Raspberry Camera API: Difference between revisions

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I didn't succeed in using the converted formats, maybe because they are based on the BA81 format.
I didn't succeed in using the converted formats, maybe because they are based on the BA81 format.
Format 'BG10' works for both cameras and is easily converted to RGB format using OpenCV (https://docs.opencv.org/3.4/db/d64/tutorial_js_colorspaces.html).
Format 'BG10' works for both cameras and is easily converted to RGB format using OpenCV (https://docs.opencv.org/3.4/db/d64/tutorial_js_colorspaces.html).
==Raspicam - old version of operating system (OLD) ==
NB! old info - rest of page <===================================
To be used from C++ direct or through openCV.
A rather short raspberry pi camera API is available from https://www.uco.es/investiga/grupos/ava/node/40.
And can be downloaded from https://sourceforge.net/projects/raspicam/files/,
or get the file from sourceforge with no GUI, with (there may be newer versions):
cd ~/Downloads
wget --no-check-certificate -O raspicam-0.1.9.zip https://downloads.sourceforge.net/project/raspicam/raspicam-0.1.9.zip?r=https%3A%2F%2Fsourceforge.net%2Fprojects%2Fraspicam%2F%3Fsource%3Dtyp_redirect&ts=1486483484&use_mirror=netix
Once fetched and available on the raspberry, then
Unpack and install:
unzip raspicam-0.1.9.zip
cd raspicam-0.1.9
mkdir build
cd build
cmake ..
make -j3
sudo make install
sudo ldconfig
The library is installed in /usr/local, and therefore to make cmake find it the path needs to be added to startup configuration, in file ~/.bashrc
cd
nano ~/.bashrc
add at the end:
export CMAKE_PREFIX_PATH=/usr/local/lib
source ~/.bashrc  # to implement export
Test with openCV
raspicam_cv_test
That takes 100 images and saves 3.
=== Camera streamer ===
To download the project you will need a source control system called git. It may not be installed on a fresh image. I know it’s not on the lite image. So you may need to install it.
sudo apt-get install git
Now that you have git installed, use it to clone a copy of the mjpg-streamer to your Pi.
git clone https://github.com/jacksonliam/mjpg-streamer.git
After the cloned copy of the mjpeg-stream has been coppied to the raspberry-pi, follow these steps.
cd mjpg-streamer/mjpg-streamer-experimental/
make -j4    # optional CMAKE_BUILD_TYPE=Debug
sudo make install
Make a script to start the streamer
cd
nano start-stream.sh
copy this into the file (uncomment only one line - the others are examples for other resolutions - edit as desired)
#!/bin/bash
#mjpg_streamer -i "input_raspicam.so -y 1600 -x 1200 -fps 5 -rot 90" -o output_http.so
mjpg_streamer -i "input_raspicam.so -y 972 -x 1296 -fps 3 -rot=0" -o output_http.so
#mjpg_streamer -i "input_raspicam.so -y 600 -x 800 -fps 3 -rot=0" -o output_http.so
#mjpg_streamer -i "input_raspicam.so -y 640 -x 480 -fps 5" -o output_http.so
Change the file to be executable
chmod +x start-stream.sh
=== Start streaming at boot ===
Start of applications can be controlled by the /etc/rc.local script:
nano /etc/rc.local
add the following lines before 'exit 0' (close to the end)
# start camera streaming
su - local /home/local/start-stream.sh &
Copy and 'shift-ctrl-v' will do
Comment the last line (su - local /home/local/start-stream.sh &) if camera streaming should not start.
to stop streaming use (if started)
pkill mjpg_streamer
=== Watch camera stream ===
When the robot is on a net connection - cable or wifi - you can watch the stream from a browser
for streaming:
HTTP://jasmin.local:8080/?action=stream
or for a snapshot
HTTP://jasmin.local:8080/?action=snapshot
where jasmin should be replaced with the robot name, or jasmin.local can be replaced with the IP address.

Revision as of 12:46, 25 December 2021

Back to Robobot

Camera use

Raspberry pi operating system version "bullseye" uses a new camera library.

The info is taken from https://www.raspberrypi.com/documentation/accessories/camera.html#libcamera-and-libcamera-apps?ref=https://githubhelp.com

LibCamera

If not already installed, install with 

sudo apt install libcamera-apps

It should not be needed to install libcamera-dev

Camera streaming

The libcamera library can do streaming out of the box with the command: 

libcamera-vid -n -t 0 --inline --listen --rotation 0 --framerate 30 --width 1920 --height 1080 -o tcp://0.0.0.0:8888

It will listen to port 8888 for TCP connections on the localhost.

From another PC the stream can be displayed by VLC, (or the same parameter in the VLC "media" -> "open network stream") 

vlc tcp/h264://192.168.2.14:8888

if 192.168.2.14 is the IP of the Raspberry pi. The streaming will terminate when the VLC stops the connection.

A start-script is placed in the "local" home folder. 

./stream_cam.sh

with examples of different solutions.

camera from OpenCV

The raspberry cameras seem to prefer 10 bit/pixel in Bayer RGGB format - other formats are not consistent for both new and old camera.

Format check

Install package v4l-utils (also installed by default) 

sudo apt install v4l-utils

The supported image formats from both cameras are: 

v4l2-ctl -d/dev/video0 --list-formats

ioctl: VIDIOC_ENUM_FMT
       Type: Video Capture
       [0]: 'BA81' (8-bit Bayer BGBG/GRGR)
       [1]: 'pBAA' (10-bit Bayer BGBG/GRGR Packed)
       [2]: 'BG10' (10-bit Bayer BGBG/GRGR)

But formats can be converted to other formats, visible as other video devices e.g /dev/video12. 

v4l2-ctl -d/dev/video12 --list-formats

ioctl: VIDIOC_ENUM_FMT
       Type: Video Capture Multiplanar
       [0]: 'YUYV' (YUYV 4:2:2)
       [1]: 'YVYU' (YVYU 4:2:2)
       [2]: 'VYUY' (VYUY 4:2:2)
       [3]: 'UYVY' (UYVY 4:2:2)
       [4]: 'YU12' (Planar YUV 4:2:0)
       [5]: 'YV12' (Planar YVU 4:2:0)
       [6]: 'RGB3' (24-bit RGB 8-8-8)
       [7]: 'BGR3' (24-bit BGR 8-8-8)
       [8]: 'BGR4' (32-bit BGRA/X 8-8-8-8)
       [9]: 'RGBP' (16-bit RGB 5-6-5)
       [10]: 'NV12' (Y/CbCr 4:2:0)
       [11]: 'NV21' (Y/CrCb 4:2:0)

Format BA81 did not give a proper result for both cameras. I didn't succeed in using the converted formats, maybe because they are based on the BA81 format. Format 'BG10' works for both cameras and is easily converted to RGB format using OpenCV (https://docs.opencv.org/3.4/db/d64/tutorial_js_colorspaces.html).

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