For more information about the pre-trained models, refer to the Open Model Zoo repository on GitHub*.
- Opencv 4.0.0
- Openvino 2019R2
- Python 3
- Pre-Trained Models
- Public Models Downloader
- Demos that demonstrate models usage with Deep Learning Deployment Toolkit
To recognize faces on a frame, the demo needs a gallery of reference images. Each image should contain a tight crop of face. You can create the gallery from an arbitrary list of images:
- Put images containing tight crops of frontal-oriented faces to a separate empty folder. Each identity could have multiple images. Name images as
id_name.0.png, id_name.1.png, .... - Run the
create_list.py <path_to_folder_with_images>command to get a list of files and identities in.jsonformat.
Modify build_dir and build_type in build_demos.sh bash script and run.
Running the application with the -h option yields the following usage message:
cd $build_dir
./face_reg_track -h
InferenceEngine:
API version ............ <version>
Build .................. <number>
face_reg_track [OPTION]
Options:
-h Print a usage message.
-i "<path>" Required. Path to a video file or a folder with images (all images should have names 0000000001.jpg, 0000000002.jpg, etc).
-m_det "<path>" Required. Path to the Pedestrian Detection Retail model (.xml) file.
-m_reid "<path>" Required. Path to the Pedestrian Reidentification Retail model (.xml) file.
-l "<absolute_path>" Optional. For CPU custom layers, if any. Absolute path to a shared library with the kernels implementation.
Or
-c "<absolute_path>" Optional. For GPU custom kernels, if any. Absolute path to the .xml file with the kernels description.
-d_det "<device>" Optional. Specify the target device for pedestrian detection (CPU, GPU, FPGA, HDDL, MYRIAD, or HETERO).
-d_reid "<device>" Optional. Specify the target device for pedestrian reidentification (CPU, GPU, FPGA, HDDL, MYRIAD, or HETERO).
-pc Optional. Enable per-layer performance statistics.
-crop_gallery Optional. Crop images during faces gallery creation.
-out "<path>" Optional. The file name to write output log file with results of pedestrian tracking. The format of the log file is compatible with MOTChallenge format.To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to https://download.01.org/opencv/.
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.
Example of a valid command line to run the application with pre-trained models for recognizing identities from faces_gallery:
cd $build_dir
sh ./face_reg_track \
-m_fd <path_to_model>/face-detection-retail-0004.xml \
-m_reid <path_to_model>/face-reidentification-retail-0095.xml \
-m_lm <path_to_model>/landmarks-regression-retail-0009.xml \
-fg <path_to_faces_gallery.json> \
-d_fd GPU -d_reid CPU -d_lm CPU \
-t_fd 0.7 -t_reid 0.6 \
-i <path_to_video_sources> \
-out <path_to_output_folder>
face_reg_track_debug \
-m_det $mFDR32 -m_reid $mPRI32 -m_lm $mLMR32 -m_hp $mHP32 \
-fg $facesGal \
-d_det CPU -d_reid CPU -d_lm CPU -d_hp CPU \
-t_det 0.5 -t_reid 0.5 \
-min_size_fr 128 -crop_gallery -auto_reg -debug \
-i /dev/video0 \
-out out
face_reg_track_debug \
-m_det $mFDR32 -m_reid $mPRI32 -m_lm $mLMR32 -m_hp $mHP32 \
-fg $facesGal \
-d_det CPU -d_reid CPU -d_lm CPU -d_hp CPU \
-t_det 0.7 -t_reid 0.3 \
-min_size_fr 128 \
-i rtsp:https://admin:[email protected]:554 \
-out out/08.31_21h15
face_reg_track_debug \
-m_det $mFDR32 -m_reid $mPRI32 -m_lm $mLMR32 -m_hp $mHP32 \
-fg ugly_template.json \
-d_det CPU -d_reid CPU -d_lm CPU -d_hp CPU \
-t_det 0.7 -t_reid 0.3 \
-min_size_fr 128 \
-i rtsp:https://admin:[email protected]:554 \
-out out/08.31_21h15