def main():
options = json.loads(os.environ['SMILER_PARAMETER_MAP'])
network_string = options.get('network', 'SAM-VGG')
use_default_center_bias = options.get('center_prior',
'default') == 'default'
do_default_smoothing = options.get('do_smoothing', 'default') == 'default'
imgs_test_path = '/opt/input_vol/'
output_folder = '/opt/output_vol/'
os.makedirs(output_folder, exist_ok=True)
x = Input((3, shape_r, shape_c))
x_maps = Input((nb_gaussian, shape_r_gt, shape_c_gt))
if network_string == "SAM-VGG":
m = Model(input=[x, x_maps], output=sam_vgg([x, x_maps]))
print("Compiling SAM-VGG...")
m.compile(RMSprop(lr=1e-4),
loss=[kl_divergence, correlation_coefficient, nss])
print("Loading SAM-VGG weights...")
m.load_weights('weights/sam-vgg_salicon_weights.pkl')
elif network_string == "SAM-ResNet":
m = Model(input=[x, x_maps], output=sam_resnet([x, x_maps]))
print("Compiling SAM-ResNet...")
m.compile(RMSprop(lr=1e-4),
loss=[kl_divergence, correlation_coefficient, nss])
print("Loading SAM-ResNet weights...")
m.load_weights('weights/sam-resnet_salicon_weights.pkl')
else:
raise NotImplementedError(
"The only supported network strings are SAM-VGG and SAM-ResNet! '{}' is unknown."
.format(network_string))
gaussian = np.zeros((b_s, nb_gaussian, shape_r_gt, shape_c_gt))
def compute_saliency(image_path):
if use_default_center_bias:
predictions = m.predict(
[preprocess_images([image_path], shape_r, shape_c),
gaussian])[0]
else:
predictions = m.predict(
[preprocess_images([image_path], shape_r, shape_c),
gaussian])[0]
original_image = cv2.imread(image_path, 0)
res = postprocess_predictions(
predictions[0][0],
original_image.shape[0],
original_image.shape[1],
do_default_smoothing=do_default_smoothing)
return res
run_model(compute_saliency)
def compute_SAMSal(rgbvid, filename):
# Check if currently exists
outname, ext = os.path.splitext(filename)
outname += '_sam.npy'
print('Checking for', outname)
if os.path.exists(outname):
salmap = np.load(outname)
if salmap.shape[-1] == rgbvid.shape[-1]:
print('file found')
return salmap
print('Saliency Map length incorrect. Recomputing')
else:
print(outname, 'does not exist. Computing from scratch')
x = Input((3, shape_r, shape_c))
x_maps = Input((nb_gaussian, shape_r_gt, shape_c_gt))
m = Model(input=[x, x_maps], output=sam_vgg([x, x_maps]))
print("Compiling SAM-VGG")
m.compile(RMSprop(lr=1e-4),
loss=[kl_divergence, correlation_coefficient, nss])
nb_imgs_test = rgbvid.shape[-1]
if nb_imgs_test % b_s != 0:
print(
"The number of test images should be a multiple of the batch size. Please change your batch size in config.py accordingly."
)
exit()
print("Loading SAM-VGG weights")
m.load_weights('./sam/weights/sam-vgg_salicon_weights.pkl')
print("Predicting saliency maps")
predictions = m.predict_generator(generator_from_mat(b_s=b_s, vid=rgbvid),
nb_imgs_test)[0]
H, W, _, n = rgbvid.shape
output = np.zeros((H, W, n))
for i, pred in enumerate(predictions):
res = postprocess_predictions(pred[0], H, W)
output[..., i] = normalize(logsal(res))
#name = 'frame' + str(i) + '.png'
#original_image = cv2.imread(imgs_test_path + name, 0)
#res = postprocess_predictions(pred[0], original_image.shape[0], original_image.shape[1])
#cv2.imwrite(output_folder + '%s' % name, res.astype(int))
outname, ext = os.path.splitext(filename)
outname += '_sam.npy'
np.save(outname, output)
return output
def saliencyattentivemodel(inputimage):
x = Input((3, shape_r, shape_c))
x_maps = Input((nb_gaussian, shape_r_gt, shape_c_gt))
# version (0 for SAM-VGG and 1 for SAM-ResNet)
if version == 0:
m = Model(input=[x, x_maps], output=sam_vgg([x, x_maps]))
print("Compiling SAM-VGG")
m.compile(RMSprop(lr=1e-4),
loss=[kl_divergence, correlation_coefficient, nss])
elif version == 1:
m = Model(input=[x, x_maps], output=sam_resnet([x, x_maps]))
print("Compiling SAM-ResNet")
m.compile(RMSprop(lr=1e-4),
loss=[kl_divergence, correlation_coefficient, nss])
else:
raise NotImplementedError
# Output Folder Path
output_folder = 'predictions/'
nb_imgs_test = 1 #len(file_names)
if nb_imgs_test % b_s != 0:
print(
"The number of test images should be a multiple of the batch size. Please change your batch size in config.py accordingly."
)
exit()
if version == 0:
print("Loading SAM-VGG weights")
m.load_weights('weights/sam-vgg_salicon_weights.pkl')
elif version == 1:
print("Loading SAM-ResNet weights")
m.load_weights('weights/sam-resnet_salicon_weights.pkl')
predictions = m.predict_generator(
generator_test_singleimage(b_s=b_s, image=inputimage), nb_imgs_test)[0]
print("predictions:", predictions[0])
outname = 'SalmapFromWrapper.jpg'
original_image = image
res = postprocess_predictions(predictions[0], original_image.shape[0],
original_image.shape[1])
#mport pdb; pdb.set_trace()
cv2.imwrite(output_folder + '%s' % outname, res.astype(int))
cv2.imshow('salmap', res.astype('uint8'))
cv2.waitKey(0)
return res.astype('uint8')
file_names = ['{}.jpg'.format(f) for f in file_names]
video_frames = [f for f in file_names if video in f]
return video_frames
if __name__ == '__main__':
if len(sys.argv) == 1:
raise NotImplementedError
else:
phase = sys.argv[1]
x = Input((3, shape_r, shape_c))
x_maps = Input((nb_gaussian, shape_r_gt, shape_c_gt))
if version == 0:
m = Model(input=[x, x_maps], output=sam_vgg([x, x_maps]))
print("Compiling SAM-VGG")
m.compile(RMSprop(lr=1e-4),
loss=[kl_divergence, correlation_coefficient, nss])
elif version == 1:
m = Model(input=[x, x_maps], output=sam_resnet([x, x_maps]))
print("Compiling SAM-ResNet")
m.compile(RMSprop(lr=1e-4),
loss=[kl_divergence, correlation_coefficient, nss])
else:
raise NotImplementedError
if phase == 'train':
if nb_imgs_train % b_s != 0 or nb_imgs_val % b_s != 0:
print(
"The number of training and validation images should be a multiple of the batch size. Please change your batch size in config.py accordingly."
if __name__ == '__main__':
seed = 7
random.seed(seed)
test_data = []
mypath = sys.argv[1]
testing_images = [sys.argv[1] + "/" + f for f in os.listdir(mypath)]
testing_images.sort()
for image in testing_images:
data = {'image': image}
test_data.append(data)
x = Input(batch_shape=(1, im_res, im_res, 3))
m = Model(inputs=x, outputs=sam_vgg(x))
print("Loading weights")
m.load_weights('PAGE-Net.h5')
print("Making prediction")
# Output Folder Path
saliency_output = sys.argv[2]
print "Saving saliency files in " + saliency_output
if not os.path.exists(saliency_output):
os.makedirs(saliency_output)
for data in test_data:
Ximg, original_image, img_name = get_test(data)
predictions = m.predict(Ximg, batch_size=1)
res_saliency = postprocess_predictions(predictions[9][0, :, :, 0],
# p = [0.1]
# import tensorflow as tf
# p = tf.stack(p, name='ToFloat')
# g = tf.stack(g, name='ToFloat')
# k = weighted_crossentropy(g, p)
# w = losses.binary_crossentropy(g, p)
# sess = tf.Session()
# print(sess.run(k))
# print(sess.run(w))
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
if version == 0:
m = Model(inputs=img_input, outputs=sam_vgg(img_input))
# Load weights
weights_path = get_file(
'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
VGG_TF_WEIGHTS_PATH,
cache_subdir='models')
weights_path = 'weights.sam-vgg.05-196.3380.h5'
m.load_weights(weights_path, by_name=True)
print("Compili ng SAM-VGG")
m.compile(Adam(lr=1e-4),
loss=[
kl_divergence, kl_divergence, kl_divergence,
