def fetch_x_y(data, limit):
batch, x, y = [], [], []
random_batch = []
for i in xrange(batch_size): # randomly fetch batch
random_batch.append(data[random.randint(0, limit - 1)])
for npyFile in random_batch:
loaded_scene = np.load(npyFile)
scene = utils.npy_cutter(loaded_scene, scene_shape)
batch.append(scene)
batch = np.reshape(batch,
(-1, scene_shape[0], scene_shape[1], scene_shape[2]))
x = batch[:, 0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
y = batch[:, 0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:scene_shape[2]] # gt
x = np.reshape(x,
(-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
y = np.reshape(y,
(-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
return x, y
def fetch_x_y(data, limit):
batch, x, y = [], [], []
random_batch = []
for i in xrange(batch_size): # randomly fetch batch
random_batch.append(data[random.randint(0, limit - 1)])
for npyFile in random_batch:
loaded_scene = np.load(npyFile)
scene = utils.npy_cutter(loaded_scene, scene_shape)
batch.append(scene)
batch = np.reshape(batch,
(-1, scene_shape[0], scene_shape[1], scene_shape[2]))
return batch
def show_result(sess):
logging.info("Creating ply files...")
print("Creating ply files...")
#//////////////////////////////////////////////////////////////////////
for counter in range(num_of_vis_batch):
trData, trLabel = [], []
batch_arr = []
batch_arr_2d = []
bs = 0
test_data = utils.fetch_random_batch(train_directory, batch_size)
for test in test_data:
loaded_file = np.load(test)
batch_arr.append(utils.npy_cutter(loaded_file, scene_shape))
bs += 1
batch_arr = np.reshape(
batch_arr, (bs, scene_shape[0], scene_shape[1], scene_shape[2]))
trData = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
trLabel = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:scene_shape[2]] # gt
trData = np.reshape(
trData, (-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
score = sess.run(ConvNet_class.score,
feed_dict={
x: trData,
keepProb: 1.0,
phase: False
})
score = np.reshape(score, (-1, scene_shape[0], scene_shape[1],
halfed_scene_shape, classes_count))
score = np.argmax(score, 4)
score = np.reshape(
score, (-1, scene_shape[0], scene_shape[1], halfed_scene_shape))
pre, rec = utils.precision_recall(score, trLabel, batch_size,
classes_count)
precision += pre
recall += rec
accu1, accu2 = accuFun(sess, trData, trLabel, bs)
accu1_all += accu1
accu2_all += accu2
logging.info("A1: %g, A2: %g" % (accu1, accu2))
print("A1: %g, A2: %g" % (accu1, accu2))
print precision / num_of_vis_batch * 1.0
print recall / num_of_vis_batch * 1.0
print accu1_all / num_of_vis_batch * 1.0
print accu2_all / num_of_vis_batch * 1.0
#//////////////////////////////////////////////////////////////////////
sys.exit(0)
bs = 0
trData, trLabel = [], []
batch_arr = []
# test_data = utils.fetch_random_batch(test_directory, batch_size)
# for test in test_data:
# loaded_file = np.load(test)
# batch_arr.append(utils.npy_cutter(loaded_file, scene_shape))
# bs += 1
# batch_arr = np.reshape( batch_arr, ( bs, scene_shape[0], scene_shape[1], scene_shape[2] ))
# trData = batch_arr[ :, 0:scene_shape[0], 0:scene_shape[1], 0:halfed_scene_shape ] # input
# trLabel = batch_arr[ :, 0:scene_shape[0], 0:scene_shape[1], halfed_scene_shape:scene_shape[2] ] # gt
# trData = np.reshape(trData, (-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
# accu1, accu2 = accuFun(sess, trData, trLabel, bs)
# logging.info("A1: %g, A2: %g" % (accu1, accu2))
# print ("A1: %g, A2: %g" % (accu1, accu2))
for item in glob.glob(directory + "/*.ply"):
os.remove(item)
batch_arr = []
name_arr = []
counter = 0
for item in glob.glob(test_directory + '*.npy'):
name_arr.append(str(item[12:]))
loaded_file = np.load(item)
batch_arr.append(utils.npy_cutter(loaded_file, scene_shape))
counter += 1
# if counter == batch_size:
batch_arr = np.reshape(
batch_arr, (-1, scene_shape[0], scene_shape[1], scene_shape[2]))
trData = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
trLabel = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:scene_shape[2]] # gt
trData = np.reshape(
trData, (-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
score = sess.run(ConvNet_class.score,
feed_dict={
x: trData,
keepProb: 1.0,
phase: False
})
score = np.reshape(score, (counter, scene_shape[0], scene_shape[1],
halfed_scene_shape, classes_count))
score = np.argmax(score, 4)
trData = np.reshape(
trData, (-1, scene_shape[0], scene_shape[1], halfed_scene_shape))
for i in range(counter):
# loaded_file = np.load(test_data[i])
# scene = utils.npy_cutter(loaded_file, scene_shape)
trData_i = trData[i, :, :, :]
trData_i = np.reshape(
trData_i, (scene_shape[0], scene_shape[1], halfed_scene_shape))
score_i = score[i, :, :, :]
score_i = np.reshape(
score_i, (scene_shape[0], scene_shape[1], halfed_scene_shape))
empty_scene = np.zeros((84, 44, 42))
empty_space = np.zeros((scene_shape[0], scene_shape[1], 50))
empty_scene = np.concatenate((trData_i, empty_scene), axis=2)
empty_scene = np.concatenate((empty_scene, empty_space), axis=2)
gen_scn = np.concatenate((trData_i, score_i), axis=2)
gen_scn = np.concatenate((empty_scene, gen_scn), axis=2)
empty_space = np.zeros((scene_shape[0], scene_shape[1], 50))
gen_scn = np.concatenate((gen_scn, empty_space), axis=2)
gen_scn = np.concatenate((gen_scn, batch_arr[i, :, :, :]), axis=2)
output = open(directory + "/" + name_arr[i] + ".ply", 'w')
ply = ""
numOfVrtc = 0
for idx1 in range(gen_scn.shape[0]):
for idx2 in range(gen_scn.shape[1]):
for idx3 in range(gen_scn.shape[2]):
if gen_scn[idx1][idx2][idx3] > 0:
ply = ply + str(idx1) + " " + str(idx2) + " " + str(
idx3) + str(utils.colors[int(
gen_scn[idx1][idx2][idx3])]) + "\n"
numOfVrtc += 1
output.write("ply" + "\n")
output.write("format ascii 1.0" + "\n")
output.write("comment VCGLIB generated" + "\n")
output.write("element vertex " + str(numOfVrtc) + "\n")
output.write("property float x" + "\n")
output.write("property float y" + "\n")
output.write("property float z" + "\n")
output.write("property uchar red" + "\n")
output.write("property uchar green" + "\n")
output.write("property uchar blue" + "\n")
output.write("property uchar alpha" + "\n")
output.write("element face 0" + "\n")
output.write("property list uchar int vertex_indices" + "\n")
output.write("end_header" + "\n")
output.write(ply)
output.close()
print(test_data[i][12:] + ".ply" + " is Done!")
batch_arr = []
name_arr = []
counter = 0
def show_result(sess):
logging.info("Creating ply files...")
print("Creating ply files...")
bs = 0
trData, trLabel = [], []
batch_arr = []
test_data = utils.fetch_random_batch(test_directory, 64)
for test in test_data:
loaded_file = np.load(test)
batch_arr.append(utils.npy_cutter(loaded_file, scene_shape))
bs += 1
batch_arr = np.reshape(
batch_arr, (bs, scene_shape[0], scene_shape[1], scene_shape[2]))
trData = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
trLabel = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:scene_shape[2]] # gt
trData = np.reshape(
trData, (-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
score = sess.run(ConvNet_class.score,
feed_dict={
x: trData,
keepProb: 1.0,
phase: False
})
accu1, accu2 = accuFun(sess, trData, trLabel, bs)
logging.info("A1: %g, A2: %g" % (accu1, accu2))
print("A1: %g, A2: %g" % (accu1, accu2))
for test in test_data:
loaded_file = np.load(test)
scene = utils.npy_cutter(loaded_file, scene_shape)
trData, trLabel = [], []
trData = scene[0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
trLabel = scene[0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:scene_shape[2]] # gt
trData = np.reshape(
trData, (-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
score = sess.run(ConvNet_class.score,
feed_dict={
x: trData,
keepProb: 1.0,
phase: False
})
score = np.reshape(score, (scene_shape[0], scene_shape[1],
halfed_scene_shape, classes_count))
score = np.argmax(score, 3)
score = np.reshape(
score, (scene_shape[0], scene_shape[1], halfed_scene_shape))
score = score[0:scene_shape[0], 0:scene_shape[1], 0:halfed_scene_shape]
trData = np.reshape(
trData, (scene_shape[0], scene_shape[1], halfed_scene_shape))
gen_scn = np.concatenate((trData, score), axis=2)
empty_space = np.zeros((10, 44, 84))
gen_scn = np.concatenate((gen_scn, empty_space), axis=0)
gen_scn = np.concatenate((gen_scn, scene), axis=0)
output = open(directory + "/" + test[10:] + ".ply", 'w')
ply = ""
numOfVrtc = 0
for idx1 in range(gen_scn.shape[0]):
for idx2 in range(gen_scn.shape[1]):
for idx3 in range(gen_scn.shape[2]):
if gen_scn[idx1][idx2][idx3] > 0:
ply = ply + str(idx1) + " " + str(idx2) + " " + str(
idx3) + str(utils.colors[int(
gen_scn[idx1][idx2][idx3])]) + "\n"
numOfVrtc += 1
output.write("ply" + "\n")
output.write("format ascii 1.0" + "\n")
output.write("comment VCGLIB generated" + "\n")
output.write("element vertex " + str(numOfVrtc) + "\n")
output.write("property float x" + "\n")
output.write("property float y" + "\n")
output.write("property float z" + "\n")
output.write("property uchar red" + "\n")
output.write("property uchar green" + "\n")
output.write("property uchar blue" + "\n")
output.write("property uchar alpha" + "\n")
output.write("element face 0" + "\n")
output.write("property list uchar int vertex_indices" + "\n")
output.write("end_header" + "\n")
output.write(ply)
output.close()
logging.info(test + ".ply" + " is Done!")
print(test + ".ply" + " is Done!")
logging.info("A1: %g, A2: %g" % (accu1, accu2))
print("A1: %g, A2: %g" % (accu1, accu2))
valid_cost = []
train_accu1 = []
train_accu2 = []
valid_accu1 = []
valid_accu2 = []
batch = []
accu1tr, accu2tr = 0, 0
try:
while (epoch < max_epoch):
for npyFile in glob.glob(data_directory + '*.npy'):
trData, trLabel = [], []
if counter < batch_size:
loaded_scene = np.load(npyFile)
scene = utils.npy_cutter(loaded_scene, scene_shape)
batch.append(scene)
counter += 1
else:
counter = 0
batch = np.reshape(batch,
(-1, scene_shape[0], scene_shape[1],
scene_shape[2]))
trData = batch[:, 0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
trLabel = batch[:, 0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:
scene_shape[2]] # gt
trData = np.reshape(
def show_result(sess):
print("Creating ply files...")
cur_vis_batch = 0
trData, trLabel = [], []
batch_arr = []
for item in glob.glob(directory + "/*.ply"):
os.remove(item)
batch_arr = []
name_arr = []
counter = 0
for item in glob.glob(train_directory + '*.npy'):
name_arr.append(str(item[12:]))
loaded_file = np.load(item)
batch_arr.append(utils.npy_cutter(loaded_file, scene_shape))
counter += 1
if counter == batch_size:
batch_arr = np.reshape(
batch_arr,
(-1, scene_shape[0], scene_shape[1], scene_shape[2]))
trData = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
trLabel = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:scene_shape[2]] # gt
trData = np.reshape(
trData,
(-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
score = sess.run(ConvNet_class.generator,
feed_dict={
x: trData,
keepProb: 1.0,
phase: False
})
score = np.reshape(score, (counter, scene_shape[0], scene_shape[1],
halfed_scene_shape, classes_count))
score = np.argmax(score, 4)
trData = np.reshape(
trData,
(-1, scene_shape[0], scene_shape[1], halfed_scene_shape))
for i in range(counter):
trData_i = trData[i, :, :, :]
trData_i = np.reshape(
trData_i,
(scene_shape[0], scene_shape[1], halfed_scene_shape))
score_i = score[i, :, :, :]
score_i = np.reshape(
score_i,
(scene_shape[0], scene_shape[1], halfed_scene_shape))
empty_scene = np.zeros((84, 44, 42))
empty_space = np.zeros((scene_shape[0], scene_shape[1], 50))
empty_scene = np.concatenate((trData_i, empty_scene), axis=2)
empty_scene = np.concatenate((empty_scene, empty_space),
axis=2)
gen_scn = np.concatenate((trData_i, score_i), axis=2)
gen_scn = np.concatenate((empty_scene, gen_scn), axis=2)
empty_space = np.zeros((scene_shape[0], scene_shape[1], 50))
gen_scn = np.concatenate((gen_scn, empty_space), axis=2)
gen_scn = np.concatenate((gen_scn, batch_arr[i, :, :, :]),
axis=2)
output = open(directory + "/" + name_arr[i] + ".ply", 'w')
ply = ""
numOfVrtc = 0
for idx1 in range(gen_scn.shape[0]):
for idx2 in range(gen_scn.shape[1]):
for idx3 in range(gen_scn.shape[2]):
if gen_scn[idx1][idx2][idx3] > 0:
ply = ply + str(idx1) + " " + str(
idx2) + " " + str(idx3) + str(
utils.colors[int(
gen_scn[idx1][idx2][idx3])]) + "\n"
numOfVrtc += 1
output.write("ply" + "\n")
output.write("format ascii 1.0" + "\n")
output.write("comment VCGLIB generated" + "\n")
output.write("element vertex " + str(numOfVrtc) + "\n")
output.write("property float x" + "\n")
output.write("property float y" + "\n")
output.write("property float z" + "\n")
output.write("property uchar red" + "\n")
output.write("property uchar green" + "\n")
output.write("property uchar blue" + "\n")
output.write("property uchar alpha" + "\n")
output.write("element face 0" + "\n")
output.write("property list uchar int vertex_indices" + "\n")
output.write("end_header" + "\n")
output.write(ply)
output.close()
print(name_arr[i][12:] + ".ply" + " is Done!")
batch_arr = []
name_arr = []
counter = 0
cur_vis_batch += 1
if num_of_vis_batch == cur_vis_batch:
sys.exit(0)
