Dynamic face detection based on python OpenCV
- 2020-09-28 08:59:12
- OfStack
This article shares the specific code of python dynamic face detection for your reference. The specific content is as follows
Code directly: Press Q to exit
import cv2
import numpy as np
cv2.namedWindow("test")
cap = cv2.VideoCapture(0) # Loading camera recording
# cap = cv2.VideoCapture("test.mp4") # Open the video file
success, frame = cap.read()
# classifier = cv2.CascadeClassifier("/Users/yuki/anaconda/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml")
# Ensure that the xml The file with the py The file in 1 If not, change this to absolute path
#haarcascade_frontalface_default.xml
classifier = cv2.CascadeClassifier("/Users/yuki/anaconda/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml")
# Ensure that the xml The file with the py The file in 1 If not, change this to absolute path
while success:
success, frame = cap.read()
size = frame.shape[:2]
image = np.zeros(size, dtype=np.float16)
image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.equalizeHist(image, image)
divisor = 8
h, w = size
minSize = (w // divisor, h // divisor)
faceRects = classifier.detectMultiScale(image, 1.2, 2, cv2.CASCADE_SCALE_IMAGE, minSize)
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
cv2.rectangle(frame,(x,y),(x+h,y+w),(0,255,0),2)
# lock Eyes and mouth
#cv2.circle(frame, (x + w // 4, y + h // 4 + 30), min(w // 8, h // 8), (255, 0, 0)) # The left eye
#cv2.circle(frame, (x + 3 * w //4, y + h // 4 + 30), min(w // 8, h // 8), (255, 0, 0)) # In the right eye
#cv2.rectangle(frame, (x + 3 * w // 8, y + 3 * h // 4), (x + 5 * w // 8, y + 7 * h // 8), (255, 0, 0))# The mouth
cv2.imshow("test", frame)
key = cv2.waitKey(10)
c = chr(key & 255)
if c in ['q', 'Q', chr(27)]:
break
cv2.destroyWindow("test")