Deepomatic CLI - Run inferences

Each model version is deployed as a web API after it has been trained. To run some inferences and evaluate the performances of your trained model version, you can use Deepomatic CLI.

Installation & Specification Parameters

Installation

See the link below to install Deepomatic CLI

Get you Model Version ID and your Routing Key

To retrieve the MODEL_VERSION_ID of the model version that you want to use within your app and the corresponding Routing Key, you need to go on the Apps tab in the Deployment section and click on the app version that is deployed on your site.

The MODEL_VERSION_ID is specified with the -r or --recognition_id argument.

The ROUTING_KEY corresponds to the rabbitMQ queue identifier.

You will need to provide one additional parameters to run model actions: the AMPQ_URL. It corresponds to the rabbitMQ address. You will find this address in your server deployment files. It should look like amqp://user:pwd@ip:port/vhost

This parameter will be defined during the GPU server setup. Besides, you don't need to specify any credentials.

Sample commands

Drawing a prediction with on-premises Server
deepo infer -i img.jpg -o pred.json -r 123 -k queue -u amqp://user:pwd@ip:port/vho

Model actions

There are four different model actions:

  • infer: Compute predictions only.

  • draw: Display the prediction result, whether tags or the bounding boxes.

  • blur: Blur the bounding boxes.

  • noop: Benchmark input and output processing capabilities.

They follow the same recipe:

  1. Retrieve one or several inputs.

  2. Compute predictions using the trained neural network

  3. Output the result in different formats: image, video, JSON, stream, etc.

Generic command
deepo infer -i myinput -o myoutput1 myoutput2 ...

Input

Input types

The Deepomatic CLI supports different types of input:

  • Image: Supported formats include bmp, jpeg, jpg , jpe,png, tif and tiff.

  • Video: Supported formats include avi, mp4, webm and mjpg.

  • Studio JSON: Deepomatic Studio JSON format, used to specify several images or videos.

Sample input JSON format
{
"images": [
{
"location": "/path/to/img.jpg"
},
{
"location": "/path/to/video.mp4"
},
]
}
  • Directory: Analyse all images and videos found in the directory.

  • Digit: Retrieve the stream from the corresponding device. For instance, 0 for the installed webcam.

  • Network stream: Supported network streams include rtsp, http and https.

Specify input

Inputs are specified using the -i for input option. Below an example with each type of inputs.

Sample input commands
deepo infer -i /path/to/my_img.bmp ... # Image
deepo infer -i /path/to/my_vid.mp4 ... # Video
deepo infer -i /path/to/my_studio.json ... # Studio JSON
deepo infer -i /path/to/my_dir ... # Directory
deepo infer -i 0 ... # Device number
deepo infer -i rtsp://ip:port/channel ... # RTSP stream

Output

Output types

The Deepomatic CLI supports different types of output:

  • Image: Supported formats include bmp, jpeg, jpg , jpe,png, tif and tiff.

  • Video: Supported formats include avi and mp4.

  • Run JSON: Deepomatic Run JSON format for raw predictions.

  • Studio JSON: Deepomatic Studio JSON format for Studio-like prediction scores. This is specified using the -s or --studio_format option.

  • Integer wildcard JSON: A standard Run/Studio JSON, except that the name contains the frame number. For instance -o frame %03d.json will output frame001.json, frame002.json, ...

  • String wildcard JSON: Same as the integer wildcard except this time the frame name is used. For instance -o pred_%s.jpg will output pred_img1_123.json, pred_img2_123.json, ...

  • Standard output: On rare cases you might want to output the model results directly to the process standard output using the stdout option. For instance this allows you to stream directly to vlc.

  • Display output: Opens a window and displays the result. Quit with q.

Specify output

Output are specified using the -o for output option. Below an example with each type of inputs.

Please note that in order to avoid duplicate computations, it is possible to specify several outputs at the same time, for instance to blur an image and store the predictions.

Sample output commands
deepo draw -i img.jpg -o img_drawn.jpg ... # Image
deepo draw -i vid.mp4 -o img_drawn_%04d.jpg ... # Wildcard images
deepo draw -i vid.mp4 -o vid_drawn.mp4 ... # Video
deepo draw -i img.jpg -o pred.json ... # Run JSON
deepo draw -i img.jpg -o pred.json -s ... # Studio JSON
deepo draw -i vid.mp4 -o pred_%s.json ... # String wildcard JSON
deepo draw -i vid.mp4 -o pred_%04d.json ... # Integer wildcard JSON
deepo draw -i vid.mp4 -o stdout ... # Standard output
deepo draw -i vid.mp4 -o window ... # Display output
deepo draw -i vid.mp4 -o vid_drawn.mp4 pred_%04d.json ... # Multiple outputs

Options

Commands have additional options that you can use with a flag. There is a short flag -f and a long flag --flag. Note that one use a simple - while the other uses two --. Also some options need an additional argument. Find below the option table. When indicated, all means that all four commands infer, draw , blur and noop are concerned.

Short

Long

Commands

Description

i

input

all

Input consummed.

input_fps

all

Input FPS used for video extraction.

skip_frame

all

Number of frames to skip inbetween two frames.

R

recursive

all

Recursive directory search.

o

output

all

Outputs produced.

output_fps

all

Output FPS used for video reconstruction.

s

studio_format

infer draw blur

Convert from Run to Studio format.

F

fullscren

draw blur noop

Fullscreen if window output.

from_file

draw blur

Use prediction from precomputed JSON.

r

recognition_id

infer draw blur

Neural network recognition version ID.

t

threshold

infer draw blur

Threshold for predictions.

u

ampq_url

infer draw blur

AMQP url for on-premises deployments.

k

routing_key

infer draw blur

Recognition routing key for on-premises deployments.

S

draw_score

draw

Overlay prediction score.

no_draw_scores

draw

Do not overlay prediction score.

L

draw_labels

draw

Overlay the prediction label.

no_draw_labels

draw

Do not overlay the prediction label.

M

blur_method

blur

Blur method,pixel, gaussian or black.

B

blur_strengh

blur

Blur strength.

verbose

all

Increase output verbosity.