On Mon, Sep 30, 2019 at 6:10 AM zongoalbert ***@***.***> wrote: Hello Mr. Pageeac Thank you for sharing your Great project .My end of study project topic, is almost the same project like you did, it's about the vehicle speed detection at real time using Camera IP in a highway , I took a look in your code , I can't understand how you calculate the speed ,and how can you know the real distance from ( fps and distance between the camera and object ) ? Also i see you calculated the speed from L2R or R2L . It's possible to calculate the speed from front to behind F2B and from behind to front B2F? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#41?email_source=notifications&email_token=ABNPKZGUKR66XCWNUUXWUDLQMHGAXA5CNFSM4I3Y2Q42YY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4HOQEM3Q>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABNPKZC4R3JKTETRZLGBRGDQMHGAXANCNFSM4I3Y2Q4Q> .

On Mon, Sep 30, 2019 at 9:26 AM zongoalbert ***@***.***> wrote: Hii sir thank you for your feedback , : i relied on this PDF for the calculation of the speed "" https://pdfs.semanticscholar.org/efb3/7b45f4c9241c7d4ddf1295450dba6cf5cd06.pdf to calculate the speed"" you can find the code as below : `import cv2 import dlib import time import threading import math #carCascade = cv2.CascadeClassifier('myhaar.xml') video = cv2.VideoCapture('0') WIDTH = 1280 HEIGHT = 720 speed = K * d_pixels / time // ( time = 1 / fps ) between 2 frames def estimateSpeed(x1, x2): d_pixels = math.sqrt(math.pow(x2[0] - x1[0], 2) + math.pow(x2[1] - x1[1], 2)) K = 15.13 # K is coeficient in relation with FOV and angle of view , u can find how to calculate the speed in this link #https://pdfs.semanticscholar.org/efb3/7b45f4c9241c7d4ddf1295450dba6cf5cd06.pdf d_meters = d_pixels * K #print("d_pixels=" + str(d_pixels), "d_meters=" + str(d_meters)) fps = 18 speed = d_meters * fps * 3.6 return speed def trackMultipleObjects(): rectangleColor = (0, 255, 0) frameCounter = 0 currentCarID = 0 fps = 0 carTracker = {} carNumbers = {} carx1 = {} carx2 = {} speed = [None] * 1000 # Write output to video file out = cv2.VideoWriter('outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (WIDTH,HEIGHT)) while True: start_time = time.time() rc, image = video.read() if type(image) == type(None): break image = cv2.resize(image, (WIDTH, HEIGHT)) resultImage = image.copy() frameCounter = frameCounter + 1 carIDtoDelete = [] for carID in carTracker.keys(): trackingQuality = carTracker[carID].update(image) if trackingQuality < 7: carIDtoDelete.append(carID) for carID in carIDtoDelete: print ('Removing carID ' + str(carID) + ' from list of trackers.') print ('Removing carID ' + str(carID) + ' previous x.') print ('Removing carID ' + str(carID) + ' current x.') carTracker.pop(carID, None) carx1.pop(carID, None) carx2.pop(carID, None) if not (frameCounter % 10): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cars = carCascade.detectMultiScale(gray, 1.1, 13, 18, (24, 24)) for (_x, _y, _w, _h) in cars: x = int(_x) y = int(_y) w = int(_w) h = int(_h) x_bar = x + 0.5 * w y_bar = y + 0.5 * h matchCarID = None for carID in carTracker.keys(): trackedPosition = carTracker[carID].get_position() t_x = int(trackedPosition.left()) t_y = int(trackedPosition.top()) t_w = int(trackedPosition.width()) t_h = int(trackedPosition.height()) t_x_bar = t_x + 0.5 * t_w t_y_bar = t_y + 0.5 * t_h if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))): matchCarID = carID if matchCarID is None: print ('Creating new tracker ' + str(currentCarID)) tracker = dlib.correlation_tracker() tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h)) carTracker[currentCarID] = tracker carx1[currentCarID] = [x, y, w, h] currentCarID = currentCarID + 1 #cv2.line(resultImage,(0,480),(1280,480),(255,0,0),5) for carID in carTracker.keys(): trackedPosition = carTracker[carID].get_position() t_x = int(trackedPosition.left()) t_y = int(trackedPosition.top()) t_w = int(trackedPosition.width()) t_h = int(trackedPosition.height()) cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), rectangleColor, 4) # speed estimation carx2[carID] = [t_x, t_y, t_w, t_h] end_time = time.time() if not (end_time == start_time): fps = 1.0/(end_time - start_time) #cv2.putText(resultImage, 'FPS: ' + str(int(fps)), (620, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2) for i in carx1.keys(): if frameCounter % 1 == 0: [x1, y1, w1, h1] = carx1[i] [x2, y2, w2, h2] = carx2[i] # print 'previous x: ' + str(carx1[i]) + ', current x: ' + str(carx2[i]) carx1[i] = [x2, y2, w2, h2] # print 'new previous x: ' + str(carx1[i]) if [x1, y1, w1, h1] != [x2, y2, w2, h2]: if (speed[i] == None or speed[i] == 0) and y1 >= 275 and y1 <= 285: speed[i] = estimateSpeed([x1, y1, w1, h1], [x2, y2, w2, h2]) #if y1 > 275 and y1 < 285: if speed[i] != None and y1 >= 180: cv2.putText(resultImage, str(int(speed[i])) + " km/hr", (int(x1 + w1/2), int(y1-5)),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2) #print ('CarID ' + str(i) + ': speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n') #else: # cv2.putText(resultImage, "Far Object", (int(x1 + w1/2), int(y1)),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) #print ('CarID ' + str(i) + ' x1: ' + str(carx1[i]) + ' x2: ' + str(carx2[i]) + ' speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n') cv2.imshow('result', resultImage) # Write the frame into the file 'output.avi' #out.write(resultImage) if cv2.waitKey(33) == 27: break cv2.destroyAllWindows() if *name* == '*main*': trackMultipleObjects()` — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#41?email_source=notifications&email_token=ABNPKZHMVDABZ63VLGPFWBLQMH5CDA5CNFSM4I3Y2Q42YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD75UC7I#issuecomment-536559997>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABNPKZBOAUFJLS5ALRGLFS3QMH5CDANCNFSM4I3Y2Q4Q> .

On Mon, Sep 30, 2019 at 12:51 PM zongoalbert ***@***.***> wrote: Yes Sir, im using my laptop not RPI ,i tested it but the value of speed not true , as you see in this picture: [image: Screenshot from 2019-09-30 17-48-05] <https://user-images.githubusercontent.com/55978810/65898935-8497d800-e3aa-11e9-95be-0a57e51a6dae.png> like you see the value of speed indicated is 28 Kps but the real value is 54 Kps , Could explain how can i play with value of parameters to get the estimation speed. Thanks — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#41?email_source=notifications&email_token=ABNPKZDRCTL5426EVUHKSP3QMIU7PA5CNFSM4I3Y2Q42YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD76KDFA#issuecomment-536650132>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABNPKZELYCMAFBJKMHYTICTQMIU7PANCNFSM4I3Y2Q4Q> .