Metadata-Version: 2.1
Name: edgeimpulse
Version: 0.0.7
Summary: Python runner for real-time ML classification
Home-page: https://github.com/pypa/sampleprojecthttps://github.com/edgeimpulse/edgeimpulse/edit/linux-node-sdk/linux/python
Author: EdgeImpulse
Author-email: mauricio@edgeimpulse.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/edgeimpulse/edgeimpulse/issues
Description: # Edge Impulse Linux SDK for Python
        
        This library lets you run machine learning models and collect sensor data on Linux machines using Python. This SDK is part of [Edge Impulse](https://www.edgeimpulse.com) where we enable developers to create the next generation of intelligent device solutions with embedded machine learning. [Start here to learn more and train your first model](https://docs.edgeimpulse.com).
        
        
        `pip install edgeimpulse`
        
        ## runner.py
        
        Implements the `ImpulseRunner`
        
        ## use:
        
        ```
        from edgeimpulse.runner import ImpulseRunner
        import signal
        runner = None
        
        def signal_handler(sig, frame):
            print('Interrupted')
            if (runner):
                runner.stop()
            sys.exit(0)
        
        signal.signal(signal.SIGINT, signal_handler)
        
        ...
        runner = ImpulseRunner(modelfile)
        model_info = runner.init()
        ...
        res = runner.classify(features[:window_size].tolist())
        ```
        
        
        ## Classify from microphone in real-time
        
        ```
        from edgeimpulse.audio import AudioImpulseRunner
        ...
        runner = AudioImpulseRunner('/path/to/your/model')
        runner.init()
        classifier = runner.classify()
        for res in classifier:
            print(res)
        ```
        
        ## Classify from camera in real-time
        
        ```
        from edgeimpulse.camera import CameraImpulseRunner
        import cv2
        ...
        runner = CameraImpulseRunner('/path/to/your/model')
        runner.init()
        classifier = runner.classify()
        for res, img in classifier:
            print(res)
            cv2.imshow('frame',img)
        ```
        
        
        ## examples:
        
        ```
        /camera
        /microphone
        ```
        
        
        ### camera
        Classifies frames grabbed directly from the webcam.
        
        ### microphone
        Classifies audio acquired directly from the audio interface.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
