def main():
filepath = "/workspace2/kitti/testing/image_2/007481.png"
filepath = "test/test0_2.jpg"
filepath = "test/test1_2.jpg"
filepath = "test/noise3.jpg"
filepath = "test/adversary.png"
filepath = "/workspace2/kitti/testing/image_2/007489.png"
filepath = "test/single_generated.jpg"
filepath = "test/valid_adv7.png"
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=config)
images = tf.placeholder(tf.float32, [1, 150, 496, 3])
scdobj = scd.SCD(input=images)
scd_saver = scd.get_saver()
scd_saver.restore(sess, "/workspace/imgsynth/ssd_300_kitti/ssd_model.ckpt")
image = cv2.resize(scd.imread_as_jpg(filepath), (496, 150))
image = np.expand_dims(image, 0)
reses = scdobj.get_image(sess, image)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/single_detected%d.jpg" % i, a)
def main():
nf = 256
zdim = 1024
filepath = "/workspace2/kitti/testing/image_2/007481.png"
filepath = "test/test0_2.jpg"
filepath = "test/test1_2.jpg"
filepath = "test/noise3.jpg"
filepath = "test/adversary.png"
filepath = "/workspace2/kitti/training/image_2/000003.png"
filepath = "/workspace2/kitti/testing/image_2/007597.png"
filepath = "/workspace2/kitti/testing/image_2/007662.png"
filepath = "/workspace2/kitti/testing/image_2/008001.png"
filepath = "/workspace2/kitti/testing/image_2/007618.png"
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=config)
images = tf.placeholder(tf.float32, [1, 150, 496, 3])
scdobj = scd.SCD(input=images)
rgan = eval_rgan.rgan(images=scdobj.end_points["pool5"],
z_dim=zdim,
nf=nf,
training=True)
imgsynth_from_feature = eval_imgsynth.ImageSynthesizer(rgan.rec_x_out)
scd_saver = scd.get_saver()
scd_saver.restore(sess, "/workspace/imgsynth/ssd_300_kitti/ssd_model.ckpt")
imgsynth_saver = eval_imgsynth.get_saver()
imgsynth_saver.restore(
sess, "/workspace/imgsynth/result_kitti256p_2/model.ckpt-89")
rgan_saver = eval_rgan.get_saver()
#rgan_saver.restore(sess,"models/model-99999")
rgan_saver.restore(sess, "models_doubleres_constr_x_rzx/model-80999")
image = cv2.resize(scd.imread_as_jpg(filepath), (496, 150))
image = np.expand_dims(image, 0)
reses = imgsynth_from_feature.generate_image_from_featuremap(
sess, image, images)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/single_generated.jpg", a)
def main():
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=config)
#define tensors and models
zb = 6
nf = 256
zdim = 1024
images = tf.placeholder(tf.float32, [2, 150, 496, 3])
z = tf.placeholder(tf.float32, [zb, zdim])
scdobj = scd.SCD(input=images)
imgsynth = eval_imgsynth.ImageSynthesizer(scdobj.end_points["pool5"])
rgan = eval_rgan.rgan(images=scdobj.end_points["pool5"],
z_dim=zdim,
nf=nf,
training=True)
imgsynth_from_feature = eval_imgsynth.ImageSynthesizer(rgan.rec_x_out,
reuse=True)
rgan_from_z = eval_rgan.rgan(latent_vec=z,
nf=nf,
z_dim=zdim,
reuse=True,
training=True)
imgsynth_from_z = eval_imgsynth.ImageSynthesizer(rgan_from_z.rec_x_p_out,
reuse=True)
#load weights
scd_saver = scd.get_saver()
scd_saver.restore(sess, "/workspace/imgsynth/ssd_300_kitti/ssd_model.ckpt")
imgsynth_saver = eval_imgsynth.get_saver()
imgsynth_saver.restore(
sess, "/workspace/imgsynth/result_kitti256p_2/model.ckpt-89")
rgan_saver = eval_rgan.get_saver()
rgan_saver.restore(sess, "models/model-99999")
co = []
image = cv2.resize(
scd.imread_as_jpg("/workspace2/kitti/testing/image_2/007482.png"),
(496, 150))
co.append(image)
image = cv2.resize(
scd.imread_as_jpg("/workspace2/kitti/testing/image_2/007481.png"),
(496, 150))
co.append(image)
#reses = imgsynth.generate_image_from_featuremap(np.expand_dims(image,0),images)
image = np.array(co)
#simply get detection result
reses = scdobj.get_image(sess, image)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/detected%d.jpg" % i, a)
#model:image=>feature, image decoder:feature=>image_hat
reses = imgsynth.generate_image_from_featuremap(sess, image, images)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/test%d.jpg" % i, a)
#model:image=>feature, gan:feature=>latent vector=>feature_hat, image decoder:feature_hat=>image_hat
reses = imgsynth_from_feature.generate_image_from_featuremap(
sess, image, images)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/test%d_2.jpg" % i, a)
#get detection result thru gan and image decoder
reses = scdobj.get_image(sess, reses)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/gan_detected%d.jpg" % i, a)
return 0
#latent vector=>feature_hat, image decoder:feature_hat=>image_hat
np.random.seed(0)
zs = []
zsp = np.random.randn(zb, zdim) + 1
zs = np.array(zsp)
reses = imgsynth_from_z.generate_image_from_featuremap(sess, zs, z)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/noise%d.jpg" % i, a)
#noise interpolation
zsb = []
for i in range(0, zb):
#zs.append(zsp[0]*float(i)/(float(zb)-1.)+zsp[1]*(1.-float(i)/(float(zb)-1.)))
zsb.append(zsp[i] * (1. - i / (zb - 1)))
zsb = np.array(zsb)
reses = imgsynth_from_z.generate_image_from_featuremap(sess, zsb, z)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/noise_interpolate%d.jpg" % i, a)
#model:images=>features, gan:features=>latent vectors=>features_hat, interpolate:features_hat=>features_interplated, image decoder:feature_interplated=>image_hat
zs = []
zsp = rgan.generate_noise_from_featuremap(sess, image, images)
for i in range(0, zb):
zs.append(zsp[0] * float(i) / zb + zsp[1] * (1 - float(i) / zb))
zs = np.array(zs)
reses = imgsynth_from_z.generate_image_from_featuremap(sess, zs, z)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/interpolate%d.jpg" % i, a)
zs = []
zsp = rgan.generate_noise_from_featuremap(sess, image, images)
for i in range(0, zb):
zs.append(zsp[0] * float(i) / zb + zsp[1] * (1 - float(i) / zb) + 3)
zs = np.array(zs)
reses = imgsynth_from_z.generate_image_from_featuremap(sess, zs, z)
for i, a in enumerate(reses):
print(i)
skimage.io.imsave("test/interpolate2%d.jpg" % i, a)
train_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=tf.contrib.framework.get_name_scope())
p2phd_var_list = [
v for v in train_vars if v.name.split("/")[0] == "pix2pixhd"
]
saver = tf.train.Saver(max_to_keep=10, var_list=p2phd_var_list)
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
with tf.Session(config=config) as session:
session.run(tf.global_variables_initializer())
"""
train_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.contrib.framework.get_name_scope())
ssd_var_list=[v for v in train_vars if v.name.split("/")[0] == "ssd_300_vgg"]
scd_saver = tf.train.Saver(var_list=ssd_var_list)
"""
scd_saver = scd.get_saver()
scd_saver.restore(session,
"/workspace/imgsynth/ssd_300_kitti/ssd_model.ckpt")
fmgan_saver = fmgan.get_saver()
fmgan_saver.restore(
session,
"/workspace/natural-adversary/image/models_doubleres_constr_x_rzx/model-80999"
)
p2phd_saver = p2phd.get_saver()
p2phd_saver.restore(session, "/workspace/p2phd_tf/models/model-716999")
images = noise = gen_cost = dis_cost = inv_cost = None
dis_cost_lst, inv_cost_lst = [], []
data_files = glob(os.path.join(args.dataset_train_path, "*.png"))
data_files = sorted(data_files)
data_files = np.array(data_files)