Face Detector Plus

"A comprehensive Python package that integrates multiple face detection algorithms, offering flexible and efficient solutions for various face recognition applications."

Key features:

• Easy to understand and setup
• Easy to manage
• Requires very less or no tuning for any resolution image
• No need to download models, they're automatically maintained
• Uses ultralight face detection models that is very fast on CPU alone
• Get very good speed and accuracy on CPU alone
• All detectors share same parameters and methods, makes it easier to switch and go

Detectors:

• Hog detector
• CNN detector
• Caffemodel detector
• UltraLight 320 detector
• UltraLight 640 detector

Installation

Use the package manager pip to install face-detector-plus with the following command:

pip install face-detector-plus

If you would like to get the latest master or branch from github, you could also:

pip install git+https://github.com/huseyindas/face-detector-plus

Or even select a specific revision (branch/tag/commit):

pip install git+https://github.com/huseyindas/face-detector-plus@master

Similarly, for tag specify tag with @v0.x.x. For example to download tag v0.1.0 from Git use:

pip install git+https://github.com/huseyindas/[email protected]

Quick usage

Like said setup and usage is very simple and easy.

• Import the detector you want,
• Initialize it,
• Get predicts

Example

from face_detector_plus import Ultralight320Detector
from face_detector_plus.utils import annotate_image

detector = Ultralight320Detector()

image = cv2.imread("image.png")

faces = detector.detect_faces(image)
image = annotate_image(image, faces, width=3)

cv2.imshow("view", image)
cv2.waitKey(100000)

CaffeModel Detector

Caffemodel is very light weight model that uses less resources to perform detections that is created by caffe (Convolutional Architecture for Fast Feature Embedding).

import cv2
from face_detector_plus import CaffemodelDetector
from face_detector_plus.utils import annotate_image

vid = cv2.VideoCapture(0)
detector = CaffemodelDetector()

while True:
    rect, frame = vid.read()
    if not rect:
        break

    bbox = detector.detect_faces(frame)
    frame = annotate_image(frame, bbox)

    cv2.imshow("Caffe Model Detection", frame)

    cv2.waitKey(1)

Configurable options for CaffeModel detector.

Syntax: CaffemodelDetector(**options)

Options	Description
convert_color	Takes OpenCV COLOR codes to convert the images. Defaults to cv2.COLOR_BGR2RGB
confidence	Confidence score is used to refrain from making predictions when it is not above a sufficient threshold. Defaults to 0.5
scale	Scales the image for faster output (No need to set this manually, scale will be determined automatically if no value is given)
mean	Scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true. Defaults to (104.0, 177.0, 123.0).
scalefactor	Multiplier for images values. Defaults to 1.0.
crop	Flag which indicates whether image will be cropped after resize or not. Defaults to False.
swapRB	Flag which indicates that swap first and last channels in 3-channel image is necessary. Defaults to False.
transpose	Transpose image. Defaults to False.
resize	Spatial size for output image. Default is (300, 300)

Useful methods for this detector:

• detect_faces(image)

  This method will return coordinates for all the detected faces of the given image

  Options	Description
  image	image in numpy array format

• detect_faces_keypoints(image, get_all=false)

  This method will return coordinates for all the detected faces along with their facial keypoints of the given image. Keypoints are detected using dlib's new shape_predictor_68_face_landmarks_GTX.dat` model.

  Note: Generating keypoints might take more time if compared with detect_faces method

  Options	Description
  image	Image in numpy array format
  get_all	Weather to get all facial keypoints or the main (chin, nose, eyes, mouth)

CNN Detector

CNN (Convolutional Neural Network) might not be a light weight model but it is good at detecting faces from all angles. This detector is a hight level wrapper around dlib::cnn_face_detection_model_v1 that is fine tuned to improve overall performance and accuracy.

import cv2
from face_detector_plus import CNNDetector
from face_detector_plus.utils import annotate_image

vid = cv2.VideoCapture(0)
detector = CNNDetector()

while True:
    rect, frame = vid.read()
    if not rect:
        break

    bbox = detector.detect_faces(frame)
    frame = annotate_image(frame, bbox)

    cv2.imshow("CNN Detection", frame)

    cv2.waitKey(1)

Configurable options for CNNDetector detector.

Syntax: CNNDetector(**options)

Options	Description
convert_color	Takes OpenCV COLOR codes to convert the images. Defaults to cv2.COLOR_BGR2RGB
number_of_times_to_upsample	Up samples the image number_of_times_to_upsample before running the basic detector. By default is 1.
confidence	Confidence score is used to refrain from making predictions when it is not above a sufficient threshold. Defaults to 0.5
scale	Scales the image for faster output (No need to set this manually, scale will be determined automatically if no value is given)

• detect_faces(image)

  This method will return coordinates for all the detected faces of the given image

  Options	Description
  image	image in numpy array format

• detect_faces_keypoints(image, get_all=false)

  This method will return coordinates for all the detected faces along with their facial keypoints of the given image. Keypoints are detected using dlib's new shape_predictor_68_face_landmarks_GTX.dat model.

  Note: Generating keypoints might take more time if compared with detect_faces method

  Options	Description
  image	Image in numpy array format
  get_all	Weather to get all facial keypoints or the main (chin, nose, eyes, mouth)

Hog Detector

This detector uses Histogram of Oriented Gradients (HOG) and Linear SVM classifier for face detection. It is also combined with an image pyramid and a sliding window detection scheme. HogDetector is a high level client over dlib's hog face detector and is fine tuned to make it more optimized in both speed and accuracy.

If you want to detect faster with HogDetector and don't care about number of detections then set number_of_times_to_upsample=1 in the options, it will detect less fasces in less time, mainly used for real time one face detection.

import cv2
from face_detector_plus import HogDetector
from face_detector_plus.utils import annotate_image

vid = cv2.VideoCapture(0)
detector = HogDetector()

while True:
    rect, frame = vid.read()
    if not rect:
        break

    bbox = detector.detect_faces(frame)
    frame = annotate_image(frame, bbox)

    cv2.imshow("Hog Detection", frame)

    cv2.waitKey(1)

Configurable options for HogDetector detector.

Syntax: HogDetector(**options)

Options	Description
convert_color	Takes OpenCV COLOR codes to convert the images. Defaults to cv2.COLOR_BGR2RGB
number_of_times_to_upsample	Up samples the image number_of_times_to_upsample before running the basic detector. By default is 2.
confidence	Confidence score is used to refrain from making predictions when it is not above a sufficient threshold. Defaults to 0.5
scale	Scales the image for faster output (No need to set this manually, scale will be determined automatically if no value is given)

• detect_faces(image)

  This method will return coordinates for all the detected faces of the given image

  Options	Description
  image	image in numpy array format

• detect_faces_keypoints(image, get_all=false)

  This method will return coordinates for all the detected faces along with their facial keypoints of the given image. Keypoints are detected using dlib's new shape_predictor_68_face_landmarks_GTX.dat model.

  Note: Generating keypoints might take more time if compared with detect_faces method

  Options	Description
  image	Image in numpy array format
  get_all	Weather to get all facial keypoints or the main (chin, nose, eyes, mouth)

Ultra Light Detection (320px)

Ultra Light detection model is what the name says, it a very light weight, accuracy with impressive speed which is pre-trained on 320x240 sized images and only excepts 320x240 sized images but don't worry Ultralight320Detector detector will do all for you.

import cv2
from face_detector_plus import Ultralight320Detector
from face_detector_plus.utils import annotate_image

vid = cv2.VideoCapture(0)
detector = Ultralight320Detector()

while True:
    rect, frame = vid.read()
    if not rect:
        break

    bbox = detector.detect_faces(frame)
    frame = annotate_image(frame, bbox)

    cv2.imshow("Ultra 320 Detection", frame)

    cv2.waitKey(1)

Configurable options for Ultralight320Detector detector.

Syntax: Ultralight320Detector(**options)

Options	Description
convert_color	Takes OpenCV COLOR codes to convert the images. Defaults to cv2.COLOR_BGR2RGB
mean	Metric used to measure the performance of models doing detection tasks. Defaults to [127, 127, 127].
confidence	Confidence score is used to refrain from making predictions when it is not above a sufficient threshold. Defaults to 0.5
scale	Scales the image for faster output (No need to set this manually, scale will be determined automatically if no value is given)
cache	It uses same model for all the created sessions. Default is True

• detect_faces(image)

  This method will return coordinates for all the detected faces of the given image

  Options	Description
  image	image in numpy array format

• detect_faces_keypoints(image, get_all=false)

  This method will return coordinates for all the detected faces along with their facial keypoints of the given image. Keypoints are detected using dlib's new shape_predictor_68_face_landmarks_GTX.dat model.

  Note: Generating keypoints might take more time if compared with detect_faces method

  Options	Description
  image	Image in numpy array format
  get_all	Weather to get all facial keypoints or the main (chin, nose, eyes, mouth)

Ultra Light Detection (640px)

Ultra Light detection model is what the name says, it a very light weight, accuracy with impressive speed which is pre-trained on 640x480 sized images and only excepts 640x480 sized images but don't worry Ultralight640Detector detector will do all for you.

This detector will be more accurate than 320 sized ultra light model (Ultralight320Detector) but might take a little more time.

import cv2
from face_detector_plus import Ultralight640Detector
from face_detector_plus.utils import annotate_image

vid = cv2.VideoCapture(0)
detector = Ultralight640Detector()

while True:
    rect, frame = vid.read()
    if not rect:
        break

    bbox = detector.detect_faces(frame)
    frame = annotate_image(frame, bbox)

    cv2.imshow("Ultra 640 Detection", frame)

    cv2.waitKey(1)

Configurable options for Ultralight640Detector detector.

Syntax: Ultralight640Detector(**options)

Options	Description
convert_color	Takes OpenCV COLOR codes to convert the images. Defaults to cv2.COLOR_BGR2RGB
mean	Metric used to measure the performance of models doing detection tasks. Defaults to [127, 127, 127].
confidence	Confidence score is used to refrain from making predictions when it is not above a sufficient threshold. Defaults to 0.5
scale	Scales the image for faster output (No need to set this manually, scale will be determined automatically if no value is given)
cache	It uses same model for all the created sessions. Default is True

• detect_faces(image)

  This method will return coordinates for all the detected faces of the given image

  Options	Description
  image	image in numpy array format

• detect_faces_keypoints(image, get_all=false)

  This method will return coordinates for all the detected faces along with their facial keypoints of the given image. Keypoints are detected using dlib's new shape_predictor_68_face_landmarks_GTX.dat model.

  Note: Generating keypoints might take more time if compared with detect_faces method

  Options	Description
  image	Image in numpy array format
  get_all	Weather to get all facial keypoints or the main (chin, nose, eyes, mouth)

Annotate Image Function

Annotates the given image with the payload returned by any of the detectors and returns a well annotated image with boxes and keypoints on the faces.

Configurable options for annotate_image function.

Syntax: annotate_image(**options)

Options	Description
image	Give image for annotation in numpy.Array format
faces	Payload returned by detector.detect_faces or detector.detect_faces_keypoints
box_rgb	RGB color for rectangle to be of. Defaults to (100, 0, 255).
keypoints_rgb	RGB color for keypoints to be of. Defaults to (150, 0, 255).
width	Width of annotations. Defaults to 2