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faceDetectFromImage.py
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faceDetectFromImage.py
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import cv2
# Get user supplied values
# imagePath = "G:\LBS\Face-Recognition\sample pics\3.png"
faceCascPath = "haarcascade_frontalface_default.xml"
eyeCascPath = "haarcascade_eye.xml"
smileCascPath = "haarcascade_smile.xml"
# Create the haar cascade
faceCascade = cv2.CascadeClassifier(faceCascPath)
eyeCascade = cv2.CascadeClassifier(eyeCascPath)
smileCascade = cv2.CascadeClassifier(smileCascPath)
# Read the image
# image = cv2.imread(imagePath)
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
import numpy as np
cap = cv2.VideoCapture(0)
while(cap.isOpened()): # check !
# capture frame-by-frame
ret, frame = cap.read()
if ret: # check ! (some webcam's need a "warmup")
# our operation on frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Display the resulting frame
cv2.imshow('frame', gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=2.1,
minNeighbors=5,
minSize=(10, 10),
flags=cv2.CASCADE_SCALE_IMAGE
)
# Draw a rectangle around the faces and eyes
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = frame[y:y+h, x:x+w]
eyes = eyeCascade.detectMultiScale(roi_gray)
smiles = smileCascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 0, 255), 2)
for (sx, sy, sw, sh) in smiles:
cv2.rectangle(roi_color, (sx, sy), (sx+sw, sy+sh), (255, 0, 0), 2)
cv2.imshow("Features found", frame)
cv2.waitKey(0)
cap.release()
cv2.destroyAllWindows()