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evaluate_pose_JPPNet-s2.py
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from __future__ import division
import scipy.io as sio
import scipy.misc
import matplotlib.pyplot as plt
from LIP_model import *
from utils import *
import os
import time
from glob import glob
import tensorflow as tf
import numpy as np
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# Hide the warning messages about CPU/GPU
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
NUM_STEPS = 6 # Number of images in the validation set.
INPUT_SIZE = (384, 384)
N_CLASSES = 20
DATA_DIRECTORY = './datasets/examples'
#DATA_DIRECTORY = './datasets/lip'
DATA_LIST_PATH = './datasets/examples/list/val.txt'
#DATA_LIST_PATH = './datasets/lip/list/val.txt'
RESTORE_FROM = './checkpoint/JPPNet-s2'
OUTPUT_DIR = './output/pose/val'
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
def main():
"""Create the model and start the evaluation process."""
# Create queue coordinator.
coord = tf.train.Coordinator()
h, w = INPUT_SIZE
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(DATA_DIRECTORY, DATA_LIST_PATH,
None, False, False, coord)
image = reader.image
image_rev = tf.reverse(image, tf.stack([1]))
image_list = reader.image_list
image_batch_origin = tf.stack([image, image_rev])
image_batch = tf.image.resize_images(image_batch_origin, [int(h), int(w)])
image_batch125 = tf.image.resize_images(
image_batch_origin, [int(h * 1.25), int(w * 1.25)])
image_batch075 = tf.image.resize_images(
image_batch_origin, [int(h * 0.75), int(w * 0.75)])
# Create network.
with tf.variable_scope('', reuse=False):
net_100 = JPPNetModel({'data': image_batch},
is_training=False, n_classes=N_CLASSES)
with tf.variable_scope('', reuse=True):
net_125 = JPPNetModel({'data': image_batch125},
is_training=False, n_classes=N_CLASSES)
with tf.variable_scope('', reuse=True):
net_075 = JPPNetModel({'data': image_batch075},
is_training=False, n_classes=N_CLASSES)
# parsing net
parsing_fea1_100 = net_100.layers['res5d_branch2b_parsing']
parsing_fea1_125 = net_125.layers['res5d_branch2b_parsing']
parsing_fea1_075 = net_075.layers['res5d_branch2b_parsing']
parsing_out1_100 = net_100.layers['fc1_human']
parsing_out1_125 = net_125.layers['fc1_human']
parsing_out1_075 = net_075.layers['fc1_human']
# pose net
resnet_fea_100 = net_100.layers['res4b22_relu']
resnet_fea_125 = net_125.layers['res4b22_relu']
resnet_fea_075 = net_075.layers['res4b22_relu']
with tf.variable_scope('', reuse=False):
pose_out1_100, pose_fea1_100 = pose_net(resnet_fea_100, 'fc1_pose')
pose_out2_100, pose_fea2_100 = pose_refine(
pose_out1_100, parsing_out1_100, pose_fea1_100, name='fc2_pose')
parsing_out2_100, parsing_fea2_100 = parsing_refine(
parsing_out1_100, pose_out1_100, parsing_fea1_100, name='fc2_parsing')
pose_out3_100, pose_fea3_100 = pose_refine(
pose_out2_100, parsing_out2_100, pose_fea2_100, name='fc3_pose')
with tf.variable_scope('', reuse=True):
pose_out1_125, pose_fea1_125 = pose_net(resnet_fea_125, 'fc1_pose')
pose_out2_125, pose_fea2_125 = pose_refine(
pose_out1_125, parsing_out1_125, pose_fea1_125, name='fc2_pose')
parsing_out2_125, parsing_fea2_125 = parsing_refine(
parsing_out1_125, pose_out1_125, parsing_fea1_125, name='fc2_parsing')
pose_out3_125, pose_fea3_125 = pose_refine(
pose_out2_125, parsing_out2_125, pose_fea2_125, name='fc3_pose')
with tf.variable_scope('', reuse=True):
pose_out1_075, pose_fea1_075 = pose_net(resnet_fea_075, 'fc1_pose')
pose_out2_075, pose_fea2_075 = pose_refine(
pose_out1_075, parsing_out1_075, pose_fea1_075, name='fc2_pose')
parsing_out2_075, parsing_fea2_075 = parsing_refine(
parsing_out1_075, pose_out1_075, parsing_fea1_075, name='fc2_parsing')
pose_out3_075, pose_fea3_075 = pose_refine(
pose_out2_075, parsing_out2_075, pose_fea2_075, name='fc3_pose')
pose_out3 = tf.reduce_mean(tf.stack([tf.image.resize_nearest_neighbor(pose_out3_100, tf.shape(image_batch_origin)[1:3, ]),
tf.image.resize_nearest_neighbor(
pose_out3_125, tf.shape(image_batch_origin)[1:3, ]),
tf.image.resize_nearest_neighbor(pose_out3_075, tf.shape(image_batch_origin)[1:3, ])]), axis=0)
head_output, tail_output = tf.unstack(pose_out3, num=2, axis=0)
tail_list = tf.unstack(tail_output, num=16, axis=2)
tail_list_rev = [None] * 16
tail_list_rev[0] = tail_list[5]
tail_list_rev[1] = tail_list[4]
tail_list_rev[2] = tail_list[3]
tail_list_rev[3] = tail_list[2]
tail_list_rev[4] = tail_list[1]
tail_list_rev[5] = tail_list[0]
tail_list_rev[10] = tail_list[15]
tail_list_rev[11] = tail_list[14]
tail_list_rev[12] = tail_list[13]
tail_list_rev[13] = tail_list[12]
tail_list_rev[14] = tail_list[11]
tail_list_rev[15] = tail_list[10]
tail_list_rev[6] = tail_list[6]
tail_list_rev[7] = tail_list[7]
tail_list_rev[8] = tail_list[8]
tail_list_rev[9] = tail_list[9]
tail_output_rev = tf.stack(tail_list_rev, axis=2)
tail_output_rev = tf.reverse(tail_output_rev, tf.stack([1]))
output_all = tf.reduce_mean(
tf.stack([head_output, tail_output_rev]), axis=0)
output_all = tf.expand_dims(output_all, dim=0)
# Which variables to load.
restore_var = tf.global_variables()
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
if RESTORE_FROM is not None:
if load(loader, sess, RESTORE_FROM):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# Iterate over training steps.
for step in range(NUM_STEPS):
predict_ = sess.run(output_all)
save_lip_images(image_list[step], predict_, OUTPUT_DIR)
if step % 100 == 0:
print('step {:d}'.format(step))
print(image_list[step])
coord.request_stop()
coord.join(threads)
def save_lip_images(image_path, samples, out_dir):
img_A = scipy.misc.imread(image_path).astype(np.float)
rows = img_A.shape[0]
cols = img_A.shape[1]
image = samples[0]
img_split = image_path.split('/')
img_id = img_split[-1][:-4]
with open('{}/{}.txt'.format(out_dir, img_id), 'w') as f:
for p in range(image.shape[2]):
channel_ = image[:, :, p]
if channel_.shape[0] != rows or channel_.shape[1] != cols:
print('sizes do not match...')
channel_ = scipy.misc.imresize(
channel_, [rows, cols], interp='nearest')
r_, c_ = np.unravel_index(channel_.argmax(), channel_.shape)
f.write('%d %d ' % (int(c_), int(r_)))
if __name__ == '__main__':
main()