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, batch_size)
for test in test_data:
batch_arr.append(np.load(test))
bs += 1
batch_arr = np.reshape( batch_arr, ( bs, scene_shape[0], scene_shape[1] ))
trData = batch_arr[ :, 0:scene_shape[0], 0:halfed_scene_shape ] # input
trLabel = batch_arr[ :, 0:scene_shape[0], halfed_scene_shape:scene_shape[1] ] # gt
trData = np.reshape(trData, (-1, scene_shape[0] * 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)
for test in test_data:
scene = np.load(test)
trData, trLabel = [], []
trData = scene[ 0:scene_shape[0], 0:halfed_scene_shape ] # input
trLabel = scene[ 0:scene_shape[0], halfed_scene_shape:scene_shape[1] ] # gt
trData = np.reshape( trData, ( -1, scene_shape[0] * 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], halfed_scene_shape, classes_count ))
score = np.argmax ( score, 2)
score = np.reshape( score, ( scene_shape[0], halfed_scene_shape ))
score = score[0:scene_shape[0], 0:halfed_scene_shape]
trData = np.reshape( trData, (scene_shape[0], halfed_scene_shape))
gen_scn = np.concatenate((trData, score), axis=1)
empty_space = np.zeros((10, scene_shape[0]))
gen_scn = np.concatenate((gen_scn, empty_space), axis=0)
gen_scn = np.concatenate((gen_scn, scene), axis=0)
output = open( directory + "/" + test[9:] + ".ply" , 'w')
ply = ""
numOfVrtc = 0
for idx1 in range(gen_scn.shape[0]):
for idx2 in range(gen_scn.shape[1]):
if gen_scn[idx1][idx2] > 0:
ply = ply + str(idx1)+ " " +str(idx2)+ " 0 " + str(utils.colors[ int(gen_scn[idx1][idx2]) ]) + "\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))
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 = [], [], []
precision = np.zeros(classes_count)
recall = np.zeros(classes_count)
accu1_all = 0.0
score = None
for counter in range(num_of_vis_batch):
trData, trLabel = [], []
batch_arr = []
bs = 0
test_data = utils.fetch_random_batch(test_directory, batch_size)
for test in test_data:
batch_arr.append(np.load(test))
bs += 1
batch_arr = np.reshape(batch_arr, (bs, scene_shape[0], scene_shape[1]))
trData = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1]] # input
trLabel = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1]] # gt
score = generate(sess, trData, halfed_scene_shape)
score = np.reshape(score, (-1, scene_shape[0], scene_shape[1]))
# pre, rec = utils.precision_recall(score, trLabel, batch_size, classes_count)
# precision += pre
# recall += rec
# accu1 = accuFun(sess, trData, trLabel, bs)
# accu1_all += accu1
# logging.info("A1: %g" % (accu1))
# print ("A1: %g" % (accu1))
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
for item in glob.glob(directory + "/*.ply"):
os.remove(item)
for i in range(batch_size):
empty_space = np.zeros((scene_shape[1], 10))
gen_scn = np.concatenate((score[i], empty_space), axis=1)
gen_scn = np.concatenate((gen_scn, trLabel[i]), axis=1)
output = open(directory + "/" + test_data[i][10:] + ".ply", 'w')
ply = ""
numOfVrtc = 0
for idx1 in range(gen_scn.shape[0]):
for idx2 in range(gen_scn.shape[1]):
if gen_scn[idx1][idx2] > 0:
ply = ply + str(idx1) + " " + str(idx2) + " 0 " + str(
utils.colors[int(gen_scn[idx1][idx2])]) + "\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_data[i] + ".ply" + " is Done!")
print(test_data[i] + ".ply" + " is Done!")