104 lines
2.8 KiB
ReStructuredText
104 lines
2.8 KiB
ReStructuredText
.. _lib_vision:
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Vision
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*******
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This module provides an interface for grabbing an rtmp stream and using the images to do some processing in opencv.
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How do I use this module?
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1. Get frames from the raspberry pi camera
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2. -- here comes your own processing --
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3. Publish the processed frames on an http server
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4. You can view the http stream of your processed images in a web browser
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Opencv Stream
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==============
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Because of the rtmp stream needing to buffer some frames and waiting for P-Frames, importing this module might take up
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to 5 Seconds.
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.. autoclass:: compLib.Vision.__Streaming
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:members:
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Examples
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=========
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Using the Vision Module
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-------------------------
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.. code-block:: python
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import cv2
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from compLib import Vision
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while True:
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# get newest opencv frame from camera
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frame = Vision.Streaming.get_frame()
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# do some processing with the frame.....
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# publish frame to streaming server
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Vision.Streaming.publish_frame(frame)
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Connect the raspberry pi to your internet and view the stream at: "http://your_raspi_ip:9898/". This should display
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your raspberry pi camera. Note: the stream will lag a little bit BUT the processing of the image will be done in
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realtime.
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The output on the website should show whatever your raspberry pi cam records:
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.. image:: images/opencv_http_stream.png
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:width: 680
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:alt: Processed frames from opencv
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Chessboard Detection
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------------------------------------------
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In this example we process the captured stream of images and want to detect chessboards. Run this example and
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point your raspberry pi camera to a chessboard and it should be detected.
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For testing you can point it at this image:
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.. image:: images/chessboard.jpg
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:width: 680
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:alt: Chessboard for opencv processing
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.. code-block:: python
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import cv2
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from compLib import Vision
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while True:
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# get newest opencv frame from camera
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frame = Vision.Streaming.get_frame()
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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# convert image to grayscale image
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# find the chessboard corners
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ret, corners = cv2.findChessboardCorners(gray, (6, 9), None)
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# draw detected chessboard position onto the image
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cv2.drawChessboardCorners(frame, (6, 9), corners, ret)
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# publish frame to streaming server
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Vision.Streaming.publish_frame(frame)
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Connect the raspberry pi to your internet and view the stream at: "http://your_raspi_ip:9898/".
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The output image should look like this:
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.. image:: images/chessboard_detected.jpg
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:width: 680
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:alt: Processed frames from opencv
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Here is a screenshot of the stream website while viewing the chessboard in this documentation.
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.. image:: images/opencv_processed.png
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:width: 680
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:alt: Processed frames from opencv
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