模型文件下载：https://github.com/guofei9987/datasets_for_ml/blob/master/cvdata.zip
import cv2
from PIL import Image, ImageDraw, ImageColor
def detect_faces(image_path):
img = cv2.imread(image_path)
face_cascade = cv2.CascadeClassifier("cvdata/haarcascades/haarcascade_frontalface_default.xml")
if img.ndim == 3:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=3, minSize=(10, 10),
flags=cv2.CASCADE_SCALE_IMAGE)
result = []
for (x, y, width, height) in faces:
result.append((x, y, x + width, y + height))
return result
def draw_faces(image_path, new_path):
"""
Open the image based on the image_path and draw a rectangle on the faces.
Save to new_path after draw.
"""
faces = detect_faces(image_path)
if faces:
img = Image.open(image_path)
draw_instance = ImageDraw.Draw(img)
for (x1, y1, x2, y2) in faces:
draw_instance.rectangle((x1, y1, x2, y2), outline=(255, 0, 0))
img.save(new_path)
draw_faces(img_path, img_new_path)
额外
- 模型文件中还有身体识别、眼睛识别之类的，替换模型文件即可，代码一样。
- todo: 不转化为gray是否可以识别
下面这个是旧版本
import cv2
# 读取待检测的图像
image = cv2.imread('dface2.jpeg')
# 获取 XML 文件，加载人脸检测器
# 这个地方只能用绝对路径，GitHub下载的也报错
faceCascade = cv2.CascadeClassifier(
'/Users/guofei/opt/anaconda3/lib/python3.8/site-packages/cv2/data/haarcascade_frontalface_default.xml')
# eye_cascade = cv2.CascadeClassifier('/Users/guofei/opt/anaconda3/lib/python3.8/site-packages/cv2/data/haarcascade_eye.xml')
# 色彩转换，转换为灰度图像
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 调用函数 detectMultiScale
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.15,
minNeighbors=5,
minSize=(5, 5)
)
print(faces)
# 打印输出的测试结果
print("发现{0}个人脸!".format(len(faces))) # 逐个标注人脸
for (x, y, w, h) in faces:
# cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2) #矩形标注
cv2.circle(image, (int((x + x + w) / 2), int((y + y + h) / 2)), int(w / 2), (0, 255, 0), 2) # 显示结果
cv2.imshow("dect", image)
# 保存检测结果 cv2.imwrite("re.jpg",image)
cv2.waitKey(0)
cv2.destroyAllWindows()
