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Copy pathpredict_albedo_shading_iiw.py
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predict_albedo_shading_iiw.py
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import sys
sys.path.append('pyAIUtils')
import os
import scipy.cluster
import scipy.ndimage
import sklearn.mixture
import numpy as np
import tensorflow as tf
import random
import network.graphs as graphs
import constants
import utils
flags = tf.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('run_id', 'decomp_iiw', '')
flags.DEFINE_string('root_id', '_n', '')
flags.DEFINE_bool('encode_train', True, '')
flags.DEFINE_bool('print_intermediate', False, '')
flags.DEFINE_integer('num_threads', 1, '')
flags.DEFINE_integer('thread_num', 0, '')
flags.DEFINE_integer('start_num', 0, '')
flags.DEFINE_integer('end_num', 5230, '')
flags.DEFINE_bool('backwards', False, '')
def read_and_encode_images(vae_encode, image_generator, name):
code = []
for i,ims in enumerate(image_generator):
means = vae_encode.generate_prediction_cat(ims[name])
Q = []
for j,m in enumerate(means):
if i == 0:
code.append([np.reshape(m, [-1, m.shape[-1]])])
else:
code[j].append(np.reshape(m, [-1, m.shape[-1]]))
out_code = []
for c in code:
out_code.append(np.concatenate(c, 0))
return out_code
def cropims(ims, crops):
return ims[:,:crops[0], :crops[1], :]
def print_preds(pred, mask, iter_num, out_dir, crops, name):
suffix = '_%d'%(iter_num) if iter_num >= 0 else ''
utils.print_images(cropims(np.exp(np.minimum(0, -pred[0])) * mask, crops),'/%s_shading%s'%(name, suffix), out_dir)
utils.print_images(cropims(np.exp(np.minimum(0, -pred[1])) * mask, crops),'/%s_albedo%s'%(name, suffix), out_dir)
utils.print_images(cropims((np.exp(-pred[2]) - .5) * mask, crops),'/%s_residual%s'%(name, suffix), out_dir)
def print_images(ims, pred, mask, iter_num, out_dir, crops, name):
suffix = '_%d'%(iter_num) if iter_num >= 0 else ''
utils.print_images(cropims(ims * mask,crops), '/%s_im%s'%(name, suffix), out_dir)
print_preds(pred, mask, iter_num, out_dir, crops, name)
def check_ims(ims, out_dir, name):
return utils.check_ims(ims, '/%s_im'%(name), out_dir)
const_ = constants.Constants(FLAGS.root_id, FLAGS.run_id)
const = utils.load_params(const_.model_dir(), 'const')
const.run_id = FLAGS.run_id
const.root_id = FLAGS.root_id
const.assign_data_dir()
const.make_pred_dirs()
pred_params = graphs.PredictionParams()
pred_params.learning_rate = .15
pred_params.decay_steps = 500
pred_params.code_initializer = tf.random_normal_initializer(0.0, 1.0)
pred_params.nmeans = 1
pred_params.cov_type = 'spherical'
pred_params.reconstruction_error_weight = 100.0
pred_params.logprob_weight = [.0001, .0001]
pred_params.corr_patch_size = [5]
pred_params.corr_patch_rate = [1]
pred_params.patch_agreement_weight = [10000.0, 10000.0, 10000.0, 10000.0]
pred_params.relative_recon_err = False
pred_params.code_initializer = 'random_normal'
pred_params.smart_init = True
utils.save_params(pred_params, const.pred_dir(), 'pred_params')
pred_params.code_initializer = tf.random_normal_initializer(0.0, 1.0)
image_shape = const.default_image_data_shape()
vae_shading_train_params = utils.load_params(const.model_dir(), 'params_shading')
vae_albedo_train_params = utils.load_params(const.model_dir(), 'params_albedo')
image_shape = [image_shape[0], 2*image_shape[1], 2*image_shape[2], image_shape[3]]
if FLAGS.encode_train:
print "encoding"
vae_encode = graphs.VAEEncodeOnlyLaplacian(image_shape, params=vae_shading_train_params, name='')
vae_encode.restore(const.model_dir() + '/shading_gen_vae')
d_train = const.get_rendered_shading_data(num_epochs=5, patch_size=image_shape[1])
code_sh = read_and_encode_images(vae_encode, d_train, 'image')
vae_encode = graphs.VAEEncodeOnlyLaplacian(image_shape, params=vae_albedo_train_params, name='')
vae_encode.restore(const.model_dir() + '/albedo_gen_vae')
d_train = const.get_mondrian_albedo_data(num_epochs = 10, patch_size = image_shape[1])
code_albedo = read_and_encode_images(vae_encode, d_train, 'image')
np.savez(const.encode_dir() + '/albedo_one', *code_albedo)
np.savez(const.encode_dir() + '/sh_one', *code_sh)
else:
print "loading"
with open(const.encode_dir() + '/albedo_one.npz') as f:
f_alb = np.load(f)
code_albedo = []
for i in range(len(f_alb.files)):
code_albedo.append(f_alb['arr_%d'%(i)])
with open(const.encode_dir() + '/sh_one.npz') as f:
f_sh = np.load(f)
code_sh = []
for i in range(len(f_alb.files)):
code_sh.append(f_sh['arr_%d'%(i)])
gmm_material = []
gmm_sh = []
gmm_albedo = []
for i in range(len(code_albedo)):
print "gmm computation"
# GMM STUFF
code_sh_ = np.random.permutation(code_sh[i])
code_albedo_ = np.random.permutation(code_albedo[i])
sh_gmm = code_sh_[:100000]
albedo_gmm = code_albedo_[:100000]
gmm_sh.append(sklearn.mixture.GaussianMixture(n_components=pred_params.nmeans, covariance_type=pred_params.cov_type, max_iter=100, n_init=10))
gmm_sh[-1].fit(sh_gmm)
gmm_albedo.append(sklearn.mixture.GaussianMixture(n_components=pred_params.nmeans, covariance_type=pred_params.cov_type, max_iter=100, n_init=10))
gmm_albedo[-1].fit(albedo_gmm)
# START DECOMP CODE
d_test = const.get_iiw_datamgr()
idxs = range(FLAGS.start_num, FLAGS.end_num)
if FLAGS.backwards:
idxs = reversed(idxs)
for image_idx in idxs:
if (check_ims(np.ones((1,5)), const.pred_dir(), d_test.im_list[image_idx])):
print '%d exists: %s'%(image_idx, d_test.im_list[image_idx])
continue
else:
print '%d non_exists: %s'%(image_idx, d_test.im_list[image_idx])
ims_ = d_test.ims(image_idx)
w = ims_.image.shape[0]
crop_w = w
while w%64 != 0:
w+=1
h = ims_.image.shape[1]
crop_h = h
while h%64 != 0:
h+=1
ims_ = ims_.crop_image([0,0], [w,h])
ims = {}
ims['image'] = np.expand_dims(ims_.image,0).astype(np.float32)
ims['mask'] = np.expand_dims(np.expand_dims(ims_.mask,0),-1).astype(np.float32)
if pred_params.smart_init:
# Initialize smartly
im = ims['image'] * (ims['mask'] + (1-ims['mask']) * .01)
shading_init = scipy.ndimage.gaussian_filter(im, sigma=5)
albedo_init = im / np.maximum(shading_init, .01)
vae_encode = graphs.VAEEncodeOnlyLaplacian(ims['image'].shape, params=vae_albedo_train_params, name='')
vae_encode.restore(const.model_dir() + '/albedo_gen_vae')
albedo_code = vae_encode.generate_predictions(albedo_init)
vae_encode = graphs.VAEEncodeOnlyLaplacian(ims['image'].shape, params=vae_shading_train_params, name='')
vae_encode.restore(const.model_dir() + '/shading_gen_vae')
shading_code = vae_encode.generate_predictions(shading_init)
im_pred = graphs.VAEPrediction(ims['image'], ims['mask'],
[gmm_sh, gmm_albedo],
[vae_shading_train_params, vae_albedo_train_params], pred_params=pred_params,
initial_codes = [shading_code, albedo_code])
else:
im_pred = graphs.VAEPrediction(ims['image'], ims['mask'],
[gmm_sh, gmm_albedo],
[vae_shading_train_params, vae_albedo_train_params], pred_params=pred_params)
im_pred.restore([const.model_dir() + '/shading_gen_vae',
const.model_dir() + '/albedo_gen_vae'])
for o in range(5):
if FLAGS.print_intermediate:
im_out = im_pred.get_image()
print_preds(im_out, ims['mask'], o, const.pred_dir(), [crop_w, crop_h], ims_.name)
for i in range(100):
min_out = im_pred.minimize()
print im_pred.minimize_print()
im_out = im_pred.get_image()
print_images(ims['image'], im_out, ims['mask'], -1, const.pred_dir(), [crop_w, crop_h], ims_.name)