207 lines
8.5 KiB
Python
207 lines
8.5 KiB
Python
import cv2
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import numpy as np
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import vehicles
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import time
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counter_up=0 #licznik pojazdow jadacych w gore
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counter_down=0 #licznik pojazdow jadacych w dol
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while True:
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user_choice = input("Wybierz nagranie:\n1. video_1.mp4\n2. video_2.avi\n3. video_3.mp4\n")
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if user_choice == "1":
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input_video = 'data/video_1.avi'
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vid_name = 'video_1'
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kernelSize = 6
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break
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elif user_choice == "2":
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input_video = 'data/video_2.mp4'
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vid_name = 'video_2'
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kernelSize = 3
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break
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elif user_choice == "3":
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input_video = 'data/video_3.mp4'
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vid_name = 'video_3'
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kernelSize = 3
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break
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cap = cv2.VideoCapture(input_video)
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height, width, frames_count, fps = cap.get(cv2.CAP_PROP_FRAME_HEIGHT), cap.get(
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cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_COUNT), cap.get(cv2.CAP_PROP_FPS),
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height = int(height)
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width = int(width)
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frameArea = height*width
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print("Wysokosc", height, "szerokosc", width, "liczba klatek", frames_count, "fps",fps)
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font = cv2.FONT_HERSHEY_SIMPLEX
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"""
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#Polozenie linii "mety" (na srodku kadru)
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lineUpper = int(4*(height/10))
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lineLower = int(6*(height/10))
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upLimit = int(2*(height/10))
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downLimit = int(8*(height/10))
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"""
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#Polozenie linii "mety" (w dolnej czesci kadru)
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lineUpper = int(7*(height/10))
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lineLower = int(8*(height/10))
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upLimit = int(4*(height/10))
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downLimit = int(10*(height/10))
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print("Zielona linia y:",str(lineLower))
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print("Czerwona linia y:",str(lineUpper))
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lineLower_color = (0,255,0)
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lineUpper_color = (0,0,255)
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point1 = [0, lineLower]
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point2 = [width, lineLower]
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points_lineLower = np.array([point1,point2], np.int32)
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# Przekształcenie tablicy do 1x2, polecane w dokumentacji OpenCV ale chyba zbedne w tym przypadku
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#points_lineLower = points_lineLower.reshape((-1,1,2))
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point3 = [0, lineUpper]
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point4 = [width, lineUpper]
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points_LineUpper = np.array([point3,point4], np.int32)
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#points_LineUpper = points_LineUpper.reshape((-1,1,2))
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point5 = [0, upLimit]
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point6 = [width, upLimit]
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points_upLimit = np.array([point5,point6], np.int32)
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#points_upLimit = points_upLimit.reshape((-1,1,2))
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point7 = [0, downLimit]
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point8 = [width, downLimit]
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points_downLimit= np.array([point7,point8], np.int32)
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#points_downLimit = points_downLimit.reshape((-1,1,2))
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sub=cv2.createBackgroundSubtractorMOG2() # utworz background subtractor
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size = 2.0 #wielkosc obrazu
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cars = []
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maxAllowedAge = 3 #przez ile klatek "zgubiony" pojazd bedzie pozostawal na liscie do "sledzenia"
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id = 1
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while(cap.isOpened()):
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ret, frame = cap.read()
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for i in cars:
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i.age_one()
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if ret == True: #przetwarzanie kadru
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#cv2.imshow("bez zmian", frame) # wyswietlanie filmu wejsciowego bez zmian
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#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#cv2.imshow("czarnobialy", gray) # wyswietlanie filmu w skali szarosci
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fgMask = sub.apply(frame) # uzycie background subtraction
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#fgMask2 = sub.apply(frame)
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#cv2.imshow("fgMask", fgMask)
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# operacje morfologiczne. Wg tutoriala na docs.opencv.org. Opening, closing - usuwanie szumów
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#Binarization
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ret,imBinary = cv2.threshold(fgMask,200,255,cv2.THRESH_BINARY)
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#cv2.imshow("binarny", imBinary)
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kernelOpening = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernelSize, kernelSize)) # kernels to apply to the morphology
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kernelClosing = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
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#Opening is just another name of erosion followed by dilation.
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#It is useful in removing noise
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mask = cv2.morphologyEx(imBinary,cv2.MORPH_OPEN,kernelOpening)
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#cv2.imshow("opening", mask)
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#Closing is reverse of Opening, Dilation followed by Erosion.
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#It is useful in closing small holes inside the foreground objects, or small black points on the object.
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mask = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernelClosing)
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#cv2.imshow("closing", mask)
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# kontury (obrysowywanie)
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contours,hierarchy=cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) # RETR_EXTERNAL - bierz pod uwage najbardziej 'zewnetrzne' kontury
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# kontury zajmujace zbyt duza lub mala czesc kadru beda ignorowane
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maxContourSize = frameArea / 4
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minContourSize = frameArea / 400
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for j in range(len(contours)): # przejdz wszystkie kontury w kadrze
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area = cv2.contourArea(contours[j])
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#print("wielkosc konturu:",area)
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if minContourSize < area < maxContourSize: # zbyt duze i male obiekty sa ignorowane
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#### Sledzenie konturow ######
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centroid = contours[j]
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moments = cv2.moments(centroid)
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cx = int(moments['m10'] / moments['m00'])
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cy = int(moments['m01'] / moments['m00'])
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# punkty graniczne (bounding points) konturu. x,y to wspolrzedne lewego gornego rogu, w,h to szerokosc i wysokosc prostokata
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x, y, w, h = cv2.boundingRect(centroid)
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new = True #przyjmij ze kontur to nowy pojazd
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if cy in range(upLimit,downLimit): # sprawdz czy jest wart sledzenia (czyli miedzy szarymi liniami limitujacymi)
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for i in cars:
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# jesli odnajdziesz sledzony kontur o bardzo zblizonych koordynatach,
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#przymij ze to ten sam i zaktualizuj jego polozenie
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if abs(x - i.getX()) <= w and abs(y - i.getY()) <= h: #abs function calculates an absolute value of each matrix element.
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new = False
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i.updateCoords(cx, cy)
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#sprawdz czy prekroczyl mete
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if i.going_UP(lineUpper) == True:
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counter_up+=1
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print("Pojazd o ID:",i.getId(),'przekroczyl linie mety w gore o czasie:', time.strftime("%c"))
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elif i.going_DOWN(lineLower) == True:
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counter_down+=1
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print("Pojazd o ID:", i.getId(), 'przekroczyl linie mety w dol o czasie:', time.strftime("%c"))
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break
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#jesli kierunek ruchu jest znany i linia mety zostala przekroczona, przestan sledzic i usun z listy
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if i.getState() == '1':
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if i.getDir() == 'down'and i.getY() > downLimit:
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i.setDone()
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elif i.getDir() == 'up'and i.getY() < upLimit:
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i.setDone()
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if i.timedOut():
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index=cars.index(i)
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cars.pop(index)
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del i
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if new == True: #Jesli kontur nie byl dotychczas sledzony, dodaj nowy samochod do listy
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newCar=vehicles.Car(id,cx,cy,maxAllowedAge)
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cars.append(newCar)
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id+=1
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cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
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#for i in cars:
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#cv2.putText(frame, str(i.getId()), (i.getX(), i.getY()), font, 0.3, i.getRGB(), 1, cv2.LINE_AA) #wypisywanie id
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str_up='W GORE: '+str(counter_up)
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str_down='W DOL: '+str(counter_down)
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frame = cv2.polylines(frame,[points_lineLower],False,lineLower_color,thickness=2)
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frame = cv2.polylines(frame,[points_LineUpper],False,lineUpper_color,thickness=2)
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frame = cv2.polylines(frame,[points_upLimit],False,(255,255,255),thickness=1)
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frame = cv2.polylines(frame,[points_downLimit],False,(255,255,255),thickness=1)
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cv2.putText(frame, str_up, (10, 40), font, 0.4, (255, 255, 255), 2, cv2.LINE_AA) #biale tlo zeby licznik byl wyrazniejszy
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cv2.putText(frame, str_up, (10, 40), font, 0.4, lineUpper_color, 1, cv2.LINE_AA)
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cv2.putText(frame, str_down, (10, 90), font, 0.4, (255, 255, 255), 2, cv2.LINE_AA) #biale tlo zeby licznik byl wyrazniejszy
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cv2.putText(frame, str_down, (10, 90), font, 0.4, lineLower_color, 1, cv2.LINE_AA)
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#frame = cv2.resize(frame, (0, 0), None, size, size) # rozmiar obrazu x2
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cv2.imshow('Frame',frame)
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key = cv2.waitKey(30)
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if key == 27: # Wyjdz po nacisnieciu escape
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break
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else:
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break
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timestr = time.strftime("%Y_%m_%d-%H_%M_%S")
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f = open("traffic logs/"+vid_name+" "+timestr+".txt","w+")
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f.write("Natężenie ruchu:\r\nliczba samochodów jadących w górę: %d\rliczba samochodów jadących w dół: %d" %(counter_up, counter_down))
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cap.release()
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cv2.destroyAllWindows()
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