lern-tracking-system/raspberry-pi/sensors/opticalSensor.py

#!/bin/python3

import time
import threading
import traceback
import numpy as np
import cv2
import cv2.aruco as aruco

aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_50)

def saveMarkers():
  image = np.zeros((400,400), np.uint8)
  image[:,:] = (255)

  image[ 50: 50 + 100, 50: 50 + 100] = aruco_dict.drawMarker(0, 100)
  image[-50 - 100:-50, 50: 50 + 100] = aruco_dict.drawMarker(1, 100)
  image[ 50: 50 + 100,-50 - 100:-50] = aruco_dict.drawMarker(2, 100)
  image[-50 - 100:-50,-50 - 100:-50] = aruco_dict.drawMarker(3, 100)

  cv2.imwrite("markers.png", image)

parameters =  aruco.DetectorParameters_create()

def find_marker(image):
    # Our operations on the frame come here
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters)
  
  markers = [None] * 4

  if not corners:
    return markers

  for corner, id in zip(corners, ids.flatten()):
    # draw the bounding box of the ArUCo detection
    c = corner[0]
    cv2.line(image, tuple(c[0]), tuple(c[1]), (0, 255, 0), 2)
    cv2.line(image, tuple(c[1]), tuple(c[2]), (0, 255, 0), 2)
    cv2.line(image, tuple(c[2]), tuple(c[3]), (0, 255, 0), 2)
    cv2.line(image, tuple(c[3]), tuple(c[0]), (0, 255, 0), 2)
    cv2.putText(image, str(id), (int(c[0][0]), int(c[0][1]) - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    cX = sum(c[:,0]) / 4
    cY = sum(c[:,1]) / 4
    cv2.circle(image, (int(cX), int(cY)), 4, (0, 0, 255), -1)

    if id < 4:
      markers[id] = (cX, cY)

  return markers

def measureDistances(image):
    markers = find_marker(image)

    if markers[0] and markers[1]:
      cv2.line(image, (int(markers[0][0]),int(markers[0][1])), (int(markers[1][0]),int(markers[1][1])), (255, 0, 0), 2)
      
      length = np.sqrt((markers[0][0]-markers[1][0])**2 + (markers[0][1]-markers[1][1])**2)
      middle = abs(markers[0][0]-markers[1][0])

      return middle, length

    else:
      return None

class OpticalSensor():
  def __init__(self):
    self.cap = cv2.VideoCapture(0)
    self._t  = None
    self.values = None

  def start(self):
    if not self._t:
      self._t = threading.Thread(target=self._getFrames, args=())
      self._t.daemon = True  # thread dies when main thread (only non-daemon thread) exits.
      self._t.start()

  def _getFrames(self):
    while True:
      success, image = self.cap.read()
      if success:
        self.values = measureDistances(image)
        print("opt:", self.values)

  def calibrate(self, x, y):
    pass

  def read(self):
    return self.values

if __name__ == "__main__":
  # opt = OpticalSensor()
  # opt.start()
  # while True:
  #   time.sleep(1)
  cap = cv2.VideoCapture(0) 
  while True:
    success, image = cap.read()
    markers = find_marker(image)

    if markers[0] and markers[1]:
      cv2.line(image, (int(markers[0][0]),int(markers[0][1])), (int(markers[1][0]),int(markers[1][1])), (255, 0, 0), 2)
      length = np.sqrt((markers[0][0]-markers[1][0])**2 + (markers[0][1]-markers[1][1])**2)

      cv2.putText(image, "%.2fpx" % (length), (int(markers[0][0]), int(markers[0][1])), cv2.FONT_HERSHEY_SIMPLEX,
        1.0, (255, 255, 0), 3)
    cv2.imshow("image", image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
      break