Python+MediaPipe实现检测人脸功能详解

pip install mediapipe

pip install opencv-Python

import cv2
import mediapipe as mp

mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils

from google.colab import files
 
uploaded_short_range = files.upload()

from google.colab import files
 
uploaded_full_range = files.upload()

cv2.imread() # 获取输入

import cv2
from google.colab.patches import cv2_imshow
import math
import numpy as np
 
DESIRED_HEIGHT = 480
DESIRED_WIDTH = 480
def resize_and_show(image):
  h, w = image.shape[:2]
  if h < w:
    img = cv2.resize(image, (DESIRED_WIDTH, math.floor(h/(w/DESIRED_WIDTH))))
  else:
    img = cv2.resize(image, (math.floor(w/(h/DESIRED_HEIGHT)), DESIRED_HEIGHT))
  cv2_imshow(img)
 

# 预览图片.
 
short_range_images = {name: cv2.imread(name) 
for name in uploaded_short_range.keys()}
for name, image in short_range_images.items():
  print(name)   
  resize_and_show(image)
 
 
full_range_images = {name: cv2.imread(name) 
for name in uploaded_full_range.keys()}
for name, image in full_range_images.items():
  print(name)   
 
  resize_and_show(image)

drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)

help(mp_face_detection.FaceDetection)

with mp_face_detection.FaceDetection(min_detection_confidence=0.5, model_selection=0) as face_detection:

# 运行MediaPipe人脸检测与近距离模型
 
with mp_face_detection.FaceDetection(
    
    min_detection_confidence=0.5, model_selection=0) as face_detection:
        
  for name, image in short_range_images.items():
    # 将BGR图像转换为RGB，并使用MediaPipe人脸检测进行处理。
 
    results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
 
    # 绘制每个人脸的检测。
    print(f'Face detections of {name}:')
    
    if not results.detections:
        
      continue
    annotated_image = image.copy()

    for detection in results.detections:
 
      mp_drawing.draw_detection(annotated_image, detection)
 
    resize_and_show(annotated_image)

with mp_face_detection.FaceDetection(
 
    min_detection_confidence=0.5, model_selection=1) as face_detection:
 
  for name, image in full_range_images.items():
    # 将BGR图像转换为RGB，并使用MediaPipe人脸检测进行处理。
    results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    # 绘制每个人脸的检测。
    print(f'Face detections of {name}:')
 
    if not results.detections:
 
      continue
 
    annotated_image = image.copy()
 
    for detection in results.detections:
 
      mp_drawing.draw_detection(annotated_image, detection)
 
    resize_and_show(annotated_image)