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visualize_sample.py
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visualize_sample.py
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from matplotlib import pyplot as plt
from mpl_toolkits import mplot3d#from mpl_toolkits.mplot3d import Axes3D
from modelnet_dataloader import Modelnet40_data, Modelnet40_data_h5, Shapenet_data, Scannet_data_h5
import numpy as np
import os
def mkdir(path):
folder = os.path.exists(path)
if not folder:
os.makedirs(path)
print("--- new folder... ---")
print("--- OK ---")
else:
print("--- There is this folder! ---")
def draw_pc(pc, show=False, save_dir=None):
ax = plt.figure().add_subplot(111, projection='3d')
ax.scatter(pc[:, 0], pc[:, 1], pc[:, 2], marker='.')
ax.grid(False)
# ax.axis('off')
if show:
plt.show()
if save_dir is not None:
mkdir(save_dir)
save_dir = save_dir + '/' + str(i) +'.jpg'
plt.savefig(save_dir)
plt.close()
for dataset in ['shapenet','scannet']:
for state in ['train', 'test']:
save_dir = '../3d_imgs/' + dataset + '/' + state
mkdir(save_dir)
class_num=0
data_loader = Shapenet_data(pc_root='../PointDAN_Code/Transfer_3d_data/Transfer_3d_data/' + dataset, status=state)
rand_list = np.random.permutation(len(data_loader))
lable_list = ['Bathtub','Bed','Bookshelf','Cabinet','Chair','Keyboard','Lamp','Laptop','Sofa','Table']
for i in rand_list:
pc, lbl = data_loader.__getitem__(i)
for class_num in range(10):
print(lable_list[class_num],i) if lbl==class_num else None
draw_pc(pc.squeeze().transpose(1,0), save_dir = save_dir+'/'+ lable_list[class_num] ) if lbl==class_num else None