5. Configuring applications¶
The demo config file uses YAML format to define input sources, models, outputs
and finally the flows which defines how everything is connected. Config files
for out-of-box demos are kept in edgeai-gst-apps/configs folder. The
folder contains config files for all the use cases and also multi-input and
multi-inference case. The folder also has a template YAML file
app_config_template.yaml which has detailed explanation of all the
parameters supported in the config file.
Config file is divided in 4 sections:
Inputs
Models
Outputs
Flows
5.1. Inputs¶
The input section defines a list of supported inputs like camera, video files etc. Their properties like shown below.
inputs:
input0: #Camera Input
source: /dev/video-usb-cam0 #Device file entry of the camera
format: jpeg #Input data format supported by camera
width: 1280 #Width and Height of the input
height: 720
framerate: 30 #Framerate of the source
input1: #Video Input
source: /opt/edgeai-test-data/videos/video0_1280_768.h264 #Video file
format: h264 #File encoding format
width: 1280
height: 768
framerate: 30
input2: #Image Input
source: /opt/edgeai-test-data/videos/images/%04d.jpg #Sequence of Image files, printf style formatting is used
width: 1280
height: 720
index: 0 #Starting Index (optional)
framerate: 1
All supported inputs are listed in template config file. Below are the details of most commonly used inputs.
5.1.1. Camera sources (v4l2)¶
v4l2src GStreamer element is used to capture frames from camera sources which are exposed as v4l2 devices. In Linux, there are many devices which are implemented as v4l2 devices. Not all of them will be camera devices. You need to make sure the correct device is configured for running the demo successfully.
init_script.sh is ran as part of systemd, which detects all cameras connected
and prints the detail like below in the console:
/opt/edgeai-gst-apps# ./init_script.sh
USB Camera detected
device = /dev/video-usb-cam0
format = jpeg
CSI Camera 0 detected
device = /dev/video-imx219-cam0
format = [fmt:SRGGB8_1X8/1920x1080]
subdev_id = /dev/v4l-imx219-subdev0
isp_required = yes
script can also be run manually later to get the camera details.
From the above log we can determine that 1 USB camera is connected (/dev/video-usb-cam0), and 1 CSI camera is connected (/dev/video-imx219-cam0) which is IMX219 raw sensor and needs ISP.
Using this method, you can configure correct device for camera capture in the input section of config file.
input0:
source: /dev/video-usb-cam0 #USB Camera
format: jpeg #if connected USB camera supports jpeg
width: 1280
height: 720
framerate: 30
input1:
source: /dev/video-imx219-cam0 #IMX219 raw sensor that needs ISP
format: rggb #ISP will be added in the pipeline
width: 1920
height: 1080
framerate: 30
subdev-id: /dev/v4l-imx219-subdev0 #needed by ISP to control sensor params via ioctls
Make sure to configure correct format for camera input. jpeg for USB
camera that supports MJPEG (Ex. C270 logitech USB camera). auto for CSI
camera to allow GStreamer to negotiate the format. rggb for sensor
that needs ISP.
5.1.2. Video sources¶
H.264 and H.265 encoded videos can be provided as input sources to the demos.
Sample video files are provided under /opt/edgeai-test-data/videos/
input1:
source: /opt/edgeai-test-data/videos/video0_1280_768.h264
format: h264
width: 1280
height: 768
framerate: 30
input2:
source: /opt/edgeai-test-data/videos/video0_1920_1088.h264
format: h264
width: 1920
height: 1088
framerate: 30
Make sure to configure correct format for video input as shown above.
By default the format is set to auto which will then use the GStreamer
bin decodebin instead.
5.1.3. Image sources¶
JPEG compressed images can be provided as inputs to the demos. A sample set of
images are provided under /opt/edgeai-test-data/images. The names of the
files are numbered sequentially and incrementally and the demo plays the files
at the fps specified by the user.
input2:
source: /opt/edgeai-test-data/images/%04d.jpg
width: 1280
height: 720
index: 0
framerate: 1
5.1.4. RTSP sources¶
H.264 encoded video streams either coming from a RTSP compliant IP camera or via RTSP server running on a remote PC can be provided as inputs to the demo.
input0:
source: rtsp://172.24.145.220:8554/test # rtsp stream url, replace this with correct url
width: 1280
height: 720
framerate: 30
5.2. Models¶
The model section defines a list of models that are used in the demo. Path to the model directory is a required argument for each model and rest are optional properties specific to given use cases like shown below.
models:
model0:
model_path: /opt/model_zoo/TFL-OD-2020-ssdLite-mobDet-DSP-coco-320x320 #Model Directory
viz_threshold: 0.6 #Visualization threshold for adding bounding boxes (optional)
model1:
model_path: /opt/model_zoo/ONR-CL-6360-regNetx-200mf
topN: 5 #Number of top N classes (optional)
model2:
model_path: /opt/model_zoo/ONR-SS-8610-deeplabv3lite-mobv2-ade20k32-512x512
alpha: 0.4 #alpha for blending segmentation mask (optional)
Below are some of the use case specific properties:
viz_threshold: Score threshold to draw the bounding boxes for detected objects in object detection. This can be used to control the number of boxes in the output, increase if there are too many and decrease if there are very few
topN: Number of most probable classes to overlay on image classification output
alpha: This determines the weight of the mask for blending the semantic segmentation output with the input image
alpha * mask + (1 - alpha) * image
The content of the model directory and its structure is discussed in detail in Import Custom Models
5.3. Outputs¶
The output section defines a list of supported outputs.
outputs:
output0: #Display Output
sink: kmssink
width: 1920 #Width and Height of the output
height: 1080
overlay-perf-type: graph #Overlay performance stat (graph or text default:No overlay)
connector: 39 #Connector ID for kmssink (optional)
output1: #Video Output
sink: /opt/edgeai-test-data/outputs/output_video.mkv #Output video file
width: 1920
height: 1080
output2: #Image Output
sink: /opt/edgeai-test-data/outputs/output_image_%04d.jpg #Image file name, printf style formatting is used
width: 1920
height: 1080
output3:
sink: remote #Publish output to udp port as jpeg encoded frames
width: 1920
height: 1080
port: 8081 #udp port (default:8081)
host: 127.0.0.1 #udp host (default:0.0.0.0)
encoding: jpeg #encoding type (jpeg or mp4)
All supported outputs are listed in template config file. Below are the details of most commonly used outputs
5.3.1. Display sink (kmssink)¶
When you have only one display connected to the SK, kmssink will try to use it for displaying the output buffers. In case you have connected multiple display monitors (e.g. Display Port and HDMI), you can select a specific display for kmssink by passing a specific connector ID number. Following command finds out the connected displays available to use.
Note: Run this command to check which display is connected. The first number in each line is the connector-id to be used in the next step.
/opt/edgeai-gst-apps# modetest -M tidss -c | grep connected
39 38 connected DP-1 530x300 12 38
48 0 disconnected HDMI-A-1 0x0 0 47
Configure the required connector ID in the output section of the config file.
5.3.2. Video sinks¶
The post-processed outputs can be encoded in H.264 format and stored on disk. Please specify the location of the video file in the configuration file.
output1:
sink: /opt/edgeai-test-data/outputs/output_video.mkv #(.mkv or .mp4 or .mov)
width: 1920
height: 1080
5.3.3. Image sinks¶
The post-processed outputs can be stored as JPEG compressed images. Please specify the location of the image files in the configuration file. The images will be named sequentially and incrementally as shown.
output2:
sink: /opt/edgeai-test-data/outputs/output_image_%04d.jpg
width: 1920
height: 1080
5.3.4. Remote sinks¶
Post-processed frames can be encoded as jpeg or h264 frames and send as udp packets to a port. Please specify the sink as remote in the configuration file. The udp port and host to send packets to can be defined. If not, default port is 8081 and host is 127.0.0.1.
output3:
sink: remote
width: 1920
height: 1080
port: 8081
host: 127.0.0.1
encoding: jpeg #(jpeg or h264)
A NodeJS server is provided under /opt/edgeai-gst-apps/scripts/remote_streaming
which establishes a node server on the target and listens to the udp port (8081)
on localhost (127.0.0.1) and can be used to view the frames remotely.
/opt/edgeai-gst-apps# node scripts/remote_streaming/server.js
5.4. Flows¶
The flows section defines how inputs, models and outputs are connected. Multiple flows can be defined to achieve multi input, multi inference as shown
Note
The format of specifying flows is changed as of 08.05.00 release to enable multiple outputs in the same sub-flow The older config files may not be compatible from this release onwards and should be changed to below format
flows:
# flowname : [input,mode1,output,[mosaic_pos_x,mosaic_pos_y,width,height]]
flow0: [input0,model0,output0,[160,90,800,450]]
flow1: [input0,model1,output0,[960,90,800,450]]
flow2: [input1,model2,output0,[160,540,800,450]]
flow3: [input1,model3,output0,[960,540,800,450]]
Each flow defined here has exactly 1 input and 1 model. If multiple flows have same input, they are clubbed together internally in the application for optimization. Along with input, models and outputs it is required to define n mosaics which are the position of the inference output in the final output plane. This is needed because multiple inference outputs can be rendered to same output (Ex: Display).
5.5. GStreamer plugins¶
The edgeai-gst-apps essentially constructs GStreamer pipelines for dataflow.
This pipeline is constructed optimally and dynamically based on a pool of
specific plugins available on the platform. The defined pool of plugins for
different platform can be found in edgeai-gst-apps/configs/gst_plugin_maps.yaml
file.
This file contains the plugin used for certain task and the property of plugin (if applicable).
5.5.1. Default GStreamer plugins map for AM69A¶
<soc-type>:
dlcolorconvert:
element: tiovxdlcolorconvert
property:
out-pool-size: 4
colorconvert:
element: tiovxcolorconvert
property:
target: [0,1]
out-pool-size: 4
scaler:
element: tiovxmultiscaler
property:
target: [0,1] #[MSC targets to balance loads across]
dlpreproc:
element: tiovxdlpreproc
property:
out-pool-size: 4
mosaic:
element: tiovxmosaic
isp:
element: tiovxisp
ldc:
element: tiovxldc
h264dec:
element: v4l2h264dec
property:
capture-io-mode: 5 #[setting the mode for decoder]
h265dec:
element: v4l2h265dec
h264enc:
element: v4l2h264enc
h265enc: null
jpegenc:
element: jpegenc
inferer:
target: dsp #[dsp for c7x offload, arm for no offload]
core-id: [1] #[specify list of c7x cores to offload models]