OpenCV 4 with Python Blueprints - Second Edition

Using a pre-trained cascade classifier

A cascade classifier can be loaded and applied to an image (grayscale) using the following code, where we first read the image, then convert it to grayscale, and finally detect all the faces using a cascade classifier:

import cv2

gray_img = cv2.cvtColor(cv2.imread('example.png'), cv2.COLOR_RGB2GRAY)cascade_clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')faces = cascade_clf.detectMultiScale(gray_img,                                     scaleFactor=1.1,                                     minNeighbors=3,                                     flags=cv2.CASCADE_SCALE_IMAGE)

From the previous code, the detectMultiScale function comes with a number of options:

• minFeatureSize is the minimum face size to consider—for example, 20 x 20 pixels.
• searchScaleFactor is the amount by which we rescale the ...