Welcome to the MenpoDetect documentation!
MenpoDetect is a Python package designed to make object detection, in particular face detection, simple. MenpoDetect relies on the core package of Menpo, and thus the output of MenpoDetect is always assumed to be Menpo core types. If you aren’t sure what Menpo is, please take a look over at Menpo.org.
A short example is often more illustrative than a verbose explanation. Let’s assume that you want to load a set of images and that we want to detect all the faces in the images. We could do this using the Viola-Jones detector provided by OpenCV as follows:
import menpo.io as mio from menpodetect import load_opencv_frontal_face_detector opencv_detector = load_opencv_frontal_face_detector() images =  for image in mio.import_images('./images_folder'): opencv_detector(image) images.append(image)
Where we use Menpo to load the images from disk and then detect as many faces as possible using OpenCV. The detections are automatically attached to each image in the form of a set of landmarks. These are then easily viewed within a Jupyter notebook using the MenpoWidgets package:
%matplotlib inline from menpowidgets import visualize_images visualize_images(images)
MenpoDetect was not designed for performing novel object detection research. Therefore, it relies on a number of existing packages and merely normalizes the inputs and outputs so that they are consistent with core Menpo types. These projects are as follows:
dlib - Provides the detection capabilities of the Dlib project. This is a HOG-SVM based detector that will return a very low number of false positives.
OpenCV - Provides the detection capabilities of the OpenCV project. This is only available for Python 2.x due to limitations of the OpenCV project. OpenCV implements a Viola-Jones detector and provides models for both frontal and profile faces as well as eyes.
We would be very happy to see this collection expand, so pull requests are very welcome!