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smile_detector_image.py
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smile_detector_image.py
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import cv2
width = 800
height = 600
blue = (255, 0, 0)
# load the image, resize it, and convert it to grayscale
image = cv2.imread("images/1.jpg")
image = cv2.resize(image, (width, height))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# load the haar cascades face and smile detectors
face_detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
smile_detector = cv2.CascadeClassifier("haarcascade_smile.xml")
# detect faces in the grayscale image
face_rects = face_detector.detectMultiScale(gray, 1.1, 8)
# loop over the face bounding boxes
for (x, y, w, h) in face_rects:
cv2.rectangle(image, (x, y), (x + w, y + h), blue, 2)
# extract the face from the grayscale image
roi = gray[y:y + h, x:x + w]
# apply the smile detector to the face roi
smile_rects, rejectLevels, levelWeights = smile_detector.detectMultiScale3(
roi, 2.5, 20, outputRejectLevels=True)
# if there was no detection, we consider this a "no smiling" detection
if len(levelWeights) == 0:
cv2.putText(image, "Not Smiling", (20, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, blue, 3)
else:
# if `levelWeights` is below 2, we classify this as "Not Smiling"
if max(levelWeights) < 2:
cv2.putText(image, "Not Smiling", (20, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, blue, 3)
# otherwise, there is a smiling in the face ROI
else:
cv2.putText(image, "Smiling", (20, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, blue, 3)
cv2.imshow("image", image)
cv2.waitKey(0)