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import cv2 as cv2
import matplotlib.pyplot as plt
# Load the cascade
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# Read the input image
img = cv2.imread(r"--file path--")
# Detect faces
faces = face_cascade.detectMultiScale(img,1.1,5)
# Draw bounding box around the faces
countFace = 0
for (x, y, w, h) in faces:
rect = cv2.rectangle(img, (x, y), (x+w, y+h), (2, 150, 255), 2)
countFace = countFace + 1
num = str(countFace)
cv2.putText(rect, num, (x, y-5), cv2.FONT_ITALIC, 0.5, (2, 150, 255), 2)
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# Showing number of faces detected in the image
print(len(faces),"faces detected!")
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# Plotting the image with face detected
# picture size
plt.figure(figsize=(10,10))
#show Picture detected
cv2.imshow('Results',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
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import cv2 as cv2
import matplotlib.pyplot as plt
# Load the cascade
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# open camera
cap = cv2.VideoCapture(0)
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while True:
# read frame
ret, frame = cap.read()
# detect face from cmera frame
faces = face_cascade.detectMultiScale(frame, scaleFactor=1.3, minNeighbors=5)
countFace = 0
# drawing rectangle
for (x, y, w, h) in faces:
rect = cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
countFace = countFace + 1
num = str(countFace)
cv2.putText(rect, num, (x, y-5), cv2.FONT_ITALIC, 0.5, (2, 150, 255), 2)
# show image
cv2.imshow('Face Detection', frame)
# press ESC to close camera tap
if cv2.waitKey(1) == 27:
break
# close camera and tap
cap.release()
cv2.destroyAllWindows()
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