def run_foa(sample, gt):
# Import image
test_image = foai.ImageObject(sample, rgb=False)
test_image.ground_truth = gt
# Generate Gaussian Blur Prior - Time ~0.0020006
foveation_prior = foac.matlab_style_gauss2D(test_image.modified.shape, 300)
# Generate Gamma Kernel
k = np.array([1, 9], dtype=float)
mu = np.array([0.2, 0.5], dtype=float)
kernel = foac.gamma_kernel(mask_size=(14, 14), k=k, mu=mu)
# plt.imshow(kernel)
# Generate Saliency Map
foac.convolution(test_image, kernel, foveation_prior)
# Bound and Rank the most Salient Regions of Saliency Map
foas.salience_scan(test_image, rank_count=3, bbox_size=(28, 28))
test_image.draw_image_patches()
test_image.plot_main()
test_image.plot_patches()
plt.show()
return test_image
def run_foa(sample, prior, kernel, bbox=(28, 28)):
# Import image
img = foai.ImageObject(sample, rgb=False)
# Generate Saliency Map
foac.convolution(img, kernel, prior)
# Bound and Rank the most Salient Regions of Saliency Map
foas.salience_scan(img, rank_count=1, bbox_size=bbox)
img.draw_image_patches()
# img.plot_main()
# Pad patches to be the same size (28x28)
patch = img.patch_list[0]
if (patch.shape != bbox):
pad = np.zeros(bbox)
pad[:patch.shape[0], :patch.shape[1]] = patch
patch = pad
return patch.reshape(1, bbox[0], bbox[1])
def run_foa_svhn(input=SVHNImage, tf=int, gt=None, k=None, mu=None):
assert (input.image is not None)
img = np.array(input.image)
x_dim = img.shape[0]
y_dim = img.shape[1]
print(np.array(img.shape))
# Load Image Object
img = foai.ImageObject(img)
img.ground_truth = gt
# Generate Gaussian Blur Prior - Time ~0.0020006
foveation_prior = foac.matlab_style_gauss2D(img.modified.shape, 300)
# Generate Gamma Kernel
# k = np.array([1, 26, 1, 25, 1, 19], dtype=float)
# mu = np.array([2, 2, 1, 1, 0.5, 0.5], dtype=float)
k = np.array([1, 5, 1, 9, 1, 13], dtype=float)
mu = np.array([0.8, 0.7, 0.3, 0.5, 0.1, 0.3], dtype=float)
kernel = foac.gamma_kernel(img, mask_size=(32, 32), k=k, mu=mu)
# Generate Saliency Map
start = time.time()
foac.convolution(img, kernel, foveation_prior)
stop = time.time()
print(f"Salience Map Generation: {stop - start} seconds")
# Bound and Rank the most Salient Regions of Saliency Map
foas.salience_scan(img, rank_count=4, bbox_size=(y_dim // 4, y_dim // 4))
bbt = 2
if (x_dim < 100 and y_dim < 100):
bbt = 1
img.draw_image_patches(bbt=bbt)
# Threshold
img.salience_map = np.where(img.salience_map > tf, img.salience_map, 0)
img.plot_main()
img.plot_patched_map()
x, y = np.where(gt > 0)
for i in zip(x, y):
temp.append(
np.log2(eps + smap[i[0], i[1]]) -
np.log2(eps + baseline[i[0], i[1]]))
return np.mean(temp)
# Main Routine
if __name__ == '__main__':
plt.close('all')
# Open test images as 8-bit RGB values - Time ~0.0778813
file = "./SMW_Test_Image.png"
mario = foai.ImageObject(file)
file = "../datasets/AIM/eyetrackingdata/original_images/22.jpg"
banana = foai.ImageObject(file)
file = "../datasets/AIM/eyetrackingdata/ground_truth/d22.jpg"
gt_banana = Image.open(file)
gt_banana.load()
file = "../datasets/AIM/eyetrackingdata/original_images/120.jpg"
corner = foai.ImageObject(file)
file = "../datasets/AIM/eyetrackingdata/ground_truth/d120.jpg"
gt_corner = Image.open(file)
gt_corner.load()
# Test Image
test_image = corner
test_image.ground_truth = np.array(gt_corner, dtype=np.float64)
os.makedirs(dir_path, exist_ok=True)
# Read first frame
success, image = movie.read()
print("FIRST READ SUCCESS: ", success)
# Get image dimensions: Mario - 224x256x3
height, width, channels = image.shape
print("H: ", height, "W: ", width, "C: ", channels)
start = time.time()
# Generate Gaussian Blur Prior - Time ~0.0020006
prior = foac.matlab_style_gauss2D(image.shape, 300)
# Generate Gamma Kernel
image_curr = foai.ImageObject(image, fc=frame)
image_prev = image_curr
image_prev.salience_map = np.zeros(image_prev.original.shape[:-1])
kernel = foac.gamma_kernel(image_curr)
# Step through each movie frame
while success:
frame += 1
image_name = str(frame) + ext
image_curr = foai.ImageObject(image, fc=frame)
# Generate Saliency Map with Gamma Filter
foac.convolution(image_curr, kernel, prior)
if (frame % 100 == 0):
stop = time.time()
plt.rcParams.update({'font.size': 22})
#image_set = np.array([False])
# Main Routine
if __name__ == '__main__':
plt.close('all')
# Load image set data
if (image_set.any() == False):
print("Importing data...")
image_set = np.load("./STATE0.npy")
# Test Image
test_image = image_set[0]
cv2.imwrite("0.png", test_image)
test_image = foai.ImageObject(cv2.imread("0.png"))
os.remove("0.png")
# %% Generate Gaussian and Gamma Kernel
# Generate Gaussian Blur Prior - Time ~0.0020006
foveation_prior = foac.matlab_style_gauss2D(test_image.modified.shape, 300)
# Generate Gamma Kernel
kernel = foac.gamma_kernel(test_image)
# %% Process Images
image_height = image_set.shape[1]
image_width = image_set.shape[2]
rankCount = 10 # Number of maps scans to run
processed_data = np.empty(