Demo Configuration file#
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 edge_ai_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
Inputs#
The input section defines a list of supported inputs like camera, filesrc etc. Their properties like shown below.
inputs:
input0: #Camera Input
source: /dev/video2 #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: ../data/videos/video_0000_h264.mp4 #Video file
format: h264 #File encoding format
width: 1280
height: 720
framerate: 25
input2: #Image Input
source: ../data/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.
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 UART console:
debian@beaglebone:/opt/edge_ai_apps# ./init_script.sh
USB Camera detected
device = /dev/video18
format = jpeg
CSI Camera 0 detected
device = /dev/video2
name = imx219 8-0010
format = [fmt:SRGGB8_1X8/1920x1080]
subdev_id = 2
isp_required = yes
IMX390 Camera 0 detected
device = /dev/video18
name = imx390 10-001a
format = [fmt:SRGGB12_1X12/1936x1100 field: none]
subdev_id = /dev/v4l-subdev7
isp_required = yes
ldc_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/video18), and 1 CSI camera is connected (/dev/video2) which is imx219 raw sensor and needs ISP. IMX390 camera needs both ISP and LDC.
Using this method, you can configure correct device for camera capture in the input section of config file.
input0:
source: /dev/video18 #USB Camera
format: jpeg #if connected USB camera supports jpeg
width: 1280
height: 720
framerate: 30
input1:
source: /dev/video2 #CSI Camera
format: auto #let the gstreamer negotiate the format
width: 1280
height: 720
framerate: 30
input2:
source: /dev/video2 #IMX219 raw sensor that needs ISP
format: rggb #ISP will be added in the pipeline
width: 1920
height: 1080
framerate: 30
subdev-id: 2 #needed by ISP to control sensor params via ioctls
input3:
source: /dev/video2 #IMX390 raw sensor that needs ISP
width: 1936
height: 1100
format: rggb12 #ISP will be added in the pipeline
subdev-id: 2 #needed by ISP to control sensor params via ioctls
framerate: 30
sen-id: imx390
ldc: True #LDC will be added in the pipeline
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.
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/edge_ai_apps/data/videos/video_0000_h264.mp4
and /opt/edge_ai_apps/data/videos/video_000_h265.mp4
input1:
source: ../data/videos/video_0000_h264.mp4
format: h264
width: 1280
height: 720
framerate: 25
input2:
source: ../data/videos/video_0000_h265.mp4
format: h265
width: 1280
height: 720
framerate: 25
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.
Image sources#
JPEG compressed images can be provided as inputs to the demos. A sample set of
images are provided under /opt/edge_ai_apps/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: ../data/images/%04d.jpg
width: 1280
height: 720
index: 0
framerate: 1
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
Note
Usually video streams from any IP camera will be encrypted and cannot be played back directly without a decryption key. We tested RTSP source by setting up an RTSP server on a Ubuntu 18.04 PC by referring to this writeup, Setting up RTSP server on PC
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: ../models/segmentation/ONR-SS-871-deeplabv3lite-mobv2-cocoseg21-512x512 #Model Directory
alpha: 0.4 #alpha for blending segmentation mask (optional)
model1:
model_path: ../models/detection/TFL-OD-202-ssdLite-mobDet-DSP-coco-320x320
viz_threshold: 0.3 #Visualization threshold for adding bounding boxes (optional)
model2:
model_path: ../models/classification/TVM-CL-338-mobileNetV2-qat
topN: 5 #Number of top N classes (optional)
Below are some of the use case specific properties:
alpha: This determines the weight of the mask for blending the semantic segmentation output with the input image
alpha * mask + (1 - alpha) * imageviz_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
The content of the model directory and its structure is discussed in detail in Import Custom Models
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
connector: 39 #Connector ID for kmssink (optional)
output1: #Video Output
sink: ../data/output/videos/output_video.mkv #Output video file
width: 1920
height: 1080
output2: #Image Output
sink: ../data/output/images/output_image_%04d.jpg #Image file name, printf style formatting is used
width: 1920
height: 1080
All supported outputs are listed in template config file. Below are the details of most commonly used outputs
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 outside docker container. The first number in each line is the connector-id which we will use in next step.
debian@beaglebone:/opt/edge_ai_apps# modetest -M tidss -c | grep connected
39 38 connected DP-1 530x300 12 38
48 0 disconnected HDMI-A-1 0x0 0 47
From above output, we can see that connector ID 39 is connected. Configure the connector ID in the output section of the config file.
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: ../data/output/videos/output_video.mkv
width: 1920
height: 1080
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: ../data/output/images/output_image_%04d.jpg
width: 1920
height: 1080
Flows#
The flows section defines how inputs, models and outputs are connected. Multiple flows can be defined to achieve multi input, multi inference like below.
flows:
flow0: #First Flow
input: input0 #Input for the Flow
models: [model1, model2] #List of models to be used
outputs: [output0, output0] #Outputs to be used for each model inference output
mosaic: #Positions to place the inference outputs in the output frame
mosaic0:
width: 800
height: 450
pos_x: 160
pos_y: 90
mosaic1:
width: 800
height: 450
pos_x: 960
pos_y: 90
flow1: #Second Flow
input: input1
models: [model0, model3]
outputs: [output0, output0]
mosaic:
mosaic0:
width: 800
height: 450
pos_x: 160
pos_y: 540
mosaic1:
width: 800
height: 450
pos_x: 960
pos_y: 540
Each flow should have exactly 1 input, n models to infer the given input and n outputs to render the output of each inference. 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).
Command line arguments#
Limited set of command line arguments can be provided, run with ‘-h’ or ‘–help’ option to list the supported parameters.
usage: Run : ./app_edgeai.py -h for help
positional arguments:
config Path to demo config file
ex: ./app_edgeai.py ../configs/app_config.yaml
optional arguments:
-h, --help show this help message and exit
-n, --no-curses Disable curses report
default: Disabled
-v, --verbose Verbose option to print profile info on stdout
default: Disabled
