The Haar cascades method can be extended to detect all types of objects. Let's see how to use it to detect the eyes and nose in the input video.
- Create a new Python file, and import the following packages:
import cv2 import numpy as np
- Load the face, eyes, and nose cascade files:
# Load face, eye, and nose cascade files face_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_frontalface_alt.xml') eye_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_eye.xml') nose_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_mcs_nose.xml') - Check whether the files loaded correctly:
# Check if face cascade file has been loaded if face_cascade.empty(): raise IOError('Unable to load the face cascade classifier xml file') # Check if eye cascade file has been loaded if eye_cascade.empty(): raise IOError('Unable to load the eye cascade classifier xml file') # Check if nose cascade file has been loaded if nose_cascade.empty(): raise IOError('Unable to load the nose cascade classifier xml file') - Initialize the video capture object:
# Initialize video capture object and define scaling factor cap = cv2.VideoCapture(0)
- Define the scaling factor:
scaling_factor = 0.5
- Keep looping until the user presses the Esc key:
while True: # Read current frame, resize it, and convert it to grayscale ret, frame = cap.read() - Resize the frame:
frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA) - Convert the image to grayscale:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
- Run the face detector on the grayscale image:
# Run face detector on the grayscale image faces = face_cascade.detectMultiScale(gray, 1.3, 5) - As we know that eyes and noses are always on faces, we can run these detectors only in the face region:
# Run eye and nose detectors within each face rectangle for (x,y,w,h) in faces: - Extract the face ROI:
# Grab the current ROI in both color and grayscale images roi_gray = gray[y:y+h, x:x+w] roi_color = frame[y:y+h, x:x+w] - Run the eye detector:
# Run eye detector in the grayscale ROI eye_rects = eye_cascade.detectMultiScale(roi_gray) - Run the nose detector:
# Run nose detector in the grayscale ROI nose_rects = nose_cascade.detectMultiScale(roi_gray, 1.3, 5) - Draw circles around the eyes:
# Draw green circles around the eyes for (x_eye, y_eye, w_eye, h_eye) in eye_rects: center = (int(x_eye + 0.5*w_eye), int(y_eye + 0.5*h_eye)) radius = int(0.3 * (w_eye + h_eye)) color = (0, 255, 0) thickness = 3 cv2.circle(roi_color, center, radius, color, thickness) - Draw a rectangle around the nose:
for (x_nose, y_nose, w_nose, h_nose) in nose_rects: cv2.rectangle(roi_color, (x_nose, y_nose), (x_nose+w_nose, y_nose+h_nose), (0,255,0), 3) break - Display the image:
# Display the image cv2.imshow('Eye and nose detector', frame) - Wait for 1 ms before going to the next iteration. If the user presses the Esc key, then break the loop.
# Check if Esc key has been pressed c = cv2.waitKey(1) if c == 27: break - Release and destroy the objects before exiting the code.
# Release video capture object and close all windows cap.release() cv2.destroyAllWindows()
- The full code is given in the
eye_nose_detector.pyfile that's already provided to you for reference. If you run this code, you will see the eyes and nose being detected in the webcam video:
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