def main(video_file='soccer.avi', roi=((140, 100), (500, 600))):
# open video file
if path.isfile(video_file):
video = cv2.VideoCapture(video_file)
else:
print 'File "' + video_file + '" does not exist.'
raise SystemExit
# initialize tracker
mot = MultipleObjectsTracker()
while True:
# grab next frame
success, img = video.read()
if success:
if roi:
# original video is too big: grab some meaningful ROI
img = img[roi[0][0]:roi[1][0], roi[0][1]:roi[1][1]]
# generate saliency map
sal = Saliency(img, use_numpy_fft=False, gauss_kernel=(3, 3))
cv2.imshow('original', img)
cv2.imshow('saliency', sal.get_saliency_map())
cv2.imshow('objects', sal.get_proto_objects_map(use_otsu=False))
cv2.imshow('tracker', mot.advance_frame(img,
sal.get_proto_objects_map(use_otsu=False)))
if cv2.waitKey(100) & 0xFF == ord('q'):
break
else:
break
def main(video_file='soccer.avi', roi=((140, 100), (500, 600))):
# open video file
if path.isfile(video_file):
video = cv2.VideoCapture(video_file)
else:
print 'File "' + video_file + '" does not exist.'
raise SystemExit
# initialize tracker
mot = MultipleObjectsTracker()
while True:
# grab next frame
success, img = video.read()
if success:
if roi:
# original video is too big: grab some meaningful ROI
img = img[roi[0][0]:roi[1][0], roi[0][1]:roi[1][1]]
# generate saliency map
sal = Saliency(img, use_numpy_fft=False, gauss_kernel=(3, 3))
cv2.imshow('original', img)
cv2.imshow('saliency', sal.get_saliency_map())
cv2.imshow('objects', sal.get_proto_objects_map(use_otsu=False))
cv2.imshow(
'tracker',
mot.advance_frame(img,
sal.get_proto_objects_map(use_otsu=False)))
if cv2.waitKey(100) & 0xFF == ord('q'):
break
else:
break
def main(video_file='jet.mp4', region=((0, 0), (350, 450))):
# open video file
if path.isfile(video_file):
video = cv2.VideoCapture(video_file)
else:
print 'File "' + video_file + '" does not exist.'
raise SystemExit
# initialize tracker
mot = MultipleObjectsTracker()
while True:
# grab next frame
success, img = video.read()
if success:
if region:
#original video is too big: grab some meaningful region of interest
img = img[region[0][0]:region[1][0], region[0][1]:region[1][1]]
# generate saliency map
sal = Saliency(img)
cv2.imshow('original', img)
cv2.imshow('saliency', sal.get_saliency_map())
cv2.imshow('objects', sal.get_proto_objects_map())
cv2.imshow('tracker',
mot.advance_frame(img, sal.get_proto_objects_map()))
if cv2.waitKey(100) & 0xFF == ord('q'):
break
else:
break
def get_saliency_map(arr):
size = arr.shape[0] * arr.shape[1]
sali = Saliency(arr)
m = sali.get_saliency_map()
sali_stats = getstats(m)
cnt = (m > 0.80).sum()
cnt2 = (m > 0.9).sum()
cnt3 = (m < 0.1).sum()
cnt4 = (m < 0.2).sum()
return sali_stats + [
cnt, cnt / size, cnt2, cnt2 / size, cnt3, cnt3 / size, cnt4,
cnt4 / size
]
def sal_image(frame_queue, sal_queue, queue_lock):
print "sal_image called"
frame = 0
while True:
if not frame_queue.empty():
queue_lock.acquire()
img = frame_queue.get()
queue_lock.release()
sal = Saliency(img)
sal_map = sal.get_saliency_map()
sal_img = (sal_map*255).round().astype(np.uint8)
img = cv2.cvtColor(sal_img, cv2.COLOR_GRAY2BGR)
queue_lock.acquire()
if sal_queue.qsize() < 3:
sal_queue.put(img)
else:
time.sleep(1)
queue_lock.release()
frame = frame + 1
print "SAL _ FRAME : ", frame
import time
BUFFER = 40
# read an image
img_original = cv2.imread("test_image.jpg")
img_grayscale = cv2.cvtColor(img_original, cv2.COLOR_BGR2GRAY)
# img_sized = cv2.resize(img_original,(1920,1080))
img_sized = cv2.resize(img_original, (1280, 720))
img_size_exclusion = img_sized.copy()
img_hist = img_sized.copy()
start = time.time()
# find the saliency map
sal = Saliency(img_sized)
sal_map = sal.get_saliency_map()
end = time.time()
test_duration = end - start
# convert to the correct type
sal_conv = (sal_map * 255).round().astype(np.uint8)
# find the contours
ret, thresh = cv2.threshold(sal_conv, 100, 255, 0)
_, contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
color = ("b", "r", "g")
for cnt in contours:
