kenzy_image ·

This module is dedicated to simplifying the interactions required for face detection, face recognition, object detection, and motion detection.
Installation
The easiest way to install kenzy_image is with the following:
pip install kenzy-image
Just make sure you're running Python 3.6 or newer.
Embedding into your program
Visit the detector page
Running as module
Options are as follows for starting kenzy_image:
python -m kenzy_image [OPTIONS]
General Options:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
Configuration file
-v, --version Print Version
Startup Options:
--camera-device Device ID or RTSP stream to leverage for source images.
--no-markup Hide outlines and names
--scale-factor SCALE_FACTOR
Image scale factor (decimal). Values < 1 improve performance.
--orientation ORIENTATION
Image orientation. (0, 90, 180, or 270)
Face Detection:
--no-faces Disable face detection
--face-detect-default-name FACE_DETECT_DEFAULT_NAME
Set the Unknown face name
--face-detect-model FACE_DETECT_MODEL
Model to leverage (hog or cnn)
--face-detect-font-color FACE_DETECT_FONT_COLOR
Face names font color as tuple e.g. (0, 0, 255)
--face-detect-outline-color FACE_DETECT_OUTLINE_COLOR
Faces outline color as tuple e.g. (0, 0, 255)
--no-face-names Hides the face names even if identified.
--faces path name Face image and name e.g. --face image.jpg LNXUSR1
Object Detection:
--no-objects Disable object detection
--object-detect-type TYPE
Type of model to use (yolo or ssd)
--object-detect-config OBJECT_DETECT_CONFIG
Object detection configuration
--object-detect-model OBJECT_DETECT_MODEL
Object detection inference model file
--object-detect-labels OBJECT_DETECT_LABELS
Object detection inference model label files
--object-detect-font-color OBJECT_DETECT_FONT_COLOR
Object names font color as tuple e.g. (0, 0, 255)
--object-detect-outline-color OBJECT_DETECT_OUTLINE_COLOR
Object detection outline color as tuple e.g. (0, 0, 255)
--object-list OBJECT_LIST
Limit list of objects to detect detection (optional)
--no-object-names Hides the object names even if identified.
Motion Detection:
--no-motion Disable motion detection
--motion-detect-threshold MOTION_DETECT_THRESHOLD
Motion detection difference threshold
--motion-detect-min-area MOTION_DETECT_MIN_AREA
Motion detection minimum pixel area
--motion-detect-outline-color MOTION_DETECT_OUTLINE_COLOR
Motion area outline color as tuple e.g. (0, 0, 255)
Logging Options:
--log-level LOG_LEVEL
Options are full, debug, info, warning, error, and critical
--log-file LOG_FILE Redirects all logging messages to the specified file
To start the services try:
python3 -m kenzy_image
More information available at:
http://kenzy.ai
The Object Detection model for ssd
is MobileNet V3. The model for yolo
is Yolov7 which will leverage any available and compatible cuda GPU. You may need to test both methods to find the one that suits your needs the best.
Help & Support
Help and additional details is available at https://kenzy.ai