def wrapper(video_path, city_name, location_name, minm_area, roi):
# vo = TrafficCongestion(video_path, city_name, location_name)
# vo.congestion(video_path, minm_area=minm_area,roi=roi,plot_intermediate=False)
vo = VehicleCounter(video_path, city_name, location_name)
vo.vehicle_counter(minm_area=minm_area,
roi=roi,
plot_intermediate=True,
check_interval=10,
method='quantile',
aggregation_time='3T',
congestion_threshold=0.20)
def main():
bg_subtractor = cv2.createBackgroundSubtractorMOG2()
car_counter = None # Will be created after first frame is captured
# Set up image source
#cap = cv2.VideoCapture("flow.mp4")
cap = cv2.VideoCapture(URL)
while True:
ret, frame = cap.read()
if not ret:
print 'failed'
else:
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
#car_counter = VehicleCounter(frame.shape[:2], frame.shape[1] / 2)
car_counter = VehicleCounter(frame.shape[:2], DIVIDER1,
DIVIDER2, DIVIDER3, DIVIDER4,
DIVIDER5, DIVIDER6)
#print frame.shape
# Archive raw frames from video to disk for later inspection/testing
processed = process_frame(frame, bg_subtractor, car_counter)
#cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
c = cv2.waitKey(10)
if c == 27:
break
cap.release()
cv2.destroyAllWindows()
def main():
log = logging.getLogger('main')
log.debug('Creating backgound subtractor')
bg_subtractor = cv2.createBackgroundSubtractorKNN(detectShadows=False)
log.debug('Pre-training the background subtractor')
# default_bg = cv2.imread(IMAGE_FILENAME_FORMAT % 119)
# bg_subtractor.apply(default_bg, None, 1.0)
car_counter = None
log.debug('Initializing video capture device #%s')
cap = cv2.VideoCapture(url)
frame_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
log.debug('Video capture frame size=(w=%d, h=%d)', frame_width,
frame_height)
log.debug('Starting capture loop...')
frame_number = -1
while True:
frame_number += 1
log.debug('Capturing frame #%d', frame_number)
ret, frame = cap.read()
if not ret:
log.error('Frame capture failed, stopping')
break
log.debug('Got frame #%d: shape=%s', frame_number, frame.shape)
if not car_counter:
log.debug('Creating vehicle counter')
car_counter = VehicleCounter(frame.shape[:2], frame.shape[0] / 2)
log.debug('Processing frame #%d', frame_number)
processed = process_frame(frame_number, frame, bg_subtractor,
car_counter)
save_frame(IMAGE_DIR + '/processed_%04d.png', frame_number, processed,
'processed frame #%d')
log.debug('Frame #%d processed', frame_number)
if __name__ == '__main__':
cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
c = cv2.waitKey(WAIT_TIME)
if c == 27:
break
else:
cv2.imwrite('temp.jpg', processed)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' +
open('temp.jpg', 'rb').read() + b'\r\n')
log.debug('Closing video capture device')
cap.release()
cv2.destroyAllWindows()
def main():
bg_subtractor = cv2.createBackgroundSubtractorMOG2()
fgbg = cv2.createBackgroundSubtractorMOG2()
car_counter = None # Will be created after first frame is captured
# Set up image source
cap = cv2.VideoCapture('v3.mp4')
#cap = cv2.VideoCapture(URL)
while True:
ret, frame = cap.read()
if not ret:
print('failed')
else:
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
#car_counter = VehicleCounter(frame.shape[:2], frame.shape[1] / 2)
car_counter = VehicleCounter(frame.shape[:2], DIVIDER1,
DIVIDER2, DIVIDER3, DIVIDER4,
DIVIDER5, DIVIDER6)
#print frame.shape
# Archive raw frames from video to disk for later inspection/testing
rows, cols, _ = frame.shape
#-----------------------------------
kernel = np.ones((2, 2), np.uint8)
fgmask = fgbg.apply(frame)
opening = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)
#dilation = cv2.dilate(opening, kernel, iterations=1)
#-----------------------------------
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 90, 1)
dst = cv2.warpAffine(frame, M, (cols, rows))
dst2 = cv2.warpAffine(opening, M, (cols, rows))
processed = process_frame(dst, bg_subtractor, car_counter)
#M2 = cv2.getRotationMatrix2D((cols/2,rows/2),0,1)
#dst2 = cv2.warpAffine(processed,M2,(cols,rows))
#cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
cv2.imshow('fg', dst2)
c = cv2.waitKey(10)
if c == 27:
break
cap.release()
cv2.destroyAllWindows()
def main():
bg_subtractor = cv2.createBackgroundSubtractorMOG2()
car_counter = None # Will be created after first frame is captured
# Set up image source
cap = cv2.VideoCapture('s1.mp4')
#cap = cv2.VideoCapture(URL)
while True:
ret, frame = cap.read()
if not ret:
print('failed')
else:
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
#car_counter = VehicleCounter(frame.shape[:2], frame.shape[1] / 2)
car_counter = VehicleCounter(frame.shape[:2], DIVIDER1,
DIVIDER2, DIVIDER3, DIVIDER4,
DIVIDER5, DIVIDER6)
#print frame.shape
# Archive raw frames from video to disk for later inspection/testing
# rows,cols,_ = frame.shape
# M = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)
# dst = cv2.warpAffine(frame,M,(cols,rows))
processed = process_frame(frame, bg_subtractor, car_counter)
#M2 = cv2.getRotationMatrix2D((cols/2,rows/2),0,1)
#dst2 = cv2.warpAffine(processed,M2,(cols,rows))
#cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
c = cv2.waitKey(10)
if c == 27:
break
cap.release()
cv2.destroyAllWindows()
def main():
log = logging.getLogger("main")
#print (cv2.__version__)
log.debug("Creating background subtractor...")
bg_subtractor = cv2.bgsegm.createBackgroundSubtractorMOG()
log.debug("Pre-training the background subtractor...")
default_bg = cv2.imread(IMAGE_FILENAME_FORMAT % 119)
bg_subtractor.apply(default_bg, None, 1.0)
car_counter = None # Will be created after first frame is captured
# Set up image source
log.debug("Initializing video capture device #%s...", IMAGE_SOURCE)
cap = cv2.VideoCapture(IMAGE_SOURCE)
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
log.debug("Video capture frame size=(w=%d, h=%d)", frame_width, frame_height)
log.debug("Starting capture loop...")
frame_number = -1
x1 = 0
x2 = 0
y1 = 0
y2 = 0
while True:
frame_number += 1
log.debug("Capturing frame #%d...", frame_number)
ret, frame = cap.read()
for i in range(0,2,1):
ret, frame = cap.read()
if not ret:
log.error("Frame capture failed, stopping...")
break
#Redimensionando a imagem
#frame= cv2.resize(frame,(0,0),fx=0.3,fy=0.3)
#Girando a imagem 90 graus
frame = imutils.rotate_bound(frame,90)
'''if frame_number==0:
bbox = cv2.selectROI(frame, False)
#print (bbox)
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[0]) + int(bbox[2])
y2 = int(bbox[1]) + int(bbox[3])
print("[%d a %d] [%d a %d]" % (y1,y2,x1,x2))
print (frame.shape)
'''
#RAFAEL - PARAMETRO
#frame = frame[y1:y2,x1:x2] #output1.avi
#frame = frame[115:172,72:280] #output1.avi
#frame = frame[384:573,240:934] #output1.avi
#frame = frame[130:200,0:230] video1.avi
log.debug("Got frame #%d: shape=%s", frame_number, frame.shape)
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
log.debug("Creating vehicle counter...")
#RAFAEL - se tirar o /2 a imagem inteira é avaliada
car_counter = VehicleCounter(frame.shape[:2], int(frame.shape[0]/2))
# Archive raw frames from video to disk for later inspection/testing
if CAPTURE_FROM_VIDEO:
save_frame(IMAGE_FILENAME_FORMAT
, frame_number, frame, "source frame #%d")
log.debug("Processing frame #%d...", frame_number)
processed = process_frame(frame_number, frame, bg_subtractor, car_counter)
#Girar novamente as imagens
processed = imutils.rotate_bound(processed,-90)
frame = imutils.rotate_bound(frame,-90)
save_frame(IMAGE_DIR + "/processed_%04d.png"
, frame_number, processed, "processed frame #%d")
cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
log.debug("Frame #%d processed.", frame_number)
c = cv2.waitKey(WAIT_TIME)
if c == 27:
log.debug("ESC detected, stopping...")
break
print ("Carros: %d " % car_counter.car_count)
print ("Motos: %d" % car_counter.motocycle_count)
log.debug("Closing video capture device...")
cap.release()
cv2.destroyAllWindows()
log.debug("Done.")
def main():
log = logging.getLogger("main")
log.debug("Creating background subtractor...")
bg_subtractor = cv2.BackgroundSubtractorMOG()
log.debug("Pre-training the background subtractor...")
default_bg = cv2.imread(IMAGE_FILENAME_FORMAT % 119)
bg_subtractor.apply(default_bg, None, 1.0)
car_counter = None # Will be created after first frame is captured
# Set up image source
log.debug("Initializing video capture device #%s...", IMAGE_SOURCE)
cap = cv2.VideoCapture(IMAGE_SOURCE)
frame_width = cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)
frame_height = cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)
log.debug("Video capture frame size=(w=%d, h=%d)", frame_width,
frame_height)
log.debug("Starting capture loop...")
frame_number = -1
while True:
frame_number += 1
log.debug("Capturing frame #%d...", frame_number)
ret, frame = cap.read()
if not ret:
log.error("Frame capture failed, stopping...")
break
log.debug("Got frame #%d: shape=%s", frame_number, frame.shape)
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
log.debug("Creating vehicle counter...")
car_counter = VehicleCounter(frame.shape[:2], frame.shape[0] / 2)
# Archive raw frames from video to disk for later inspection/testing
if CAPTURE_FROM_VIDEO:
save_frame(IMAGE_FILENAME_FORMAT, frame_number, frame,
"source frame #%d")
log.debug("Processing frame #%d...", frame_number)
processed = process_frame(frame_number, frame, bg_subtractor,
car_counter)
save_frame(IMAGE_DIR + "/processed_%04d.png", frame_number, processed,
"processed frame #%d")
cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
log.debug("Frame #%d processed.", frame_number)
c = cv2.waitKey(WAIT_TIME)
if c == 27:
log.debug("ESC detected, stopping...")
break
log.debug("Closing video capture device...")
cap.release()
cv2.destroyAllWindows()
log.debug("Done.")
def main():
start = time.time()
log = logging.getLogger("main")
log.debug("Creating background subtractor...")
bg_subtractor = cv2.BackgroundSubtractorMOG()
log.debug("Pre-training the background subtractor...")
default_bg = cv2.imread(IMAGE_FILENAME_FORMAT % 1)
bg_subtractor.apply(default_bg, None, 1.0)
car_counter = None # Will be created after first frame is captured
# Set up image source
log.debug("Initializing video capture device #%s...", IMAGE_SOURCE)
cap = cv2.VideoCapture(IMAGE_SOURCE)
# Capture every TIME_INTERVAL seconds (here, TIME_INTERVAL = 5)
fps = cap.get(cv2.cv.CV_CAP_PROP_FPS) # Gets the frames per second
#multiplier = TIME_INTERVAL
log.debug("Update car counting by every %d ...", TIME_INTERVAL)
# Check Camera is open or not
if not cap.isOpened():
log.debug("The Camera is not open ...")
cap.open()
CV_CAP_PROP_FRAME_WIDTH = 3
CV_CAP_PROP_FRAME_HEIGHT = 4
if CAPTURE_FROM_STREAMING:
cap.set(CV_CAP_PROP_FRAME_WIDTH, 320);
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 320);
frame_width = cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)
frame_height = cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)
log.debug("Video capture frame size=(w=%d, h=%d)", frame_width, frame_height)
log.debug("Starting capture loop...\n")
frame_number = -1
while True:
frame_number += 1
log.debug("Capturing frame #%d...", frame_number)
ret, frame = cap.read()
if not ret:
log.error("Frame capture failed, stopping...")
break
log.debug("Got frame #%d: shape=%s", frame_number, frame.shape)
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
log.debug("Creating vehicle counter...")
car_counter = VehicleCounter(frame.shape[:2], frame.shape[0] / 2)
# Archive raw frames from video to disk for later inspection/testing
if CAPTURE_FROM_VIDEO and CAPTURE_FROM_STREAMING:
save_frame(IMAGE_FILENAME_FORMAT
, frame_number, frame, "source frame #%d")
log.debug("Processing frame #%d...", frame_number)
processed = process_frame(frame_number, frame, bg_subtractor, car_counter)
save_frame(IMAGE_DIR + "/processed_%04d.png"
, frame_number, processed, "processed frame #%d")
cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
log.debug("Frame #%d processed.\n", frame_number)
c = cv2.waitKey(WAIT_TIME)
if c == 27:
log.debug("ESC detected, stopping...")
break
during = frame_number / fps
log.debug("Closing video capture device...")
cap.release()
cv2.destroyAllWindows()
log.debug("Done.")
end = time.time()
return car_counter.vehicle_count,during
def main():
log = logging.getLogger("main")
log.debug("Creating background subtractor...")
###MUDADO....
bg_subtractor = cv2.bgsegm.createBackgroundSubtractorMOG()
# bg_subtractor = cv2.BackgroundSubtractorMOG2()
log.debug("Pre-training the background subtractor...")
default_bg = cv2.imread(IMAGE_FILENAME_FORMAT)
bg_subtractor.apply(default_bg, None, 1.0)
car_counter = None # Will be created after first frame is captured
# Set up image source
log.debug("Initializing video capture device #%s...", IMAGE_SOURCE)
cap = cv2.VideoCapture(IMAGE_SOURCE)
frame_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) #MUDEI
log.debug("Video capture frame size=(w=%d, h=%d)", frame_width, frame_height)
log.debug("Starting capture loop...")
frame_number = -1
while True:
frame_number += 1
log.debug("Capturing frame #%d...", frame_number)
ret, frame = cap.read()
if not ret:
log.error("Frame capture failed, stopping...")
break
frame= cv2.resize(frame,(0,0),fx=0.3,fy=0.3)
#RAFAEL - PARAMETRO
frame = frame[115:172,72:280] #output1.avi
#frame = frame[384:573,240:934] #output1.avi
#frame = frame[130:200,0:230] video1.avi
log.debug("Got frame #%d: shape=%s", frame_number, frame.shape)
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
log.debug("Creating vehicle counter...")
#RAFAEL - se tirar o /2 a imagem inteira é avaliada
#print frame.shape[0]/2
car_counter = VehicleCounter(frame.shape[:2], frame.shape[0]/2)
# Archive raw frames from video to disk for later inspection/testing
##MUDADO
# if CAPTURE_FROM_VIDEO:
# save_frame(IMAGE_FILENAME_FORMAT
# , frame_number, frame, "source frame #%d")
log.debug("Processing frame #%d...", frame_number)
processed = process_frame(frame_number, frame, bg_subtractor, car_counter)
save_frame(IMAGE_DIR + "/processed_%04d.png"
, frame_number, processed, "processed frame #%d")
cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
log.debug("Frame #%d processed.", frame_number)
c = cv2.waitKey(WAIT_TIME)
if c == 27:
log.debug("ESC detected, stopping...")
break
print ("Carros: %d " % car_counter.car_count)
print ("Motos: %d" % car_counter.motocycle_count)
log.debug("Closing video capture device...")
cap.release()
cv2.destroyAllWindows()
log.debug("Done.")
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
fps = FPS().start()
vehicle_count = VehicleCounter(exit_masks=EXIT_PTS)
d = {}
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=400)
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
0.007843, (300, 300), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
def main():
log = logging.getLogger('main')
# creating exit mask from points, where we will be counting our vehicles
base = np.zeros(SHAPE + (3, ), dtype='uint8')
exit_mask = cv2.fillPoly(base, EXIT_PTS, (255, 255, 255))[:, :, 0]
stream = None
# produce a stabilized video
if args.stabilize_video == 'yes':
cap = cv2.VideoCapture(args.video_source)
stabilize_frames(cap, log)
return
else:
stream = cv2.VideoCapture(args.video_source)
stream.set(cv2.CAP_PROP_FRAME_WIDTH, SHAPE[1])
stream.set(cv2.CAP_PROP_FRAME_HEIGHT, SHAPE[0])
writer = VideoWriter('detected.mp4', (SHAPE[1], SHAPE[0]))
bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500,
detectShadows=True)
# skipping 500 frames to train bg subtractor
train_bg_subtractor(bg_subtractor, stream, num=500)
pipeline = PipelineRunner(
pipeline=[
ContourDetection(bg_subtractor=bg_subtractor,
save_image=False,
image_dir=IMAGE_DIR),
# we use y_weight == 2.0 because traffic are moving vertically on video
# use x_weight == 2.0 for horizontal.
# VehicleCounter(exit_masks=[exit_mask], y_weight=2.0),
VehicleCounter(),
Visualizer(image_dir=IMAGE_DIR),
CsvWriter(path='./', name='report.csv')
],
log_level=logging.DEBUG)
_frame_number = -1
frame_number = -1
while True:
(grabbed, frame) = stream.read()
if not frame.any():
log.error("Frame capture failed, stopping...")
break
# real frame number
_frame_number += 1
# skip every 2nd frame to speed up processing
if _frame_number % 2 != 0:
continue
# frame number that will be passed to pipline
# this needed to make video from cutted frames
frame_number += 1
pipeline.set_context({
'frame': frame,
'frame_number': frame_number,
})
new_context = pipeline.run()
cv2.imshow('Video', new_context['frame'])
writer(new_context['frame'])
if cv2.waitKey(1) & 0xFF == ord('q'):
break