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Mind+python 防“近视”灯

【项目背景】
坐在电脑前，时间长了，眼睛不免有些看不清，就愿意往前凑，越来越近。有网友在电脑前使用超声波检测人与屏幕的距离，这可能会受到肢体的干扰。而电脑一般都配有摄像头，这个设备我们可以利用起来。
【项目设计】
在Mind+Python模式下，通过mediapipe库检测人脸，提取在摄像头的宽度值，以此判断与屏幕的距离。通过Pinpong库控制Arduino，用LED灯提醒。并pynput库，模拟键盘和鼠标操作，“Ctrl+鼠标滚轮”放大和缩小网面上的文字。
【技术亮点】
使用Python中列表的“队列”功能。
队列，其实就是一个先进先出的线性表，只能在队首执行删除操作，在队尾执行插入操作。用列表表示队列，可以用append()方法实现在队尾插入元素，用pop(0)方法实现在队首删除元素。
【人脸检测】通过mediapipe库，进行人脸检测，并找到人脸的宽：detection.location_data.relative_bounding_box.width



import cv2
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils

# For webcam input:
cap = cv2.VideoCapture(0)
with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detection:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = face_detection.process(image)

    # Draw the face detection annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.detections:
      for detection in results.detections:
        mp_drawing.draw_detection(image, detection)
      print(detection.location_data.relative_bounding_box.width)
    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()
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【亮灯提醒】

通过mediapipe人脸检测，人脸在摄像头上会出现近大远小的情况，以脸在摄像头中的宽度来判断，人眼是否离屏幕过近。当超过阈值时（过近），LED提醒。



import cv2
import mediapipe as mp
from pinpong.board import Board,NeoPixel,Pin

Board("uno").begin()
NEOPIXEL_PIN = Pin(Pin.D2)
PIXELS_NUM = 60 #灯数
np = NeoPixel(NEOPIXEL_PIN,PIXELS_NUM)

mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
np.clear()
# For webcam input:
cap = cv2.VideoCapture(0)
with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detection:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = face_detection.process(image)

    # Draw the face detection annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.detections:
      for detection in results.detections:
        mp_drawing.draw_detection(image, detection)
      if detection.location_data.relative_bounding_box.width>0.4:
          np.rainbow(0,60,0,0x00FF00)
      else:
          np.clear()    
    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()
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【模拟放大操作】
通过pynput库，模拟键盘和鼠标操作，“Ctrl+鼠标滚轮”放大和缩小网面上的文字。


import pynput
import time
keyboard =pynput.keyboard.Controller()
key=pynput.keyboard.Key
mouse = pynput.mouse.Controller()
time.sleep(10)
keyboard.press(key.ctrl)
mouse.scroll(0, 50)
keyboard.release(key.ctrl)
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【完整代码】


import cv2
import mediapipe as mp
from pinpong.board import Board,NeoPixel,Pin
from pykeyboard import *
import pynput
import numpy as npy
import time
Board("uno").begin()
NEOPIXEL_PIN = Pin(Pin.D2)
PIXELS_NUM = 60 #灯数
np = NeoPixel(NEOPIXEL_PIN,PIXELS_NUM)
np.clear()
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
keyboard =pynput.keyboard.Controller()
key=pynput.keyboard.Key
mouse = pynput.mouse.Controller()
scrollnum=25
bs=0
# For webcam input:
cap = cv2.VideoCapture(0)
scroll=[]
bs1=0
with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detection:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = face_detection.process(image)

    # Draw the face detection annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.detections:
      for detection in results.detections:
        mp_drawing.draw_detection(image, detection)
        width=detection.location_data.relative_bounding_box.width
      if bs1<10:
          bs1=bs1+1
          scroll.append(width)
      else:
          scroll.append(width)
          scroll.pop(0)
          width=npy.mean(scroll)
          if width>0.5:
            np.rainbow(0,60,0,0x00FF00)
            bs=1
          elif bs==1:
            bs=0
            np.clear()
          else:
            width=int(width*100/10)
            if width!=scrollnum:
              temp=-width+scrollnum
              keyboard.press(key.ctrl)
              mouse.scroll(0, temp*10)#向上滚动50单位
              keyboard.release(key.ctrl)
              scrollnum=width
              print(width)

    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()
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