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WebGear_RTC_RTC Examples

 

Using WebGear_RTC with RaspberryPi Camera Module

Because of WebGear_RTC API's flexible internal wapper around VideoGear, it can easily access any parameter of CamGear and PiGear videocapture APIs.

Following usage examples are just an idea of what can be done with WebGear_RTC API, you can try various VideoGear, CamGear and PiGear parameters directly in WebGear_RTC API in the similar manner.

Here's a bare-minimum example of using WebGear_RTC API with the Raspberry Pi camera module while tweaking its various properties in just one-liner:

Backend PiGear API now fully supports the newer picamera2 python library under the hood for Raspberry Pi camera modules. Follow this guide ➶ for its installation.

Make sure to complete Raspberry Pi Camera Hardware-specific settings prior using this backend, otherwise nothing will work.

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# import libs
import uvicorn
from libcamera import Transform
from vidgear.gears.asyncio import WebGear_RTC

# various WebGear_RTC performance 
# and Picamera2 API tweaks
options = {
    "frame_size_reduction": 25,
    "queue": True,
    "buffer_count": 4,
    "controls": {"Brightness": 0.5, "ExposureValue": 2.0},
    "transform": Transform(hflip=1),
    "auto_align_output_config": True,  # auto-align camera configuration
}

# initialize WebGear app
web = WebGear_RTC(
    enablePiCamera=True, resolution=(640, 480), framerate=60, logging=True, **options
)

# run this app on Uvicorn server at address http://localhost:8000/
uvicorn.run(web(), host="localhost", port=8000)

# close app safely
web.shutdown()
Under the hood, Backend PiGear API (version 0.3.3 onwards) prioritizes the new picamera2 API backend.

However, the API seamlessly switches to the legacy picamera backend, if the picamera2 library is unavailable or not installed.

It is advised to enable logging(logging=True) to see which backend is being used.

The picamera library is built on the legacy camera stack that is NOT (and never has been) supported on 64-bit OS builds.

You could also enforce the legacy picamera API backend in PiGear by using the enforce_legacy_picamera user-defined optional parameter boolean attribute.

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# import libs
import uvicorn
from vidgear.gears.asyncio import WebGear_RTC

# various WebGear_RTC performance and Picamera API tweaks
options = {
    "frame_size_reduction": 25,
    "hflip": True,
    "exposure_mode": "auto",
    "iso": 800,
    "exposure_compensation": 15,
    "awb_mode": "horizon",
    "sensor_mode": 0,
}

# initialize WebGear app
web = WebGear_RTC(
    enablePiCamera=True, resolution=(640, 480), framerate=60, logging=True, **options
)

# run this app on Uvicorn server at address http://localhost:8000/
uvicorn.run(web(), host="localhost", port=8000)

# close app safely
web.shutdown()

 

Using WebGear_RTC with real-time Video Stabilization enabled

Here's an example of using WebGear_RTC API with real-time Video Stabilization enabled:

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# import libs
import uvicorn
from vidgear.gears.asyncio import WebGear_RTC

# various webgear_rtc performance tweaks
options = {
    "frame_size_reduction": 25,
}

# initialize WebGear_RTC app  with a raw source and enable video stabilization(`stabilize=True`)
web = WebGear_RTC(source="foo.mp4", stabilize=True, logging=True, **options)

# run this app on Uvicorn server at address http://localhost:8000/
uvicorn.run(web(), host="localhost", port=8000)

# close app safely
web.shutdown()

 

Display Two Sources Simultaneously in WebGear_RTC

In this example, we'll be displaying two video feeds side-by-side simultaneously on browser using WebGear_RTC API by simply concatenating frames in real-time:

New in v0.2.4

This example was added in v0.2.4.

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# import necessary libs
import uvicorn, cv2
import numpy as np
from vidgear.gears.helper import reducer
from vidgear.gears.asyncio import WebGear_RTC

# initialize WebGear_RTC app without any source
web = WebGear_RTC(logging=True)

# frame concatenator
def get_conc_frame(frame1, frame2):
    h1, w1 = frame1.shape[:2]
    h2, w2 = frame2.shape[:2]

    # create empty matrix
    vis = np.zeros((max(h1, h2), w1 + w2, 3), np.uint8)

    # combine 2 frames
    vis[:h1, :w1, :3] = frame1
    vis[:h2, w1 : w1 + w2, :3] = frame2

    return vis


# create your own custom streaming class
class Custom_Stream_Class:
    """
    Custom Streaming using two OpenCV sources
    """

    def __init__(self, source1=None, source2=None):

        # !!! define your own video source here !!!
        # check is source are provided
        if source1 is None or source2 is None:
            raise ValueError("Provide both source")

        # initialize global params
        # define both source here
        self.stream1 = cv2.VideoCapture(source1)
        self.stream2 = cv2.VideoCapture(source2)

        # define running flag
        self.running = True

    def read(self):

        # don't forget this function!!!

        # check if sources were initialized or not
        if self.stream1 is None or self.stream2 is None:
            return None

        # check if we're still running
        if self.running:
            # read video frame
            (grabbed1, frame1) = self.stream1.read()
            (grabbed2, frame2) = self.stream2.read()

            # if NoneType
            if not grabbed1 or not grabbed2:

                # do something with your OpenCV frame here

                # concatenate frame
                frame = get_conc_frame(frame1, frame2)

                # reducer frames size if you want more performance otherwise comment this line
                # frame = await reducer(frame, percentage=30)  # reduce frame by 30%

                # return our gray frame
                return frame
            else:
                # signal we're not running now
                self.running = False
        # return None-type
        return None

    def stop(self):

        # don't forget this function!!!

        # flag that we're not running
        self.running = False
        # close stream
        if not (self.stream1 is None):
            self.stream1.release()
            self.stream1 = None

        if not (self.stream2 is None):
            self.stream2.release()
            self.stream2 = None


# assign your Custom Streaming Class with adequate two sources
# to `custom_stream` attribute in options parameter
options = {
    "custom_stream": Custom_Stream_Class(
        source1="foo1.mp4", source2="foo2.mp4"
    )
}

# initialize WebGear_RTC app without any source
web = WebGear_RTC(logging=True, **options)

# run this app on Uvicorn server at address http://localhost:8000/
uvicorn.run(web(), host="localhost", port=8000)

# close app safely
web.shutdown()

On successfully running this code, the output stream will be displayed at address http://localhost:8000/ in Browser.