def saliency(image):
saliency_map = FasaSaliencyMapping(image.shape[0], image.shape[1])
image_salient_1 = saliency_map.returnMask(image,
tot_bins=8,
format='BGR2LAB')
image_salient = cv2.GaussianBlur(image_salient_1, (3, 3), 1)
image_salient = cv2.convertScaleAbs(image_salient)
return image_salient
def main():
# Open the video stream and set the webcam resolution.
# It may give problem if your webcam does not support the particular resolution used.
video_capture = cv2.VideoCapture(0)
video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, RESOLUTION_WIDTH)
video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, RESOLUTION_HEIGHT)
print video_capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)
print video_capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)
if (video_capture.isOpened() == False):
print("Error: the resource is busy or unvailable")
return
else:
print("The video source has been opened correctly...")
# Create the main window and move it
cv2.namedWindow('Video')
cv2.moveWindow('Video', 20, 20)
# Obtaining the CAM dimension
cam_w = int(video_capture.get(3))
cam_h = int(video_capture.get(4))
# Defining the FASA object using the camera resolution
my_map = FasaSaliencyMapping(cam_h, cam_w)
while True:
start = timer()
# Capture frame-by-frame
ret, frame = video_capture.read()
image_salient = my_map.returnMask(frame, tot_bins=8, format='BGR2LAB')
end = timer()
# Print the time for processing the frame
if PRINT_TIME:
print("--- %s Tot seconds ---" % (end - start))
print("")
cv2.imshow('Video', image_salient)
# Press Q to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def main():
# Open the video stream and set the webcam resolution.
# It may give problem if your webcam does not support the particular resolution used.
video_capture = cv2.VideoCapture(0)
video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, RESOLUTION_WIDTH)
video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, RESOLUTION_HEIGHT)
print video_capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)
print video_capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)
if(video_capture.isOpened() == False):
print("Error: the resource is busy or unvailable")
return
else:
print("The video source has been opened correctly...")
# Create the main window and move it
cv2.namedWindow('Video')
cv2.moveWindow('Video', 20, 20)
# Obtaining the CAM dimension
cam_w = int(video_capture.get(3))
cam_h = int(video_capture.get(4))
# Defining the FASA object using the camera resolution
my_map = FasaSaliencyMapping(cam_h, cam_w)
while True:
start = timer()
# Capture frame-by-frame
ret, frame = video_capture.read()
image_salient = my_map.returnMask(frame, tot_bins=8, format='BGR2LAB')
end = timer()
# Print the time for processing the frame
if PRINT_TIME:
print("--- %s Tot seconds ---" % (end - start))
print("")
cv2.imshow('Video', image_salient)
# Press Q to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def main():
image_1 = cv2.imread("./horse.jpg")
image_2 = cv2.imread("./car.jpg")
image_3 = cv2.imread("./plane.jpg")
image_4 = cv2.imread("./pear.jpg")
# for each image the same operations are repeated
my_map = FasaSaliencyMapping(image_1.shape[0], image_1.shape[1]) # init the saliency object
start = timer()
image_salient_1 = my_map.returnMask(image_1, tot_bins=8, format='BGR2LAB') # get the mask from the original image
image_salient_1 = cv2.GaussianBlur(image_salient_1, (3,3), 1) # applying gaussin blur to make it pretty
end = timer()
print("--- %s Image 1 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_2.shape[0], image_2.shape[1])
start = timer()
image_salient_2 = my_map.returnMask(image_2, tot_bins=8, format='BGR2LAB')
image_salient_2 = cv2.GaussianBlur(image_salient_2, (3,3), 1)
end = timer()
print("--- %s Image 2 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_3.shape[0], image_3.shape[1])
start = timer()
image_salient_3 = my_map.returnMask(image_3, tot_bins=8, format='BGR2LAB')
#image_salient_3 = cv2.GaussianBlur(image_salient_3, (3,3), 1)
end = timer()
print("--- %s Image 3 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_4.shape[0], image_4.shape[1])
start = timer()
image_salient_4 = my_map.returnMask(image_4, tot_bins=8, format='BGR2LAB')
image_salient_4 = cv2.GaussianBlur(image_salient_4, (3,3), 1)
end = timer()
print("--- %s Image 4 tot seconds ---" % (end - start))
# Creating stack of images and showing them on screen
original_images_stack = np.hstack((image_1, image_2, image_3, image_4))
saliency_images_stack = np.hstack((image_salient_1, image_salient_2, image_salient_3, image_salient_4))
saliency_images_stack = np.dstack((saliency_images_stack,saliency_images_stack,saliency_images_stack))
cv2.imshow("Original-Saliency", np.vstack((original_images_stack, saliency_images_stack)))
while True:
if cv2.waitKey(33) == ord('q'):
cv2.destroyAllWindows()
break
def main():
for i in range (512): # was 200
image_1 = cv2.imread("./horse.jpg")
image_2 = cv2.imread("./car.jpg")
image_3 = cv2.imread("./plane.jpg")
image_4 = cv2.imread("./pear.jpg")
# for each image the same operations are repeated
my_map = FasaSaliencyMapping(image_1.shape[0], image_1.shape[1]) # init the saliency object
start = timer()
image_salient_1 = my_map.returnMask(image_1, tot_bins=8, format='BGR2LAB') # get the mask from the original image
image_salient_1 = cv2.GaussianBlur(image_salient_1, (3,3), 1) # applying gaussin blur to make it pretty
end = timer()
#print("--- %s Image 1 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_2.shape[0], image_2.shape[1])
start = timer()
image_salient_2 = my_map.returnMask(image_2, tot_bins=8, format='BGR2LAB')
image_salient_2 = cv2.GaussianBlur(image_salient_2, (3,3), 1)
end = timer()
#print("--- %s Image 2 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_3.shape[0], image_3.shape[1])
start = timer()
image_salient_3 = my_map.returnMask(image_3, tot_bins=8, format='BGR2LAB')
#image_salient_3 = cv2.GaussianBlur(image_salient_3, (3,3), 1)
end = timer()
#print("--- %s Image 3 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_4.shape[0], image_4.shape[1])
start = timer()
image_salient_4 = my_map.returnMask(image_4, tot_bins=8, format='BGR2LAB')
image_salient_4 = cv2.GaussianBlur(image_salient_4, (3,3), 1)
end = timer()
#print("--- %s Image 4 tot seconds ---" % (end - start))
# Creating stack of images and showing them on screen
original_images_stack = np.hstack((image_1, image_2, image_3, image_4))
saliency_images_stack = np.hstack((image_salient_1, image_salient_2, image_salient_3, image_salient_4))
saliency_images_stack = np.dstack((saliency_images_stack,saliency_images_stack,saliency_images_stack))
cv2.imshow("Original-Saliency", np.vstack((original_images_stack, saliency_images_stack)))
cv2.destroyAllWindows()
def main():
i = 0
loopNum = 0
a = open('latency_start.csv', 'a')
a.write('start' + '\n')
b = open('latency_end.csv', 'a')
b.write('end' + '\n')
c = open('latency_time.csv', 'a')
c.write('time' + '\n')
d = open('latency_loop.csv', 'a')
d.write('loop' + '\n')
for i in range(10):
loopNum += 1
image_1 = cv2.imread("./horse.jpg")
image_2 = cv2.imread("./car.jpg")
image_3 = cv2.imread("./plane.jpg")
image_4 = cv2.imread("./pear.jpg")
# for each image the same operations are repeated
my_map = FasaSaliencyMapping(
image_1.shape[0], image_1.shape[1]) # init the saliency object
start = timer()
image_salient_1 = my_map.returnMask(
image_1, tot_bins=8,
format='BGR2LAB') # get the mask from the original image
image_salient_1 = cv2.GaussianBlur(
image_salient_1, (3, 3),
1) # applying gaussin blur to make it pretty
end = timer()
#print("--- %s Image 1 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_2.shape[0], image_2.shape[1])
start = timer()
image_salient_2 = my_map.returnMask(image_2,
tot_bins=8,
format='BGR2LAB')
image_salient_2 = cv2.GaussianBlur(image_salient_2, (3, 3), 1)
end = timer()
#print("--- %s Image 2 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_3.shape[0], image_3.shape[1])
start = timer()
image_salient_3 = my_map.returnMask(image_3,
tot_bins=8,
format='BGR2LAB')
#image_salient_3 = cv2.GaussianBlur(image_salient_3, (3,3), 1)
end = timer()
#print("--- %s Image 3 tot seconds ---" % (end - start))
my_map = FasaSaliencyMapping(image_4.shape[0], image_4.shape[1])
start = timer()
image_salient_4 = my_map.returnMask(image_4,
tot_bins=8,
format='BGR2LAB')
image_salient_4 = cv2.GaussianBlur(image_salient_4, (3, 3), 1)
end = timer()
#print("--- %s Image 4 tot seconds ---" % (end - start))
# Creating stack of images and showing them on screen
original_images_stack = np.hstack((image_1, image_2, image_3, image_4))
saliency_images_stack = np.hstack((image_salient_1, image_salient_2,
image_salient_3, image_salient_4))
saliency_images_stack = np.dstack(
(saliency_images_stack, saliency_images_stack,
saliency_images_stack))
t0 = timer()
cv2.imshow("Original-Saliency",
np.vstack((original_images_stack, saliency_images_stack)))
t1 = timer()
while True:
t4 = timer()
if cv2.waitKey(33) == ord('q'):
t2 = timer()
cv2.destroyAllWindows()
t3 = timer()
t5 = timer()
break
start = t1 - t0
end = t3 - t2
latency = t5 - t4
a.write(str(start) + '\n')
b.write(str(end) + '\n')
c.write(str(latency) + '\n')
d.write(str(loopNum) + '\n')
a.close()
b.close()
c.close()
d.close()