import numpy as np
import cv2 cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier("data/haarcascade_eye.xml")
smile_cascade = cv2.CascadeClassifier("data/haarcascade_smile.xml")
# img = cv2.imread("img/test1.jpg") while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y : y + h, x : x + w]
roi_color = img[y : y + h, x : x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
# smile = smile_cascade.detectMultiScale(
# roi_gray,
# scaleFactor=1.16,
# minNeighbors=35,
# minSize=(25, 25),
# flags=cv2.CASCADE_SCALE_IMAGE,
# )
# for (x2, y2, w2, h2) in smile:
# cv2.rectangle(roi_color, (x2, y2), (x2 + w2, y2 + h2), (255, 0, 0), 2)
# cv2.putText(img, "Smile", (x, y - 7), 3, 1.2, (0, 255, 0), 2, cv2.LINE_AA)
cv2.imshow("img", img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
加点代码实现实时磨皮效果,sigmaSpace值取的越大,循环次数越多运行越卡,可以只对脸部区域磨皮、但是一旦失去脸部焦点,瞬间被打回原形。
import numpy as np
import cv2 cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier("data/haarcascade_eye.xml")
smile_cascade = cv2.CascadeClassifier("data/haarcascade_smile.xml")
# img = cv2.imread("img/test1.jpg") while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
img = cv2.bilateralFilter(src=img, d=0, sigmaColor=50, sigmaSpace=5)
roi_gray = gray[y : y + h, x : x + w]
roi_color = img[y : y + h, x : x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
# smile = smile_cascade.detectMultiScale(
# roi_gray,
# scaleFactor=1.16,
# minNeighbors=35,
# minSize=(25, 25),
# flags=cv2.CASCADE_SCALE_IMAGE,
# )
# for (x2, y2, w2, h2) in smile:
# cv2.rectangle(roi_color, (x2, y2), (x2 + w2, y2 + h2), (255, 0, 0), 2)
# cv2.putText(img, "Smile", (x, y - 7), 3, 1.2, (0, 255, 0), 2, cv2.LINE_AA)
cv2.imshow("img", img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break