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a guest May 25th, 2019 88 Never
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  1. #Bibliotheken einbinden
  2. import RPi.GPIO as GPIO
  3. import time
  4. from picamera.array import PiRGBArray
  5. from picamera import PiCamera
  6. import cv2
  7. import numpy as np
  8. from PIL import Image
  9.  
  10. while True:
  11.     #GPIO Modus (BOARD / BCM)
  12.     GPIO.setmode(GPIO.BCM)
  13.    
  14.     #GPIO Pins zuweisen
  15.     trigger = 18   #BCM 18
  16.     echo = 24      #BCM 24
  17.  
  18.     #Richtung der GPIO-Pins festlegen (IN/OUT)
  19.     GPIO.setup(trigger, GPIO.OUT)
  20.     GPIO.setup(echo, GPIO.IN)
  21.  
  22.     #define function
  23.     #Funktion um die Distanz zu messen
  24.     def distance():
  25.         # setz Trigger auf HIGH
  26.         GPIO.output(trigger, True)
  27.    
  28.         #setzt Trigger nach 0.01ms auf LOW
  29.         time.sleep(0.00001)
  30.         GPIO.output(trigger, False)
  31.    
  32.         startTime = time.time()
  33.         stopTime = time.time()
  34.    
  35.         #speichere startTime
  36.         while GPIO.input(echo) == 0:
  37.             startTime = time.time()
  38.    
  39.         #speichere Ankunftszeit
  40.         while GPIO.input(echo) == 1:
  41.             stopTime = time.time()
  42.    
  43.         #Zeit Differenz zwischen Start und Ankunft
  44.         TimeElapsed = stopTime - startTime
  45.         #mit der Schallgeschwindigkeit (34300 cm/s) multiplizieren
  46.         #und durch 2 teilen, da hin und zurueck
  47.         distance = (TimeElapsed * 34300) / 2
  48.         return distance
  49.  
  50.     #Funktion um ein Bild aufzunehmen
  51.     def capture():
  52.         camera = PiCamera()
  53.         rawCapture = PiRGBArray(camera)
  54.        
  55.         time.sleep(0.1) #gibt der Kamera Zeit zum starten
  56.  
  57.         camera.capture(rawCapture, format="bgr")
  58.         return rawCapture.array
  59.  
  60.         width = 736/2
  61.         height = 480/2
  62.  
  63.         cv2.rectangle(image,(int(width-width/2),int(height-height/2)),(int(width+width/2),int(height+height/2)),(0,0,0),-1)
  64.  
  65.     abstand = distance()
  66.     abstand = round(abstand, 0)
  67.     print ('Entfernung = ' +str(abstand) +'cm', abstand)
  68.     default_distance = 35
  69.  
  70.     if default_distance != abstand:
  71.         ledPin = 17     #BCM 17
  72.         uvPin = 27      #BCM 27
  73.  
  74.         #GPIO.setwarnings(False)
  75.         GPIO.setmode(GPIO.BCM)
  76.         #Richtung der GPIO-Pins festlegen (IN/OUT)
  77.         GPIO.setup(ledPin, GPIO.OUT)
  78.         GPIO.setup(uvPin, GPIO.OUT)
  79.  
  80.         GPIO.output(ledPin, GPIO.HIGH)
  81.         print("LED on")
  82.         time.sleep(1)
  83.            
  84.         #Bildaufnahme Bild
  85.         image = capture()
  86.         cv2.imwrite('/home/sandra/image.jpg',image)
  87.         time.sleep(3)
  88.        
  89.         GPIO.output(ledPin, GPIO.LOW)
  90.         time.sleep(1)
  91.            
  92.            
  93.         GPIO.output(uvPin, GPIO.HIGH)
  94.         print("UV-LED on")
  95.         time.sleep(1)
  96.            
  97.         #Bildaufnahme UV-Bild
  98.         uv = capture()
  99.         cv2.imwrite('/home/sandra/uv.jpg',uv)
  100.         time.sleep(3)
  101.        
  102.         GPIO.output(uvPin, GPIO.LOW)        
  103.            
  104.         print("Pictures captured")
  105.         GPIO.cleanup()
  106.        
  107.         break
  108.        
  109.     else:
  110.         print('nothing')
  111.         time.sleep(1)
  112.  
  113.  
  114. #Bildauswertung
  115.  
  116. #Bild wird leicht verunschärft um eindeutigere Umrisse zu bekommen
  117. blur = cv2.bilateralFilter(image,9,75,75)
  118. cv2.imwrite('/home/sandra/blur.jpg',blur)
  119.  
  120. #das Bild wird in Grautöne umgewandelt
  121. gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
  122. cv2.imwrite('/home/sandra/gray.jpg',gray)
  123.  
  124. #das in Grautöne unterteilte Bild wir zu einem schwarz-weiß Bild umgewandelt
  125. thresh = 70
  126. bw = cv2.threshold(gray, thresh, 255, cv2.THRESH_BINARY)[1]
  127. cv2.imwrite('bw.jpg', bw)
  128.    
  129. #invertiert die Farben eines Bildes
  130. #invert = cv2.bitwise_not(bw)
  131. #cv2.imwrite('invert.jpg', invert)
  132.        
  133.  
  134. #UV
  135.        
  136. #UV-Bild wird leicht verunschärft um eindeutigere Umrisse zu bekommen
  137. uvblur = cv2.bilateralFilter(uv,9,75,75)
  138. cv2.imwrite('/home/sandra/uvblur.jpg',uvblur)
  139.  
  140. #das UV-Bild wird in Grautöne umgewandelt
  141. uvgray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
  142. cv2.imwrite('/home/sandra/uvgray.jpg',uvgray)
  143.  
  144. #das in Grautöne unterteilte UV-Bild wir zu einem schwarz-weiß Bild umgewandelt
  145. uvbw = cv2.threshold(uvgray, thresh, 255, cv2.THRESH_BINARY)[1]
  146. cv2.imwrite('uvbw.jpg', uvbw)
  147.  
  148. #Maske mit Umrisse wird über das UV-Bild gelegt
  149. uvnew = np.zeros(image.shape, np.uint8)
  150. uvlayer = cv2.bitwise_or(uvimage, uvnew, mask=uvbw)
  151. cv2.imwrite('/home/sandra/uvlayer.jpg',uvlayer)
  152.        
  153. #ändert im UV-Bild alles schwarze zu rot
  154. uvred = cv2.imread('uvlayer.jpg')
  155. uvred[np.where((colour == [0,0,0]).all(axis = 2))] = [0,33,166] #BGR
  156. cv2.imwrite('uvred.jpg', uvred)
  157.  
  158.  
  159. #Maske mit Umrisse wird über das bearbeitete UV-Bild gelegt
  160. new = np.zeros(image.shape, np.uint8)
  161. final = cv2.bitwise_or(uvred, new, mask=bw)
  162. cv2.imwrite('/home/sandra/final.jpg',final)
  163.  
  164. print("edited")
  165.  
  166. whole = 0
  167. black = 0
  168. red = 0
  169. hand = 0
  170. clean = 0
  171.  
  172. for pixel in final.getdata():
  173.     whole += 1
  174.     if pixel == (0, 0, 0): #schwarz # if your image is RGB (if RGBA, (0, 0, 0, 255) or so
  175.         black += 1
  176.     else:
  177.         if pixel == (166, 33, 0): #rot
  178.             red += 1
  179.  
  180. hand = whole-black
  181. clean = hand-red
  182.  
  183.  
  184. print('[in pixel] hand: ' +str(hand) +'clean:' +str(clean) +', dirty: ' +str(red))
  185.  
  186. clean = (clean/hand)*100
  187. red = (red/hand)*100
  188.  
  189. print('[in Prozent] clean: ' +str(clean) +'% , dirty: ' +str(red) +'%')
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