kenzy_image.detector

Basic Usage

import cv2  # For webcam input
from kenzy_image.core import detector

# Create our Kenzy Detector Object
myImageDetector = detector(detectFaces=True, 
                           detectObjects=True, 
                           objModelType="ssd",  # or yolo
                           detectMotion=True, 
                           imageMarkupFaces=True, 
                           scaleFactor=0.5)

# Add Named Faces to Recognize
myImageDetector.addFace("IMG_4291_portrait_jon.jpg", "Jon Doe")
myImageDetector.addFace("IMG_7033_portrait_jane.jpg", "Jane Doe")

# Open the camera stream
cam = cv2.VideoCapture(0)

while True:

    # Read frame from camera
    ret, frame = cam.read()

    # Analyze with kenzy_image
    myImageDetector.analyze(frame)

    print("=======================")
    print("FACES     =", len(myImageDetector.faces))
    print("OBJECTS   =", [x.get("name") for x in myImageDetector.objects])
    print("MOVEMENTS =", True if len(myImageDetector.movements) > 0 else False)

    # Show the frame with markup
    cv2.imshow('KENZY_IMAGE', myImageDetector.image)

    # Loop until [Esc] or "q"
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cam.release()
cv2.destroyAllWindows()

detector Constructor

Variable	Default	Description
orientation	`0`	Image orientation. (0, 90, 180, or 270)
detectFaces	`True`	Enable/disable face detection
detectObjects	`True`	Enable/disable object detection
detectMotion	`True`	Enable/disable motion detection
imageMarkupFaces	`True`	Enables drawing face outlines on detector.image
imageMarkupObjects	`True`	Enables drawing object outlines on detector.image
imageMarkupMotion	`True`	Enables drawing motion outlines on detector.image
scaleFactor	`1.0`	Scale coefficient to apply to original image 0.75 = scale source image to 75% to improve performance
defaultFaceName	`"Unknown"`	The label to use for any faces detected but not recognized
faceModel	`"hog"`	Face model to use (hog or cnn)
faceFontColor	`(255, 255, 255)`	Color as RGB tuple for text of face name
faceOutlineColor	`(0, 0, 255)`	Color as RGB tuple for box around the face
showFaces	`True`	Enable drawing outlines around faces on detector.image
objModelType	`ssd`	Sets the model type (can be yolo or ssd)
objDetectCfg	String	Sets the path to the configuration for the inference model
objDetectModel	String	Sets the inference model to use for objects
objDetectLabels	String	Sets the path to the object labels list file
objDetectList	String	Limits list of objects to detect detection (optional)
objFontColor	`(255, 255, 255)`	Color as RGB tuple for text of object name
objOutlineColor	`(255, 0, 0)`	Color as RGB tuple for box around the object
showObjectNames	`True`	Enable drawing outlines around objects on detector.image
motionThreshold	`20`	Threshold of color change between frames indicating motion
motionMinArea	`50`	Minimum pixel area of change indicating motion
motionOutlineColor	`(255, 0, 0)`	Color as RGB tuple for box around the motion

Return Values

The detector.analyze() processes the supplied image and saves the results into variables as defined below:

Variable	Type	Description
detector.faces	list	Listing of faces detected, their location in the image, and their name if known
detector.objects	list	Listing of objects detected, their location within the image, and their type (e.g. "cellphone")
detector.movements	list	Listing of all areas where movement was identified with their bounding boxes in the image

In the Basic Usage section above it shows a very simple example of how these values could be leveraged.

Yolov7 - objDetectCfg

For yolo models you can supply your own configuration to change the confidence score, IOU, and channels.

import cv2  # For webcam input
from kenzy_image.core import detector

# Create our Kenzy Detector Object
myImageDetector = detector(detectFaces=True, 
                           detectObjects=True, 
                           objModelType="yolo",
                           objDetectModel="/path/to/yolov7-tiny.pt",
                           objDetectLabels="/path/to/yolov7-labels.txt",
                           objDetectCfg={
                               "libary": "yolov7",
                               "confidence": 0.5,
                               "iou": 0.45,
                               "size": 640,
                               "augment": False
                           },
                           detectMotion=True, 
                           imageMarkup=True, 
                           scaleFactor=0.5)

Optimizing Performance & Results

Object detection is currently using Yolov7 with the COCO data set from the yolov7-tiny model.
To use motion detection you must have at least 2 image frames sent via .analyze() as it calculates the difference between the two images to identify motion. For best results consider using a video or camera feed.
You may need to increase the motionMinArea to reduce chatter
Shrinking the scaleFactor to a decimal between 0 and 1 will improve performance at the cost of accuracy by scaling down the image. Likewise, if you increase this value beyond 1 your results may improve marginally at the expense of performance.