def visualizeTracks(total_frames, videoFile, reSize=None):
fno = 0
completed_tracks = []
root = ET.Element("Traffic")
static_plane = np.zeros((2880, 5120, 3), dtype=float)
vehicleDetectionFile = videoFile.split('.')[0] + "_predict.txt"
#print vehicleDetectionFile
cap = cv2.VideoCapture(videoFile)
visual = np.zeros((1), np.uint8)
color = np.random.randint(0, 255, (100, 3))
tracks = []
for fno in range(total_frames):
print "Frameno: ", fno, len(completed_tracks)
#pdb.set_trace()
#print "frame number - " + str(fno)
#print "Completed Tracks ", len(completed_tracks)
#print "total tracks length " + str(len(tracks) + len(completed_tracks))
ret, frames = cap.read()
cv2.putText(frames, str(fno), (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75,
(255, 0, 0), 1, cv2.CV_AA)
if reSize != None:
frames = misc.imresize(frames, reSize)
if visual.shape == (1, ):
visual = np.zeros(frames.shape, np.uint8)
vehicles, vehicleTypes = getVehiclesFromFile(vehicleDetectionFile,
fno) #Frame number
print " number of vehicles ", vehicles.shape
if vehicles.shape == (3, 1) or vehicles.shape[0] == 0:
continue
actual_vehicles = []
print vehicles.shape
for i, vehicle in enumerate(vehicles):
#print vehicle
if vehicle[2] > 100 or vehicle[3] > 100:
continue
actual_vehicles.append(vehicle)
actual_vehicles = filterVehicles(actual_vehicles)
actual_vehicles = np.array(actual_vehicles)
trackNo = [0] * (actual_vehicles.shape[0])
hungarianMatrix, hungarianAssignment = getHungary(
actual_vehicles, tracks, frames)
tracks, trackNo, completed_tracks = updateTracks(
hungarianAssignment, tracks, actual_vehicles, vehicleTypes,
hungarianMatrix, trackNo, completed_tracks)
'''
getTransformedTracks
getTransformedVehicles
'''
frames = drawVehicles(frames, actual_vehicles, trackNo)
frames, tracks, visual = printTracks(frames, tracks, visual)
root = store_tracks_xml(tracks, root, fno)
#print fno, vehicles.shape[0], len(completed_tracks) , len(tracks)
cv2.imshow("Frames", frames)
#drawTracks(visual, tracks)
if cv2.waitKey(33) == 27:
break
cv2.destroyAllWindows()
tree = ET.ElementTree(root)
tree.write("traffic.xml")
def visualizeTracks(total_frames, videoFile, reSize):
#cap = cv2.VideoCapture("../data/Trimmed_trafficVideo.mp4");
#folder = "../output/Trimmed_trafficVideo_flow_output/tv"+str(fno)+"-"+str(fno+1)+".flo"
fno = 0
folder = "../output/" + videoFile.split('/')[-1].split(
'.')[0] + "_flow_output/"
vehicleDetectionFile = videoFile.split('.')[0] + "_predict.txt"
#print vehicleDetectionFile
cap = cv2.VideoCapture(videoFile)
visual = np.zeros(reSize, np.uint8)
color = np.random.randint(0, 255, (100, 3))
tracks = []
for fno in range(total_frames):
ret, frames = cap.read()
frames = misc.imresize(frames, reSize)
vehicles, _ = getVehiclesFromFile(vehicleDetectionFile,
fno) #Frame number
print("Vehicles detected:-")
print(vehicles.shape)
#if fno == 0:
# vehicles = vehicles[4:5]
#else:
# vehicles = np.array([])
frames = drawVehicles(frames, vehicles)
flow, w, h = getDenseflowPreComputed(
folder + str(fno) + "-" + str(fno + 1) +
".flo") #Get the flow from the frame
#print "Flow information "
#print flow.shape, w, h
newTracks = []
# if the vehicle centers dont lie in any of the tracks create a new object
count = 0
for vehicle in vehicles:
prevTrack = vehicleAlreadyTracked(vehicle, tracks)
if prevTrack is not None:
count += 1
#print prevTrack.track, " Previous track ", vehicle
prevTrack.w = vehicle[2]
prevTrack.h = vehicle[3]
#v.updateObj(vehicle)
else:
newTracks.append(
Track(vehicle[1], vehicle[0], vehicle[2], vehicle[3]))
print "Count of already things " + str(count)
tracks = updateTracksOfExistingObjects(flow, tracks, reSize)
#print "updating the tracks is done"
print "New tracks = ", len(newTracks)
tracks = tracks + newTracks
frames, tracks, visual = printTracks(frames, tracks, visual)
cv2.imshow("Frames", frames)
drawTracks(visual, tracks)
#pdb.set_trace()
if cv2.waitKey(33) == 27:
break
#cv2.waitKey(0)
cv2.destroyAllWindows()
def visualizeTracks(total_frames, videoFile, reSize):
fno = 0
completed_tracks = []
static_plane = np.zeros((2880, 5120, 3), dtype=float)
vehicleDetectionFile = videoFile.split('.')[0] + "_predict.txt"
#print vehicleDetectionFile
cap = cv2.VideoCapture(videoFile)
visual = np.zeros(reSize, np.uint8)
color = np.random.randint(0, 255, (100, 3))
tracks = []
for fno in range(total_frames):
#pdb.set_trace()
#print "frame number - " + str(fno)
#print "Completed Tracks ", len(completed_tracks)
#print "total tracks length " + str(len(tracks) + len(completed_tracks))
ret, frames = cap.read()
frames = misc.imresize(frames, reSize)
vehicles, _ = getVehiclesFromFile(vehicleDetectionFile,
fno) #Frame number
#print " number of vehicles ", vehicles.shape
if vehicles.shape == 3:
break
trackNo = [0] * (vehicles.shape[0])
hungarianMatrix, hungarianAssignment = getHungary(vehicles, tracks)
tracks, trackNo = updateTracks(hungarianAssignment, tracks, vehicles,
hungarianMatrix, trackNo,
len(completed_tracks))
tracks, ctracks = postProcessTracks(tracks)
completed_tracks += ctracks
frames = drawVehicles(frames, vehicles, trackNo)
frames, tracks, visual = printTracks(frames, tracks, visual)
cv2.imshow("Frames", frames)
drawTracks(visual, tracks)
if cv2.waitKey(33) == 27:
break
cv2.destroyAllWindows()
def visualizeTracks(total_frames, videoFile, reSize):
fno = 0
folder = "../output/" + videoFile.split('/')[-1].split(
'.')[0] + "_flow_output/"
vehicleDetectionFile = videoFile.split('.')[0] + "_predict.txt"
#print vehicleDetectionFile
cap = cv2.VideoCapture(videoFile)
visual = np.zeros(reSize, np.uint8)
color = np.random.randint(0, 255, (100, 3))
tracks = []
for fno in range(total_frames):
ret, frames = cap.read()
frames = misc.imresize(frames, reSize)
vehicles, _ = getVehiclesFromFile(vehicleDetectionFile,
fno) #Frame number
oldvsnew = [0] * (vehicles.shape[0])
print("Vehicles detected:-")
print(vehicles.shape)
#if fno == 0:
# vehicles = vehicles[4:5]
#else:
# vehicles = np.array([])
flow, w, h = getDenseflowPreComputed(
folder + "tv" + str(fno) + "-" + str(fno + 1) +
".flo") #Get the flow from the frame
newTracks = []
#pdb.set_trace()
# if the vehicle centers dont lie in any of the tracks create a new object
count = 0
for i, vehicle in enumerate(vehicles):
if vehicle[2] > 100 or vehicle[
3] > 100: ## Taking the permissable limit as 100
continue
#some = np.zeros(reSize);
#cv2.circle(some,(vehicle[1],vehicle[0]),2,(0,0,255),-1)
prevTrack = vehicleAlreadyTracked(vehicle, tracks)
if prevTrack is not None:
#cv2.rectangle(some,((int(round(prevTrack.init_x-prevTrack.width/2))),int(round(prevTrack.init_y+prevTrack.height/2))),(int(round(prevTrack.init_x+prevTrack.width/2)),int(round(prevTrack.init_y-prevTrack.height/2))),(0,0,255),2)
#some[prevTrack.track[-1][0]][prevTrack.track[-1][1]] = [255,255,0]
count += 1
oldvsnew[i] = 1
#print prevTrack.track, " Previous track ", vehicle
prevTrack.w = vehicle[2]
prevTrack.h = vehicle[3]
else:
newTracks.append(
Track(
vehicle[0], vehicle[1], vehicle[2],
vehicle[3])) ## x axis is up/down- h, y is right/left
#cv2.imshow("Some", some)
if cv2.waitKey(33) == 27:
break
#pdb.set_trace()
print "Count of already things " + str(count)
frames = drawVehicles(frames, vehicles, oldvsnew)
tracks = updateTracksOfExistingObjects(flow, tracks, reSize)
#print "updating the tracks is done"
print "New tracks = ", len(newTracks)
tracks = tracks + newTracks
frames, tracks, visual = printTracks(frames, tracks, visual)
cv2.imshow("Frames", frames)
drawTracks(visual, tracks)
if cv2.waitKey(33) == 27:
break
#cv2.waitKey(0)
cv2.destroyAllWindows()
return frame
fno = 0
frame0 = 1
tracks = []
vehicleDetectionFile = "../data/Trimmed_trafficVideo_predict.txt"
total_frames = 1000
visual = np.zeros((384, 512, 3), np.uint8)
videoFile = "/Users/gramaguru/ComputerVision/computer-vision/trafficSimulation/data/Trimmed_trafficVideo.mp4"
cap = cv2.VideoCapture('../data/Trimmed_trafficVideo.mp4')
color = np.random.randint(0, 255, (100, 3))
for fno in range(total_frames):
ret, frames = cap.read()
frames = misc.imresize(frames, (384, 512, 3))
vehicles, _ = getVehiclesFromFile(vehicleDetectionFile, fno) #Frame number
print("Vehicles detected:-")
print(vehicles.shape)
frames = drawVehicles(frames, vehicles)
flow, w, h = getDenseflowPreComputed(
"../output/Trimmed_trafficVideo_flow_output/tv" + str(fno) + "-" +
str(fno + 1) + ".flo") #Get the flow from the frame
print "Flow information "
print flow.shape, w, h
newTracks = []
# if the vehicle centers dont lie in any of the tracks create a new object
for vehicle in vehicles:
prevTrack = vehicleAlreadyTracked(vehicle)
if prevTrack is not None:
#print prevTrack.track, " Previous track ", vehicle
prevTrack.w = vehicle[2]
vis[:h2, w1:w1+w2] = img2
cv2.imshow("combined", vis)
# capture frames from a video
cap = cv2.VideoCapture("trip10/trip10_trimmed.mp4");
cap = cv2.VideoCapture("Trimmed_trafficVideo.mp4");
frameNumber = 0
if True:
# loop runs if capturing has been initialized.
# reads frames from a video
ret, frames = cap.read()
#vehicles = getVehiclesHarCascades(frames);
#vehicles, labels = getVehiclesYolo(frames);
vehicles, labels = getVehiclesFromFile(frameNumber)
print vehicles.shape, " shape of the vehicles";
i = 0;
print labels
print type(labels)
for (x,y,w,h) in vehicles:
#print "Vehicle dim" , x,y,w,h
#cv2.rectangle(frames,(int(round(x)),int(round(y))),(int(round(x+w)),int(round(y+h))),(0,0,255),2)
cv2.rectangle(frames,(int(round((x-w/2))),int(round(y+h/2))),(int(round(x+w/2)),int(round(y-h/2))),(0,0,255),2)
cv2.putText(frames, labels[i][0], (int(round(x)), int(round(y))), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255,0,0), 1, cv2.CV_AA);
i += 1
cv2.imshow('frame', frames)
frameNumber += 1
# static_plane = cv2.resize(static_plane,(int(round(0.25*static_plane.shape[0])),int(round(0.25*static_plane.shape[1]))))
# cv2.imshow('static_plane', static_plane)
#highlighting those points on the plane
frames = drawPloy(frames, other_road)
# Apply homographic transformation from the road points to the 4 selected points and see how it is, use the destination image as static_image and then see, this is using the library
H, status = cv2.findHomography(road_points, output_points)
#static_plane = warpPerspectiveForward(frames, static_plane, H, 1)
#static_plane = cv2.warpPerspective(frames, H,(4*frames.shape[1], 4*frames.shape[0]))
#static_plane = warpPerspective(frames, static_plane, H, 1)
static_plane = transformSetOfPoints(road_points, static_plane, H,
(0, 255, 0))
#vehicles = getVehiclesHarCascades(frames);
#vehicles = getVehiclesYolo(frames);
vehicleDetectionFile = videoFile.split('.')[0] + "_predict.txt"
print vehicleDetectionFile
vehicles, _ = getVehiclesFromFile(vehicleDetectionFile, frameNumber)
print vehicles.shape, " shape of the vehicles"
for (x, y, w, h) in vehicles:
print "Vehicle dim", x, y, w, h
newPoint = np.array([x, y])
vehicle_points = np.vstack([vehicle_points, newPoint])
cv2.rectangle(frames, (int(round(x)), int(round(y))),
(int(round(x + w)), int(round(y + h))), (0, 0, 255), 2)
# adding vehicles
static_plane = transformSetOfPoints(vehicle_points, static_plane, H,
(255, 0, 0))
transformed_pts = transformPoly(other_road, H)
static_plane = drawPloy(static_plane, transformed_pts)
showSideBySide(frames, static_plane, 0.25, 0.5)
#cv2.imshow('frame', frames)