def get_pc(self, sid, mid, idx):
rand_idx = idx
pcs = []
for ix in rand_idx:
f = mid + '.ply'
f = osp.join(self.pc_dir, sid, f)
pc = load_ply(f)
pcs.append(pc)
return np.stack(pcs, axis=0)
def init_pc2pix(self):
# datasets = ('test')
os.makedirs(PLOTS_PATH, exist_ok=True)
# sofa2car - modify these 2 keys manually
#keys = [ "04256520", "02958343"]
# sofa2chair
keys = ["04256520", "03001627"]
# chair2chair
# keys = [ "03001627", "03001627"]
tags = []
data = js[keys[0]]
tag = data['test']
tags.append(tag)
data = js[keys[1]]
tag = data['test']
tags.append(tag)
plys = []
ply_path = os.path.join(self.ply, keys[0])
plys.append(ply_path)
ply_path = os.path.join(self.ply, keys[1])
plys.append(ply_path)
tagslen = min(len(tags[0]), len(tags[1]))
self.tags = []
self.pc_codes = []
np.random.seed(int(time.time()))
for i in range(2):
j = np.random.randint(0, tagslen, 1)[0]
tag = tags[i][j]
self.tags.append(tag)
# images = []
# pc_codes = []
ply_file = os.path.join(plys[i], tag + ".ply")
pc = load_ply(ply_file)
target_path = os.path.join(PLOTS_PATH, tag + ".png")
fig = plot_3d_point_cloud(pc[:, 0],
pc[:, 1],
pc[:, 2],
show=False,
azim=320,
colorize='rainbow',
filename=target_path)
pc = norm_pc(pc)
shape = pc.shape
pc = np.reshape(pc, [-1, shape[0], shape[1]])
pc_code = ptcloud_ae.encoder.predict(pc)
self.pc_codes.append(pc_code)
self.show_image(target_path, x=(i * 2) * 256, y=0)
image = render_by_pc2pix(pc_code, self.pc2pix, azim=-40)
print(image.shape)
self.display_image(image, x=(i * 2) * 256, y=256)
for key in js.keys():
# key eg 03001627
data = js[key]
tags = data['test']
ply_path_main = os.path.join(args.ply, key)
tagslen = len(tags)
n_interpolate = 10
if not interpolate:
n_interpolate = 2
for i in range(tagslen - 1):
n = 0
tag = tags[i]
images = []
pc_codes = []
ply_file = os.path.join(ply_path_main, tag + ".ply")
pc = load_ply(ply_file)
target_path = os.path.join(PLOTS_PATH, tag + "_" + str(n) + ".png")
n += 1
fig = plot_3d_point_cloud(pc[:, 0],
pc[:, 1],
pc[:, 2],
show=False,
azim=320,
colorize='rainbow',
filename=target_path)
image = np.array(Image.open(target_path)) / 255.0
images.append(image)
pc = norm_pc(pc)
shape = pc.shape
pc = np.reshape(pc, [-1, shape[0], shape[1]])
pc_code1 = ptcloud_ae.encoder.predict(pc)
fake_pc_codes = None
start_time = datetime.datetime.now()
print("Generating fake pc codes...")
print("Saving pc codes to file: ", pc_codes_filename)
i = 0
for dataset in datasets:
for key in js.keys():
# key eg 03001627
data = js[key]
tags = data[dataset]
ply_path_main = os.path.join(args.ply, key)
for tag in tags:
# tag eg fff29a99be0df71455a52e01ade8eb6a
ply_file = os.path.join(ply_path_main, tag + ".ply")
pc = norm_pc(load_ply(ply_file))
shape = pc.shape
pc = np.reshape(pc, [-1, shape[0], shape[1]])
fake_pc_code = ptcloud_ae.encoder.predict(pc)
if fake_pc_codes is None:
fake_pc_codes = fake_pc_code
else:
fake_pc_codes = np.append(fake_pc_codes,
fake_pc_code,
axis=0)
elapsed_time = datetime.datetime.now() - start_time
i += 1
pcent = 100. * float(i) / steps
log = "%0.2f%% of %d [shape: %s] [tag: %s] [time: %s]" % (
pcent, steps, fake_pc_codes.shape, tag, elapsed_time)
print(log)