Decoding Network Streams¶

Similar to decoding Video files, DeFFcode's FFdecoder API directly supports Network Streams with specific protocols (such as RTSP/RTP, HTTP(s), MPEG-TS, etc.) as input to its source parameter.

We'll discuss Network Streams support briefly in the following recipes:

DeFFcode APIs requires FFmpeg executable

DeFFcode APIs MUST requires valid FFmpeg executable for all of its core functionality, and any failure in detection will raise RuntimeError immediately. Follow dedicated FFmpeg Installation doc ➶ for its installation.

Additional Python Dependencies for following recipes

Following recipes requires additional python dependencies which can be installed easily as below:

OpenCV: OpenCV is required for previewing video frames. You can easily install it directly via pip:

OpenCV installation from source

You can also follow online tutorials for building & installing OpenCV on Windows, Linux, MacOS and Raspberry Pi machines manually from its source.

Make sure not to install both pip and source version together. Otherwise installation will fail to work!

Other OpenCV binaries

OpenCV maintainers also provide additional binaries via pip that contains both main modules and contrib/extra modules opencv-contrib-python, and for server (headless) environments like opencv-python-headless and opencv-contrib-python-headless. You can also install any one of them in similar manner. More information can be found here.
```
pip install opencv-python       
```

Always use FFdecoder API's terminate() method at the end to avoid undesired behavior.

Never name your python script deffcode.py

When trying out these recipes, never name your python script deffcode.py otherwise it will result in ModuleNotFound error.

Capturing and Previewing frames from a HTTPs Stream¶

In this example we will decode live BGR24 video frames from a HTTPs protocol Stream in FFdecoder API, and preview them using OpenCV Library's cv2.imshow() method.

# import the necessary packages
from deffcode import FFdecoder
import cv2

# initialize and formulate the decoder for BGR24 pixel format output
decoder = FFdecoder("https://abhitronix.github.io/html/Big_Buck_Bunny_1080_10s_1MB.mp4", frame_format="bgr24").formulate()

# grab the BGR24 frames from decoder
for frame in decoder.generateFrame():

    # check if frame is None
    if frame is None:
        break

    # {do something with the frame here}

    # Show output window
    cv2.imshow("Output", frame)

    # check for 'q' key if pressed
    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        break

# close output window
cv2.destroyAllWindows()

# terminate the decoder
decoder.terminate()

Capturing and Previewing frames from a RTSP/RTP Stream¶

In this example we will decode live BGR24 video frames from RTSP/RTP protocol Streams in FFdecoder API, and preview them using OpenCV Library's cv2.imshow() method.

This example assume you already have a RSTP Server running at specified RSTP address with syntax rtsp://[RTSP_ADDRESS]:[RTSP_PORT]/[RTSP_PATH] and video data already being published to it.

For creating your own RSTP Server locally and publishing video data to it, You can refer this WriteGear API's bonus example ➶

Make sure to change RSTP address rtsp://localhost:8554/mystream with yours in following code before running

# import the necessary packages
from deffcode import FFdecoder
import cv2

# define suitable parameters
ffparams = {"-rtsp_transport": "tcp"}

# initialize and formulate the decoder with RTSP protocol source for BGR24 output
# [WARNING] Change your RSTP address `rtsp://localhost:8554/mystream` with yours!
decoder = FFdecoder("rtsp://localhost:8554/mystream", frame_format="bgr24", verbose=True, **ffparams).formulate()

# grab the BGR24 frames from decoder
for frame in decoder.generateFrame():

    # check if frame is None
    if frame is None:
        break

    # {do something with the frame here}

    # Show output window
    cv2.imshow("Output", frame)

    # check for 'q' key if pressed
    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        break

# close output window
cv2.destroyAllWindows()

# terminate the decoder
decoder.terminate()