(0)\ncat['landmark']       # cats.landmark(0)\ncat['face']           # cats.face(0, factor=1.7)\ncat['face_bb']        # cats.face_bb(0, factor=1.7)\ncat['face_img']       # cats.face_img(0, factor=1.7)\ncat['face_lmk']       # cats.face_lmk(0, factor=1.7)\ncat['eye']            # cats.eye(0, factor=1.7)\ncat['left_eye_bb']    # cats.left_eye_bb(0, factor=1.7)\ncat['left_eye_img']   # cats.left_eye_img(0, factor=1.7)\ncat['right_eye_bb']   # cats.right_eye_bb(0, factor=1.7)\ncat['right_eye_img']  # cats.right_eye_img(0, factor=1.7)\ncat['nose']           # cats.nose(0, factor=1.7)\ncat['nose_bb']        # cats.nose_bb(0, factor=1.7)\ncat['nose_img']       # cats.nose_img(0, factor=1.7)\ncat['ear']            # cats.ear(0, factor=1.7)\ncat['left_ear_bb']    # cats.left_ear_bb(0, factor=1.7)\ncat['left_ear_img']   # cats.left_ear_img(0, factor=1.7)\ncat['right_ear_bb']   # cats.right_ear_bb(0, factor=1.7)\ncat['right_ear_img']  # cats.right_ear_img(0, factor=1.7)\n```\nleft dict form is much more intuitive and good \nwhen you have to handle many of features at the same time.  \nright method way is good when you access specific feature.\n\n\n\n",
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