[python][mediapipe]摄像头人脸检测_mediapipe fl1623863129

import cv2
import mediapipe as mp

# 导入BlazeFace模型
mp_face_detection = mp.solutions.face_detection
model = mp_face_detection.FaceDetection(
    min_detection_confidence=0.5,  # 置信度阈值，过滤掉小于置信度的预测框
    model_selection=1,  # 选择模型，0 适用于人脸离摄像头比较近（2米内）,1 适用于比较远（5米以内）
)
# 导入可视化函数以及可视化样式
mp_drawing=mp.solutions.drawing_utils
# 关键点样式
keypoint_style=mp_drawing.DrawingSpec(thickness=5,circle_radius=3,color=(0,255,0))
# 人脸预测框样式
bbox_style=mp_drawing.DrawingSpec(thickness=5,circle_radius=3,color=(255,0,0))
cap = cv2.VideoCapture(0)

# 获取视频帧速率 FPS
frame_fps = int(cap.get(cv2.CAP_PROP_FPS))
# 获取视频帧宽度和高度
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print("video fps={},width={},height={}".format(frame_fps, frame_width, frame_height))
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # BGR转RGB
    img_RGB = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    # 将RGB输入模型预测结果
    results = model.process(img_RGB)
    # 可视化人脸框和人脸关键点
    for detection in results.detections:
        mp_drawing.draw_detection(frame,
                                  detection,
                                  keypoint_drawing_spec=keypoint_style,
                                  bbox_drawing_spec=bbox_style)
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()