f = 1
fcount = 1
while success:
success, img = vidcap.read()
if success is False:
vidcap.set(cv2.CAP_PROP_POS_FRAMES, 0)
break
if f in [25, 35, 45, 55, 65]:
# print img.type
#my_mat_img = npcv.test_np_mat(img)
#m2 = cv2.CreateMat(height, width, cv2.CV_32FC3)
#m0 = cv2.fromarray(img)
#cv2.CvtColor(m0,m2, cv2.CV_GRAY2BGR)
my_lap_obj = fv(width, height, 4, sigma_x, sigma_y)
ctr = npcv.test_np_mat(np.array(center))
foveated_img = my_lap_obj.foveate(img, ctr)
foveated_img = cv2.resize(
foveated_img, (224, 224),
interpolation=cv2.INTER_NEAREST)
# BGR to RGB
rgb_img = foveated_img[:, :, ::-1].copy()
if not os.path.exists(outdirvidkey + "/frames" +
str(fcount) + ".jpg"):
Image.fromarray(rgb_img).save(outdirvidkey +
"/frames" +
str(fcount) + ".jpg")
ctr = None
# Save image
fcount += 1
sigma_x = int(2 * current_BB[2] / resrefactor[0])
sigma_y = int(2 * current_BB[3] / resrefactor[1])
frame_path = frame_dir + str(current_frame) + ".jpg"
img = cv2.imread(frame_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (myres[0], myres[1]))
height, width, channels = img.shape
img = plt.imread(
frame_path) # CHECK THAT IMG IS GETTING RESIZED
img = cv2.resize(img, (myres[0], myres[1]))
my_lap_obj = fv(width, height, 3, sigma_x, sigma_y)
foveated_img = my_lap_obj.foveate(
img, npcv.test_np_mat(np.array(center)))
plt.cla()
# Check that output Folder Exists, if not create it
BB_counter = 0
outdir = target_dir + video_dir + '/' + str(
frames[2]) + '_BB_' + str(current_BB_number)
if not os.path.isdir(outdir):
os.makedirs(outdir)
outimgpath = outdir + '/frame' + str(counter).zfill(
1) + '.jpg'
# Convert BGR to RGB
#rgb_img = foveated_img[:, :, ::-1].copy()
Image.fromarray(foveated_img).save(outimgpath)
counter = counter + 1
sigma_y = sigma_y_max
# Create the Laplacian blending object
my_lap_obj = fv(width, height, levels, sigma_x, sigma_y)
try:
while True:
start = time.time()
sigma_x = randint(1, sigma_x_max)
sigma_y = randint(1, sigma_y_max)
# RANDOM FIXATION POINTS
center = [int(width / 2.0), int(height / 2.0)]
# Convert np array to cv::Mat object
my_mat_img = npcv.test_np_mat(img)
# RANDOM FOVEA SIZE
#my_lap_obj.update_fovea(width,height,sigma_x,sigma_y)
# Foveate the image
foveated_img = my_lap_obj.foveate(img,
npcv.test_np_mat(np.array(center)))
end = time.time()
print(end - start)
# Display the foveated image
plt.imshow(foveated_img)
#img.set_data(im)