{
    "info": {
        "author": "Jadiel de Armas",
        "author_email": "jadielam@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# Videoflow\n\n![Videoflow](assets/videoflow_logo_small.png)\n\n[![Build Status](https://travis-ci.org/videoflow/videoflow.svg?branch=master)](https://travis-ci.org/videoflow/videoflow)\n[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/videoflow/videoflow/blob/master/LICENSE)\n\n**Videoflow** is a Python framework for video stream processing. The library is designed to facilitate easy and quick definition of computer vision stream processing pipelines. It empowers developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code.  It contains off-the-shelf reference components for object detection, object tracking, human pose estimation, etc, and it is easy to extend with your own.\n\nThe complete documentation to the project is located in [**docs.videoflow.dev**](https://docs.videoflow.dev)\n\n[1.2]: http://i.imgur.com/wWzX9uB.png\n[1]: http://www.twitter.com/videoflow_py\n<!--Follow us on [![alt text][1.2]][1]-->\n\n## Installing the framework\n### Requirements\nBefore installing, be sure that you have `cv2` and `tensorflow >= 1.12` already installed. Python 2 is **NOT SUPPORTED**.  Requires Python 3.6+.  There are some [known issues](https://github.com/videoflow/videoflow/issues/56) to run it on Windows too\n\n### Installation\nYou can install directly using **pip** by doing `pip3 install videoflow`\n\nAlternatively, you can install by:\n\n1. Clone this repository\n2. Inside the repository folder, execute `pip3 install . --user`\n\n**Usage with docker**\n```bash\n# clone repo\ndocker build -t repo/videoflow:latest .\n# runs examples/object_detector.py by default\ndocker run -u $(id -u):$(id -g) -v $(pwd):/usr/src/app repo/videoflow\n# or mount the volume from your code directory  to /usr/src/app\ndocker run -u $(id -u):$(id -g) -v $(pwd):/usr/src/app repo/videoflow python /usr/src/app/yourown.py\n```\n## Contributing:\nA tentative [roadmap](ROADMAP.md) of where we are headed.\n\n[Contribution rules](CONTRIBUTING.md).\n\nIf you have new processors, producers or consumers that you can to create, check the [videoflow-contrib](https://github.com/videoflow/videoflow-contrib) project.  We want \nto keep videoflow succinct, clean, and simple, with as minimal dependencies to third-party libraries as necessaries. [videoflow-contrib](https://github.com/videoflow/videoflow-contrib) is better suited for adding new components that require new library \ndependencies.\n\n## Sample Videoflow application:\nBelow a sample videoflow application that detects automobiles in an intersection. For more examples see the [examples](examples/) folder. It uses detection model published by [tensorflow/models](https://github.com/tensorflow/models/tree/master/research/object_detection)\n\n[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/TYGMllb7fHM/0.jpg)](https://www.youtube.com/watch?v=TYGMllb7fHM)\n\n```python\nimport videoflow\nimport videoflow.core.flow as flow\nfrom videoflow.core.constants import BATCH\nfrom videoflow.consumers import VideofileWriter\nfrom videoflow.producers import VideofileReader\nfrom videoflow.processors.vision.detectors import TensorflowObjectDetector\nfrom videoflow.processors.vision.annotators import BoundingBoxAnnotator\nfrom videoflow.utils.downloader import get_file\n\nURL_VIDEO = \"https://github.com/videoflow/videoflow/releases/download/examples/intersection.mp4\"\n\nclass FrameIndexSplitter(videoflow.core.node.ProcessorNode):\n    def __init__(self):\n        super(FrameIndexSplitter, self).__init__()\n\n    def process(self, data):\n        index, frame = data\n        return frame\n\ninput_file = get_file(\"intersection.mp4\", URL_VIDEO)\noutput_file = \"output.avi\"\nreader = VideofileReader(input_file)\nframe = FrameIndexSplitter()(reader)\ndetector = TensorflowObjectDetector()(frame)\nannotator = BoundingBoxAnnotator()(frame, detector)\nwriter = VideofileWriter(output_file, fps = 30)(annotator)\nfl = flow.Flow([reader], [writer], flow_type = BATCH)\nfl.run()\nfl.join()\n```\n\nThe output of the application is an annotated video:\n\n\n## The Structure of a flow application\n\nA flow application usually consists of three parts:\n\n1. In the first part of the application you define a directed acyclic graph of computation nodes. There are 3 different kinds of nodes: producers, processors and consumers.  Producer nodes create data (commonly they will get the data from a source that is external to the flow).  Processors receive data as input and produce data as output. Consumers read data and do not produce any output.  You usually use a consumer when you want to write results to a log file, or when you want to push results to an external source (rest API, S3 bucket, etc.)\n\n2. To create a flow object, you need to pass to it your list of producers and your list of consumers. Once a flow is defined you can start it.  Starting the flow means that the producers start putting data into the flow and processors and consumers start receiving data.  Starting the flow also means allocating resources for producers, processors and consumers.  For simplicity for now we can say that each producer, processor and consumer will run on its own process space.\n\n3. Once the flow starts, you can also stop it.  When you stop the flow, it will happen organically.  Producers will stop producing data.  The rest of the nodes in the flow will continue running until the pipes run dry.  The resources used in the flow are deallocated progressively, as each node stops producing/processing/consuming data.\n\n\n",
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