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)