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 __init__(self, cnn_layers=None, fc_layers=None):
super(Model, self).__init__()
# Network layer initialization
self.cnn_layers = cnn_layers
self.fc_layers = fc_layers
# Generate Gaussian Blur Prior - Time ~0.0020006
self.prior = foac.matlab_style_gauss2D((60, 60), 300)
# Generate Gamma Kernel
k = np.array([1, 9], dtype=float)
mu = np.array([0.2, 0.5], dtype=float)
self.kernel = foac.gamma_kernel(mask_size=(14, 14), k=k, mu=mu)
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()
mario = foai.imageObject(file)
file = "./AIM/eyetrackingdata/original_images/22.jpg"
banana = foai.imageObject(file)
file = "./AIM/eyetrackingdata/original_images/120.jpg"
corner = foai.imageObject(file)
# Test Image
testIMG = mario
# %% Generate Saliency Map
# Generate Gaussian Blur Prior - Time ~0.0020006
foveation_prior = foac.matlab_style_gauss2D(testIMG.modified.shape, 300)
# Generate Gamma Kernel
kernel = foac.gamma_kernel(testIMG)
# Generate Saliency Map
start = time.time()
foac.foa_convolution(testIMG, kernel, foveation_prior)
stop = time.time()
print("Salience Map Generation: ", stop - start, " seconds")
# Bound and Rank the most Salient Regions of Saliency Map
foas.salience_scan(testIMG, rankCount=5)
# %% Plot Results
# Plot Bounding Box Patches
testIMG.draw_image_patches()
testIMG.save_image_patches()
# Test Image
test_image = corner
test_image.ground_truth = np.array(gt_corner, dtype=np.float64)
# %% Generate Saliency Map
# 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, 26, 1, 25, 1, 19], dtype=float)
# mu = np.array([2, 2, 1, 1, 0.5, 0.5], dtype=float)
k = np.array([1, 20, 1, 30, 1, 40], dtype=float)
mu = np.array([2, 2, 2, 2, 2, 2], dtype=float)
kernel = foac.gamma_kernel(test_image, mask_size=(40, 40), k=k, mu=mu)
# Generate Saliency Map
start = time.time()
foac.convolution(test_image, 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(test_image, rank_count=5, bbox_size=(80, 80))
# %% Plot Results
test_image.plot_original_map()
test_image.plot_modified_map
test_image.plot_saliency_map()
test_image.plot_ground_truth()
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()
print("Salience Map Gen ", frame, ": ", stop - start, " seconds")
start = time.time()
mario = foai.ImageObject(file)
file = "./AIM/eyetrackingdata/original_images/22.jpg"
banana = foai.ImageObject(file)
file = "./AIM/eyetrackingdata/original_images/120.jpg"
corner = foai.ImageObject(file)
# Test Image
test_image = mario
# %% Generate Saliency Map
# 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)
# Generate Saliency Map
start = time.time()
foac.convolution(test_image, kernel, foveation_prior)
stop = time.time()
print("Salience Map Generation: ", stop - start, " seconds")
# Bound and Rank the most Salient Regions of Saliency Map
foas.salience_scan(test_image, rankCount=6)
# %% Plot Results
# Plot Bounding Box Patches
test_image.draw_image_patches()
