Unit-tests, command line tools, and GUI tools listed by modules.
* File Naming Conventions:
- Unit-Tests pattern: *_utest.cpp/.h - Command line tools pattern: *_cli.cpp/.h - Graphic user interface tools pattern: *_gui.cpp/.h
The front of file name must describe the goal of the tests/tools.
* All test implementations must uses DIGIKAM_TESTS_LOG QtDebug space defined in digikam_debug.h. To show debug traces on the console, turn on the env. variable QT_LOGGING_RULES with "digikam*=true".
* No need to support translations in this area.
* With command line tools, try to use QCoreApplication instead QApplication when possible.
* All data test files must be hosted on this git LFS based repository: https://invent.kde.org/graphics/digikam-test-data
See class from dtestdatadir.h to manage data test files at run-time, from developer computer and on CI VM.

These are instructions to test facerec_dnn.py
Required packages and dependencies: 1. clone openface folder from github (https://github.com/cmusatyalab/openface) 2. install dlib for python with: pip install dlib 3. download pretrained model (nn4.small2) to openface/models/openface, from this link: https://cmusatyalab.github.io/openface/models-and-accuracies/ 4. download shape_predictor_68_face_landmarks.dat from dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2">http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2, unzip and move to openface/models/dlib 5. download SSD model for face detector from this github (https://github.com/weiliu89/caffe/tree/ssd#models), first link to 07+12: SSD300 and unzip 6. copy and paste facerec_dnn.py to folder openface/demos 7. change sys.path.append at line 18 to "path/to/openface" on your machine
How to run: > ./facerec_dnn.py -d <path_to_orl_dataset> -m <path_to_nn4_model> -dt <path_to_folder_contain_deploy.prototxt_of_ssd_detector> -o <number_of_person_to_test> -s <number_of_image_of_each_person_to_process> -r <ratio_of_train/number_of_processed_images>