#Clipping detector project how to
#Clipping detector project install

Then join PyImageSearch University today!

Ready to run the code right now on your Windows, macOS, or Linux systems?.

Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?.

Learning on your employer’s administratively locked system?.

Having problems configuring your development environment?įigure 2: Having trouble configuring your dev environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University - you’ll be up and running with this tutorial in a matter of minutes. If you need help configuring your development environment for OpenCV and dlib, I highly recommend that you read the following two tutorials:

#Clipping detector project install

Luckily, you can install OpenCV and dlib via pip: $ pip install opencv-contrib-python To follow this guide, you need to have both the OpenCV library and dlib installed on your system.

#Clipping detector project how to

In the remainder of this tutorial, you will learn how to use both of these dlib face detection methods. Note: I’ve included the mmod_human_face_detector.dat file in the “Downloads” section of this guide, so you don’t have to go hunting for it. This method accepts a single parameter, modelPath, which is the path to the pre-trained mmod_human_face_detector.dat file residing on disk. By nature of how the Histogram of Oriented Gradients (HOG) descriptor works, it is not invariant to changes in rotation and viewing angle.įor more robust face detection, you can use the MMOD CNN face detector, available via the cnn_face_detection_model_v1 function. A call to it returns the pre-trained HOG + Linear SVM face detector included in the dlib library.ĭlib’s HOG + Linear SVM face detector is fast and efficient. The get_frontal_face_detector function does not accept any parameters. MMOD CNN: dlib.cnn_face_detection_model_v1(modelPath).HOG + Linear SVM: dlib.get_frontal_face_detector().The dlib library provides two functions that can be used for face detection: The first one is a HOG + Linear SVM face detector, and the other is a deep learning MMOD CNN face detector ( image source). Figure 1: The dlib library provides two functions for face detection.