Place Recognition Python* Demo

This demo demonstrates how to run Place Recognition models using OpenVINO™.

NOTE: Only batch size of 1 is supported.

How It Works

The demo application expects a place recognition model in the Intermediate Representation (IR) format.

As input, the demo application takes:

  • a path to an image
  • a path to a folder with images
  • a path to a video file or a device node of a web-camera

The demo workflow is the following:

  1. The demo application reads input frames.
  2. Extracted input frame is passed to artificial neural network that computes embedding vector.
  3. Then the demo application searches computed embedding in gallery of images in order to determine which image in the gallery is the most similar to what one can see on frame.
  4. The app visualizes results of it work as graphical window where following objects are shown.
    • Input frame.
    • Top-10 most similar images from the gallery.
    • Performance characteristics.

NOTE: By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Running

Run the application with the -h option to see the following usage message:

usage: place_recognition_demo.py [-h] -m MODEL -i INPUT -gf GALLERY_FOLDER
[--gallery_size GALLERY_SIZE] [--loop]
[-o OUTPUT] [-limit OUTPUT_LIMIT] [-d DEVICE]
[-l CPU_EXTENSION] [--no_show]
[-u UTILIZATION_MONITORS]
Options:
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model.
-i INPUT, --input INPUT
Required. An input to process. The input must be a
single image, a folder of images, video file or camera
id.
-gf GALLERY_FOLDER, --gallery_folder GALLERY_FOLDER
Required. Path to a folder with images in the gallery.
--gallery_size GALLERY_SIZE
Optional. Number of images from the gallery used for
processing
--loop Optional. Enable reading the input in a loop.
-o OUTPUT, --output OUTPUT
Optional. Name of output to save.
-limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT
Optional. Number of frames to store in output. If 0
is set, all frames are stored.
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on: CPU,
GPU, FPGA, HDDL or MYRIAD. The demo will look for a
suitable plugin for device specified (by default, it
is CPU).
-l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
Optional. Required for CPU custom layers. Absolute
path to a shared library with the kernels
implementations.
--no_show Optional. Do not visualize inference results.
-u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS
Optional. List of monitors to show initially.

Running the application with an empty list of options yields the short version of the usage message and an error message.

To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader. The list of models supported by the demo is in models.lst.

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

To run the demo, please provide paths to the model in the IR format, to directory with gallery images, and to an input video, image, or folder with images:

python place_recognition_demo.py \
-m <path_to_model>/netvlad-tf.xml \
-i <path_to_file>/image.jpg \
-gf <path>/gallery_folder

Demo Output

The application uses OpenCV to display gallery searching result and current inference performance.

See Also