image_list.append('./data/1.png')
image_list.append('./data/2.png')
image_list.append('./data/3.png')
image_list.append('./data/4.png')
image_list.append('./data/5.png')
# network input
image_tf = tf.placeholder(tf.float32, shape=(1, 240, 320, 3))
hand_side_tf = tf.constant([[1.0, 0.0]
]) # left hand (true for all samples provided)
evaluation = tf.placeholder_with_default(True, shape=())
# build network
net = PoseEstimationNetwork()
print("Line 48:Let's test!\n")
hand_scoremap_tf, image_crop_tf, scale_crop_tf, center_tf = net.HandSegCrop(
image_tf)
print("Oops,testcrop_line 49==> The formu is :", hand_scoremap_tf.shape)
# Start TF
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# initialize network
# net.init(sess)
# retrained version: HandSegNet
last_cpt = tf.train.latest_checkpoint(PATH_TO_HANDSEGNET_SNAPSHOTS)
assert last_cpt is not None, "Could not locate snapshot to load. Did you already train the network and set the path accordingly?"
load_weights_from_snapshot(sess,
last_cpt,
discard_list=['Adam', 'global_step', 'beta'])
# Feed image list through network
shuffle=False,
use_wrist_coord=True,
scale_to_size=True)
# build network graph
data = dataset.get()
# build network
net = PoseEstimationNetwork()
# scale input to common size for evaluation
image_scaled = tf.image.resize_images(data['image'], (240, 320))
s = data['image'].get_shape().as_list()
scale = (240.0 / s[1], 320.0 / s[2])
hand_scoremap, image_crop, scale_crop, center = net.HandSegCrop(image_scaled)
# detect keypoints in 2D
s = image_crop.get_shape().as_list()
keypoints_scoremap = net.PoseNet(image_crop)
keypoints_scoremap = keypoints_scoremap[-1]
keypoints_scoremap = tf.image.resize_images(keypoints_scoremap, (s[1], s[2]))
# Start TF
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
tf.train.start_queue_runners(sess=sess)
# initialize network weights
# retrained version: HandSegNet
last_cpt = tf.train.latest_checkpoint(PATH_TO_HANDSEGNET_SNAPSHOTS)