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face_detect.py
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face_detect.py
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import cv2
def set_image_size(image, set_height = None, set_width = None):
# Calculate the aspect ratio of the original image
height, width, _ = image.shape
aspect_ratio = width / height
if set_height:
height = set_height
width = int(height * aspect_ratio)
elif set_width:
width = set_width
height = int(width / aspect_ratio)
resized_image = cv2.resize(image, (width, height))
return resized_image , width, height
def detect_image(imagePath,haarCascadePart):
# Create the haar cascade
cascPath = "HaarCascadeFiles/"+haarCascadePart+".xml"
faceCascade = cv2.CascadeClassifier(cascPath)
# Read the image
image = cv2.imread(imagePath)
image, image_width, image_height = set_image_size(image, set_height = 500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags = cv2.CASCADE_SCALE_IMAGE
)
print("Found {0} faces!".format(len(faces)))
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
# Show Image with detected Faces
cv2.namedWindow('Faces found', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Faces found', image_width, image_height)
cv2.imshow("Faces found", image)
cv2.waitKey(0)
if __name__ == '__main__':
detect_image('PhotoExample\Example.jpg',"haarcascade_frontalface_default")