├── LICENSE ├── README.md ├── data ├── filelist ├── hdf5_data │ ├── data_testing.h5 │ ├── data_training.h5 │ └── plane_color_mapping.json ├── make_filelist.py ├── make_ply.py ├── ply │ ├── chair1.ply │ ├── chair2.ply │ ├── chair3.ply │ ├── chair4.ply │ ├── chair5.ply │ ├── chair6.ply │ ├── chair7.ply │ ├── chair8.ply │ ├── table1.ply │ ├── table2.ply │ ├── table3.ply │ ├── table4.ply │ ├── table5.ply │ ├── table6.ply │ ├── table7.ply │ └── table8.ply ├── points │ ├── chair1.pts │ ├── chair2.pts │ ├── chair3.pts │ ├── chair4.pts │ ├── chair5.pts │ ├── chair6.pts │ ├── chair7.pts │ ├── chair8.pts │ ├── table1.pts │ ├── table2.pts │ ├── table3.pts │ ├── table4.pts │ ├── table5.pts │ ├── table6.pts │ ├── table7.pts │ └── table8.pts ├── points_label │ ├── chair1.seg │ ├── chair2.seg │ ├── chair3.seg │ ├── chair4.seg │ ├── chair5.seg │ ├── chair6.seg │ ├── chair7.seg │ ├── chair8.seg │ ├── table1.seg │ ├── table2.seg │ ├── table3.seg │ ├── table4.seg │ ├── table5.seg │ ├── table6.seg │ ├── table7.seg │ └── table8.seg ├── rename_pts.py ├── subsampler.py └── write_hdf5.py ├── make_testing_file_list.py ├── pointnet_plane_detection.py ├── pointnet_plane_detection2.py ├── test.py ├── testing_ply_file_list ├── testing_result.jpg ├── train.py └── training_data.jpg /README.md: -------------------------------------------------------------------------------- 1 | # PointNet Plane Detection 2 | 3 | In this experiment, based on the implementation of [PointNet](https://github.com/charlesq34/pointnet), we tried to explore the potential of neural network models for some more specific tasks on point clouds, e.g., 3D plane detection. 4 | 5 | ## Experimental Data 6 | 7 | 64 tables from the [ShapeNetPart](web.stanford.edu/~ericyi/project_page/part_annotation/ 8 | ) dataset were selected for training and 8 for testing, each of which has a significant planar surface. The picture below shows a part of the training set. For the training data, the points on the plane are labeled manually. 9 | 10 | ![training_data](training_data.jpg) 11 | 12 | ## Testing Result 13 | 14 | The network was trained for 100 epochs and a result of an accuracy around 85% was derived. The plane detection result on the testing set is as below. 15 | 16 | ![testing_result](testing_result.jpg) 17 | 18 | The result shows a few interesting patterns in it. The model seems to favor a table with a more normal shape, i.e., a table with a square tabletop and four straight legs. For tables without a regular shape, the classification accuracy is relatively lower, and the model tends to misclassify the points in the middle of tabletop. 19 | 20 | For the very specific plane detection problem, such misclassification issue does not matter much, as we can apply a [3D Hough transformation](https://link.springer.com/article/10.1007/3DRes.02%282011%293) afterwards on the detected planar part, which is robust towards missing and contaminated data, to generate the plane information. 21 | -------------------------------------------------------------------------------- /data/filelist: -------------------------------------------------------------------------------- 1 | table1 2 | chair1 3 | table2 4 | chair2 5 | table3 6 | chair3 7 | table4 8 | chair4 9 | chair5 10 | table5 11 | table6 12 | chair6 13 | chair7 14 | table7 15 | table8 16 | chair8 17 | -------------------------------------------------------------------------------- /data/hdf5_data/data_testing.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IsaacGuan/PointNet-Plane-Detection/d53acc9cb42d05ddcd1c6e4e45976ae5b8aefad4/data/hdf5_data/data_testing.h5 -------------------------------------------------------------------------------- /data/hdf5_data/data_training.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IsaacGuan/PointNet-Plane-Detection/d53acc9cb42d05ddcd1c6e4e45976ae5b8aefad4/data/hdf5_data/data_training.h5 -------------------------------------------------------------------------------- /data/hdf5_data/plane_color_mapping.json: -------------------------------------------------------------------------------- 1 | [[0.35, 0.95, 0.05], [0.05, 0.35, 0.65]] -------------------------------------------------------------------------------- /data/make_filelist.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | def number(x): 4 | return(int(x[5:-4])) 5 | 6 | filelist = open("filelist", "w+") 7 | 8 | files = os.listdir("./points") 9 | 10 | for file in sorted(files, key = number): 11 | file_name = file[:-4] 12 | filelist.write("%s\n" % file_name) 13 | -------------------------------------------------------------------------------- /data/make_ply.py: -------------------------------------------------------------------------------- 1 | import os 2 | from shutil import copyfile 3 | 4 | os.chdir("./points") 5 | 6 | for file in os.listdir('.'): 7 | copyfile(file, "../ply/" + file[:-4] + ".ply") 8 | 9 | os.chdir("../ply") 10 | 11 | for file in os.listdir('.'): 12 | f = open(file, "r+") 13 | lines = [line.rstrip() for line in f] 14 | f.seek(0) 15 | head = "ply\nformat ascii 1.0\ncomment VCGLIB generated\nelement vertex " + str(len(lines)) + "\nproperty float x\nproperty float y\nproperty float z\nelement face 0\nproperty list uchar int vertex_indices\nend_header\n" 16 | f.write(head) 17 | for line in lines: 18 | f.write(line+"\n") 19 | f.close() 20 | -------------------------------------------------------------------------------- /data/points_label/chair1.seg: -------------------------------------------------------------------------------- 1 | 0 2 | 0 3 | 0 4 | 0 5 | 0 6 | 0 7 | 0 8 | 0 9 | 0 10 | 0 11 | 0 12 | 0 13 | 0 14 | 0 15 | 0 16 | 1 17 | 0 18 | 0 19 | 0 20 | 0 21 | 0 22 | 1 23 | 0 24 | 0 25 | 0 26 | 0 27 | 0 28 | 1 29 | 0 30 | 0 31 | 0 32 | 0 33 | 0 34 | 0 35 | 0 36 | 0 37 | 0 38 | 0 39 | 0 40 | 0 41 | 0 42 | 1 43 | 0 44 | 0 45 | 0 46 | 1 47 | 0 48 | 0 49 | 0 50 | 0 51 | 0 52 | 0 53 | 0 54 | 1 55 | 0 56 | 0 57 | 0 58 | 0 59 | 0 60 | 0 61 | 0 62 | 0 63 | 0 64 | 0 65 | 0 66 | 0 67 | 0 68 | 0 69 | 0 70 | 0 71 | 1 72 | 0 73 | 0 74 | 0 75 | 0 76 | 0 77 | 0 78 | 0 79 | 0 80 | 0 81 | 0 82 | 0 83 | 1 84 | 0 85 | 0 86 | 0 87 | 0 88 | 0 89 | 0 90 | 0 91 | 0 92 | 0 93 | 1 94 | 0 95 | 0 96 | 0 97 | 1 98 | 0 99 | 0 100 | 1 101 | 0 102 | 0 103 | 0 104 | 0 105 | 0 106 | 0 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1400 | 0 1401 | 0 1402 | 0 1403 | 1 1404 | 0 1405 | 1 1406 | 0 1407 | 0 1408 | 0 1409 | 1 1410 | 1 1411 | 0 1412 | 1 1413 | 0 1414 | 1 1415 | 0 1416 | 0 1417 | 0 1418 | 0 1419 | 0 1420 | 1 1421 | 1 1422 | 1 1423 | 1 1424 | 0 1425 | 1 1426 | 0 1427 | 0 1428 | 1 1429 | 1 1430 | 0 1431 | 0 1432 | 1 1433 | 0 1434 | 0 1435 | 1 1436 | 0 1437 | 1 1438 | 0 1439 | 0 1440 | 1 1441 | 0 1442 | 1 1443 | 1 1444 | 0 1445 | 1 1446 | 0 1447 | 0 1448 | 0 1449 | 1 1450 | 0 1451 | 0 1452 | 0 1453 | 0 1454 | 1 1455 | 0 1456 | 0 1457 | 0 1458 | 1 1459 | 0 1460 | 0 1461 | 0 1462 | 0 1463 | 1 1464 | 0 1465 | 0 1466 | 0 1467 | 0 1468 | 0 1469 | 0 1470 | 0 1471 | 1 1472 | 1 1473 | 0 1474 | 1 1475 | 1 1476 | 1 1477 | 1 1478 | 1 1479 | 1 1480 | 0 1481 | 1 1482 | 1 1483 | 0 1484 | 1 1485 | 0 1486 | 0 1487 | 0 1488 | 1 1489 | 1 1490 | 1 1491 | 0 1492 | 1 1493 | 0 1494 | 0 1495 | 0 1496 | 0 1497 | 0 1498 | 1 1499 | 1 1500 | 0 1501 | 0 1502 | 0 1503 | 1 1504 | 0 1505 | 0 1506 | 0 1507 | 1 1508 | 0 1509 | 0 1510 | 1 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1733 | 0 1734 | 1 1735 | 0 1736 | 0 1737 | 1 1738 | 1 1739 | 1 1740 | 1 1741 | 1 1742 | 0 1743 | 1 1744 | 1 1745 | 0 1746 | 0 1747 | 0 1748 | 1 1749 | 0 1750 | 1 1751 | 1 1752 | 0 1753 | 0 1754 | 0 1755 | 1 1756 | 0 1757 | 0 1758 | 1 1759 | 0 1760 | 1 1761 | 0 1762 | 1 1763 | 0 1764 | 1 1765 | 0 1766 | 0 1767 | 0 1768 | 1 1769 | 1 1770 | 1 1771 | 1 1772 | 0 1773 | 0 1774 | 0 1775 | 0 1776 | 0 1777 | 1 1778 | 1 1779 | 1 1780 | 1 1781 | 0 1782 | 0 1783 | 0 1784 | 1 1785 | 0 1786 | 1 1787 | 0 1788 | 1 1789 | 0 1790 | 1 1791 | 0 1792 | 0 1793 | 1 1794 | 0 1795 | 0 1796 | 1 1797 | 1 1798 | 1 1799 | 1 1800 | 1 1801 | 0 1802 | 0 1803 | 1 1804 | 0 1805 | 1 1806 | 0 1807 | 1 1808 | 0 1809 | 0 1810 | 1 1811 | 0 1812 | 0 1813 | 1 1814 | 1 1815 | 0 1816 | 1 1817 | 1 1818 | 0 1819 | 0 1820 | 1 1821 | 1 1822 | 1 1823 | 1 1824 | 0 1825 | 0 1826 | 0 1827 | 0 1828 | 0 1829 | 1 1830 | 0 1831 | 1 1832 | 0 1833 | 1 1834 | 1 1835 | 0 1836 | 1 1837 | 0 1838 | 1 1839 | 0 1840 | 0 1841 | 0 1842 | 0 1843 | 1 1844 | 1 1845 | 1 1846 | 0 1847 | 0 1848 | 1 1849 | 0 1850 | 1 1851 | 1 1852 | 1 1853 | 0 1854 | 1 1855 | 1 1856 | 0 1857 | 0 1858 | 1 1859 | 1 1860 | 0 1861 | 1 1862 | 0 1863 | 0 1864 | 0 1865 | 0 1866 | 1 1867 | 0 1868 | 0 1869 | 1 1870 | 1 1871 | 1 1872 | 1 1873 | 1 1874 | 1 1875 | 0 1876 | 1 1877 | 1 1878 | 0 1879 | 1 1880 | 0 1881 | 1 1882 | 0 1883 | 0 1884 | 1 1885 | 0 1886 | 1 1887 | 1 1888 | 0 1889 | 0 1890 | 1 1891 | 1 1892 | 0 1893 | 0 1894 | 1 1895 | 0 1896 | 0 1897 | 1 1898 | 1 1899 | 0 1900 | 1 1901 | 1 1902 | 1 1903 | 1 1904 | 0 1905 | 0 1906 | 0 1907 | 0 1908 | 0 1909 | 1 1910 | 1 1911 | 1 1912 | 1 1913 | 1 1914 | 0 1915 | 0 1916 | 1 1917 | 1 1918 | 1 1919 | 1 1920 | 0 1921 | 1 1922 | 1 1923 | 0 1924 | 1 1925 | 1 1926 | 0 1927 | 0 1928 | 1 1929 | 0 1930 | 1 1931 | 1 1932 | 1 1933 | 0 1934 | 1 1935 | 1 1936 | 1 1937 | 1 1938 | 1 1939 | 0 1940 | 0 1941 | 1 1942 | 0 1943 | 1 1944 | 1 1945 | 0 1946 | 0 1947 | 0 1948 | 0 1949 | 1 1950 | 1 1951 | 1 1952 | 1 1953 | 1 1954 | 1 1955 | 0 1956 | 1 1957 | 1 1958 | 1 1959 | 1 1960 | 1 1961 | 1 1962 | 0 1963 | 1 1964 | 1 1965 | 0 1966 | 1 1967 | 1 1968 | 1 1969 | 0 1970 | 1 1971 | 0 1972 | 1 1973 | 1 1974 | 0 1975 | 0 1976 | 1 1977 | 1 1978 | 1 1979 | 1 1980 | 1 1981 | 1 1982 | 1 1983 | 0 1984 | 0 1985 | 1 1986 | 1 1987 | 0 1988 | 1 1989 | 0 1990 | 0 1991 | 1 1992 | 1 1993 | 0 1994 | 0 1995 | 0 1996 | 1 1997 | 0 1998 | 1 1999 | 0 2000 | 1 2001 | 1 2002 | 0 2003 | 1 2004 | 0 2005 | 0 2006 | 1 2007 | 0 2008 | 0 2009 | 1 2010 | 1 2011 | 1 2012 | 1 2013 | 0 2014 | 1 2015 | 0 2016 | 1 2017 | 1 2018 | 1 2019 | 0 2020 | 0 2021 | 0 2022 | 1 2023 | 0 2024 | 0 2025 | 1 2026 | 0 2027 | 1 2028 | 0 2029 | 0 2030 | 1 2031 | 1 2032 | 1 2033 | 0 2034 | 1 2035 | 0 2036 | 0 2037 | 1 2038 | 0 2039 | 1 2040 | 1 2041 | 0 2042 | 0 2043 | 0 2044 | 1 2045 | 1 2046 | 1 2047 | 0 2048 | 1 2049 | -------------------------------------------------------------------------------- /data/rename_pts.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | os.chdir("./points") 4 | 5 | files = os.listdir('.') 6 | files.sort() 7 | 8 | i = 1 9 | 10 | for file in files: 11 | if file[-2: ] == 'py': 12 | continue 13 | #new_name = "table" + str(i) + ".pts" 14 | new_name = "chair" + str(i) + ".pts" 15 | i += 1 16 | os.rename(file, new_name) 17 | -------------------------------------------------------------------------------- /data/subsampler.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | 4 | os.chdir("./points") 5 | 6 | for file in os.listdir('.'): 7 | f = open(file, "r+") 8 | lines = [line.rstrip() for line in f] 9 | slice = random.sample(lines, 2048) 10 | f.close() 11 | f = open(file, "w") 12 | for line in slice: 13 | f.write(line+"\n") 14 | f.close() 15 | -------------------------------------------------------------------------------- /data/write_hdf5.py: -------------------------------------------------------------------------------- 1 | import h5py 2 | import numpy as np 3 | from plyfile import PlyData, PlyElement 4 | 5 | filenames = [line.rstrip() for line in open("filelist", 'r')] 6 | 7 | #f = h5py.File("./hdf5_data/data_training.h5", 'w') 8 | f = h5py.File("./hdf5_data/data_testing.h5", 'w') 9 | 10 | a_data = np.zeros((len(filenames), 2048, 3)) 11 | a_pid = np.zeros((len(filenames), 2048), dtype = np.uint8) 12 | 13 | for i in range(0, len(filenames)): 14 | plydata = PlyData.read("./ply/" + filenames[i] + ".ply") 15 | piddata = [line.rstrip() for line in open("./points_label/" + filenames[i] + ".seg", 'r')] 16 | for j in range(0, 2048): 17 | a_data[i, j] = [plydata['vertex']['x'][j], plydata['vertex']['y'][j], plydata['vertex']['z'][j]] 18 | a_pid[i,j] = piddata[j] 19 | 20 | data = f.create_dataset("data", data = a_data) 21 | pid = f.create_dataset("pid", data = a_pid) 22 | -------------------------------------------------------------------------------- /make_testing_file_list.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | def number(x): 4 | return(int(x[5:-4])) 5 | 6 | testing_filelist = open("testing_ply_file_list", "w+") 7 | 8 | files = os.listdir("./data/points") 9 | 10 | for file in sorted(files, key = number): 11 | file_name = file[:-4] 12 | testing_filelist.write("points/%s.pts points_label/%s.seg\n" % (file_name, file_name)) 13 | -------------------------------------------------------------------------------- /pointnet_plane_detection.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | import math 4 | import os 5 | import sys 6 | BASE_DIR = os.path.dirname(os.path.abspath(__file__)) 7 | sys.path.append(os.path.dirname(BASE_DIR)) 8 | sys.path.append(os.path.join(BASE_DIR, '../utils')) 9 | import tf_util 10 | 11 | 12 | def get_transform_K(inputs, is_training, bn_decay=None, K = 3): 13 | """ Transform Net, input is BxNx1xK gray image 14 | Return: 15 | Transformation matrix of size KxK """ 16 | batch_size = inputs.get_shape()[0].value 17 | num_point = inputs.get_shape()[1].value 18 | 19 | net = tf_util.conv2d(inputs, 256, [1,1], padding='VALID', stride=[1,1], 20 | bn=True, is_training=is_training, scope='tconv1', bn_decay=bn_decay) 21 | net = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1], 22 | bn=True, is_training=is_training, scope='tconv2', bn_decay=bn_decay) 23 | net = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='tmaxpool') 24 | 25 | net = tf.reshape(net, [batch_size, -1]) 26 | net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='tfc1', bn_decay=bn_decay) 27 | net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='tfc2', bn_decay=bn_decay) 28 | 29 | with tf.variable_scope('transform_feat') as sc: 30 | weights = tf.get_variable('weights', [256, K*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) 31 | biases = tf.get_variable('biases', [K*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) + tf.constant(np.eye(K).flatten(), dtype=tf.float32) 32 | transform = tf.matmul(net, weights) 33 | transform = tf.nn.bias_add(transform, biases) 34 | 35 | #transform = tf_util.fully_connected(net, 3*K, activation_fn=None, scope='tfc3') 36 | transform = tf.reshape(transform, [batch_size, K, K]) 37 | return transform 38 | 39 | 40 | 41 | 42 | 43 | def get_transform(point_cloud, is_training, bn_decay=None, K = 3): 44 | """ Transform Net, input is BxNx3 gray image 45 | Return: 46 | Transformation matrix of size 3xK """ 47 | batch_size = point_cloud.get_shape()[0].value 48 | num_point = point_cloud.get_shape()[1].value 49 | 50 | input_image = tf.expand_dims(point_cloud, -1) 51 | net = tf_util.conv2d(input_image, 64, [1,3], padding='VALID', stride=[1,1], 52 | bn=True, is_training=is_training, scope='tconv1', bn_decay=bn_decay) 53 | net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], 54 | bn=True, is_training=is_training, scope='tconv3', bn_decay=bn_decay) 55 | net = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1], 56 | bn=True, is_training=is_training, scope='tconv4', bn_decay=bn_decay) 57 | net = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='tmaxpool') 58 | 59 | net = tf.reshape(net, [batch_size, -1]) 60 | net = tf_util.fully_connected(net, 128, bn=True, is_training=is_training, scope='tfc1', bn_decay=bn_decay) 61 | net = tf_util.fully_connected(net, 128, bn=True, is_training=is_training, scope='tfc2', bn_decay=bn_decay) 62 | 63 | with tf.variable_scope('transform_XYZ') as sc: 64 | assert(K==3) 65 | weights = tf.get_variable('weights', [128, 3*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) 66 | biases = tf.get_variable('biases', [3*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) + tf.constant([1,0,0,0,1,0,0,0,1], dtype=tf.float32) 67 | transform = tf.matmul(net, weights) 68 | transform = tf.nn.bias_add(transform, biases) 69 | 70 | #transform = tf_util.fully_connected(net, 3*K, activation_fn=None, scope='tfc3') 71 | transform = tf.reshape(transform, [batch_size, 3, K]) 72 | return transform 73 | 74 | 75 | def get_model(point_cloud, is_training, part_num, batch_size, \ 76 | num_point, weight_decay, bn_decay=None): 77 | """ ConvNet baseline, input is BxNx3 gray image """ 78 | end_points = {} 79 | 80 | with tf.variable_scope('transform_net1') as sc: 81 | K = 3 82 | transform = get_transform(point_cloud, is_training, bn_decay, K = 3) 83 | point_cloud_transformed = tf.matmul(point_cloud, transform) 84 | 85 | input_image = tf.expand_dims(point_cloud_transformed, -1) 86 | out1 = tf_util.conv2d(input_image, 64, [1,K], padding='VALID', stride=[1,1], 87 | bn=True, is_training=is_training, scope='conv1', bn_decay=bn_decay) 88 | out2 = tf_util.conv2d(out1, 128, [1,1], padding='VALID', stride=[1,1], 89 | bn=True, is_training=is_training, scope='conv2', bn_decay=bn_decay) 90 | out3 = tf_util.conv2d(out2, 128, [1,1], padding='VALID', stride=[1,1], 91 | bn=True, is_training=is_training, scope='conv3', bn_decay=bn_decay) 92 | 93 | 94 | with tf.variable_scope('transform_net2') as sc: 95 | K = 128 96 | transform = get_transform_K(out3, is_training, bn_decay, K) 97 | 98 | end_points['transform'] = transform 99 | 100 | squeezed_out3 = tf.reshape(out3, [batch_size, num_point, 128]) 101 | net_transformed = tf.matmul(squeezed_out3, transform) 102 | net_transformed = tf.expand_dims(net_transformed, [2]) 103 | 104 | out4 = tf_util.conv2d(net_transformed, 512, [1,1], padding='VALID', stride=[1,1], 105 | bn=True, is_training=is_training, scope='conv4', bn_decay=bn_decay) 106 | out5 = tf_util.conv2d(out4, 2048, [1,1], padding='VALID', stride=[1,1], 107 | bn=True, is_training=is_training, scope='conv5', bn_decay=bn_decay) 108 | out_max = tf_util.max_pool2d(out5, [num_point,1], padding='VALID', scope='maxpool') 109 | 110 | expand = tf.tile(out_max, [1, num_point, 1, 1]) 111 | concat = tf.concat(axis=3, values=[expand, out1, out2, out3, out4, out5]) 112 | 113 | net = tf_util.conv2d(concat, 256, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, 114 | bn=True, is_training=is_training, scope='seg/conv1', weight_decay=weight_decay) 115 | net = tf_util.dropout(net, keep_prob=0.8, is_training=is_training, scope='seg/dp1') 116 | net = tf_util.conv2d(net, 256, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, 117 | bn=True, is_training=is_training, scope='seg/conv2', weight_decay=weight_decay) 118 | net = tf_util.dropout(net, keep_prob=0.8, is_training=is_training, scope='seg/dp2') 119 | net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, 120 | bn=True, is_training=is_training, scope='seg/conv3', weight_decay=weight_decay) 121 | net = tf_util.conv2d(net, part_num, [1,1], padding='VALID', stride=[1,1], activation_fn=None, 122 | bn=False, scope='seg/conv4', weight_decay=weight_decay) 123 | 124 | net = tf.reshape(net, [batch_size, num_point, part_num]) 125 | 126 | return net, end_points 127 | 128 | def get_loss(seg_pred, seg, weight, end_points): 129 | # size of seg_pred is batch_size x point_num x part_cat_num 130 | # size of seg is batch_size x point_num 131 | class_weights = tf.constant([0.3, 0.7]) 132 | weights = tf.gather(class_weights, seg) 133 | per_instance_seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_pred, labels=seg) * weights, axis=1) 134 | seg_loss = tf.reduce_mean(per_instance_seg_loss) 135 | 136 | per_instance_seg_pred_res = tf.argmax(seg_pred, 2) 137 | 138 | # Enforce the transformation as orthogonal matrix 139 | transform = end_points['transform'] # BxKxK 140 | K = transform.get_shape()[1].value 141 | mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1])) - tf.constant(np.eye(K), dtype=tf.float32) 142 | mat_diff_loss = tf.nn.l2_loss(mat_diff) 143 | 144 | 145 | total_loss = weight * seg_loss + mat_diff_loss * 1e-3 146 | 147 | return total_loss, seg_loss, per_instance_seg_loss, per_instance_seg_pred_res 148 | 149 | -------------------------------------------------------------------------------- /pointnet_plane_detection2.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | import math 4 | import os 5 | import sys 6 | BASE_DIR = os.path.dirname(os.path.abspath(__file__)) 7 | sys.path.append(os.path.dirname(BASE_DIR)) 8 | sys.path.append(os.path.join(BASE_DIR, '../utils')) 9 | import tf_util 10 | 11 | 12 | def get_transform_K(inputs, is_training, bn_decay=None, K = 3): 13 | """ Transform Net, input is BxNx1xK gray image 14 | Return: 15 | Transformation matrix of size KxK """ 16 | batch_size = inputs.get_shape()[0].value 17 | num_point = inputs.get_shape()[1].value 18 | 19 | net = tf_util.conv2d(inputs, 256, [1,1], padding='VALID', stride=[1,1], 20 | bn=True, is_training=is_training, scope='tconv1', bn_decay=bn_decay) 21 | net = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1], 22 | bn=True, is_training=is_training, scope='tconv2', bn_decay=bn_decay) 23 | net = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='tmaxpool') 24 | 25 | net = tf.reshape(net, [batch_size, -1]) 26 | net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='tfc1', bn_decay=bn_decay) 27 | net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='tfc2', bn_decay=bn_decay) 28 | 29 | with tf.variable_scope('transform_feat') as sc: 30 | weights = tf.get_variable('weights', [256, K*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) 31 | biases = tf.get_variable('biases', [K*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) + tf.constant(np.eye(K).flatten(), dtype=tf.float32) 32 | transform = tf.matmul(net, weights) 33 | transform = tf.nn.bias_add(transform, biases) 34 | 35 | #transform = tf_util.fully_connected(net, 3*K, activation_fn=None, scope='tfc3') 36 | transform = tf.reshape(transform, [batch_size, K, K]) 37 | return transform 38 | 39 | 40 | 41 | 42 | 43 | def get_transform(point_cloud, is_training, bn_decay=None, K = 3): 44 | """ Transform Net, input is BxNx3 gray image 45 | Return: 46 | Transformation matrix of size 3xK """ 47 | batch_size = point_cloud.get_shape()[0].value 48 | num_point = point_cloud.get_shape()[1].value 49 | 50 | input_image = tf.expand_dims(point_cloud, -1) 51 | net = tf_util.conv2d(input_image, 64, [1,3], padding='VALID', stride=[1,1], 52 | bn=True, is_training=is_training, scope='tconv1', bn_decay=bn_decay) 53 | net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], 54 | bn=True, is_training=is_training, scope='tconv3', bn_decay=bn_decay) 55 | net = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1], 56 | bn=True, is_training=is_training, scope='tconv4', bn_decay=bn_decay) 57 | net = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='tmaxpool') 58 | 59 | net = tf.reshape(net, [batch_size, -1]) 60 | net = tf_util.fully_connected(net, 128, bn=True, is_training=is_training, scope='tfc1', bn_decay=bn_decay) 61 | net = tf_util.fully_connected(net, 128, bn=True, is_training=is_training, scope='tfc2', bn_decay=bn_decay) 62 | 63 | with tf.variable_scope('transform_XYZ') as sc: 64 | assert(K==3) 65 | weights = tf.get_variable('weights', [128, 3*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) 66 | biases = tf.get_variable('biases', [3*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) + tf.constant([1,0,0,0,1,0,0,0,1], dtype=tf.float32) 67 | transform = tf.matmul(net, weights) 68 | transform = tf.nn.bias_add(transform, biases) 69 | 70 | #transform = tf_util.fully_connected(net, 3*K, activation_fn=None, scope='tfc3') 71 | transform = tf.reshape(transform, [batch_size, 3, K]) 72 | return transform 73 | 74 | 75 | def get_model(point_cloud, is_training, part_num, batch_size, \ 76 | num_point, weight_decay, bn_decay=None): 77 | """ ConvNet baseline, input is BxNx3 gray image """ 78 | end_points = {} 79 | 80 | with tf.variable_scope('transform_net1') as sc: 81 | K = 3 82 | transform = get_transform(point_cloud, is_training, bn_decay, K = 3) 83 | point_cloud_transformed = tf.matmul(point_cloud, transform) 84 | 85 | input_image = tf.expand_dims(point_cloud_transformed, -1) 86 | out1 = tf_util.conv2d(input_image, 64, [1,K], padding='VALID', stride=[1,1], 87 | bn=True, is_training=is_training, scope='conv1', bn_decay=bn_decay) 88 | out2 = tf_util.conv2d(out1, 128, [1,1], padding='VALID', stride=[1,1], 89 | bn=True, is_training=is_training, scope='conv2', bn_decay=bn_decay) 90 | out3 = tf_util.conv2d(out2, 128, [1,1], padding='VALID', stride=[1,1], 91 | bn=True, is_training=is_training, scope='conv3', bn_decay=bn_decay) 92 | 93 | 94 | with tf.variable_scope('transform_net2') as sc: 95 | K = 128 96 | transform = get_transform_K(out3, is_training, bn_decay, K) 97 | 98 | end_points['transform'] = transform 99 | 100 | squeezed_out3 = tf.reshape(out3, [batch_size, num_point, 128]) 101 | net_transformed = tf.matmul(squeezed_out3, transform) 102 | net_transformed = tf.expand_dims(net_transformed, [2]) 103 | 104 | out4 = tf_util.conv2d(net_transformed, 512, [1,1], padding='VALID', stride=[1,1], 105 | bn=True, is_training=is_training, scope='conv4', bn_decay=bn_decay) 106 | out5 = tf_util.conv2d(out4, 2048, [1,1], padding='VALID', stride=[1,1], 107 | bn=True, is_training=is_training, scope='conv5', bn_decay=bn_decay) 108 | 109 | concat = tf.concat(axis=3, values=[out1, out2, out3, out4, out5]) 110 | 111 | net = tf_util.conv2d(concat, 256, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, 112 | bn=True, is_training=is_training, scope='seg/conv1', weight_decay=weight_decay) 113 | net = tf_util.dropout(net, keep_prob=0.8, is_training=is_training, scope='seg/dp1') 114 | net = tf_util.conv2d(net, 256, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, 115 | bn=True, is_training=is_training, scope='seg/conv2', weight_decay=weight_decay) 116 | net = tf_util.dropout(net, keep_prob=0.8, is_training=is_training, scope='seg/dp2') 117 | net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, 118 | bn=True, is_training=is_training, scope='seg/conv3', weight_decay=weight_decay) 119 | net = tf_util.conv2d(net, part_num, [1,1], padding='VALID', stride=[1,1], activation_fn=None, 120 | bn=False, scope='seg/conv4', weight_decay=weight_decay) 121 | 122 | net = tf.reshape(net, [batch_size, num_point, part_num]) 123 | 124 | return net, end_points 125 | 126 | def get_loss(seg_pred, seg, weight, end_points): 127 | # size of seg_pred is batch_size x point_num x part_cat_num 128 | # size of seg is batch_size x point_num 129 | class_weights = tf.constant([0.3, 0.7]) 130 | weights = tf.gather(class_weights, seg) 131 | per_instance_seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_pred, labels=seg) * weights, axis=1) 132 | seg_loss = tf.reduce_mean(per_instance_seg_loss) 133 | 134 | per_instance_seg_pred_res = tf.argmax(seg_pred, 2) 135 | 136 | # Enforce the transformation as orthogonal matrix 137 | transform = end_points['transform'] # BxKxK 138 | K = transform.get_shape()[1].value 139 | mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1])) - tf.constant(np.eye(K), dtype=tf.float32) 140 | mat_diff_loss = tf.nn.l2_loss(mat_diff) 141 | 142 | 143 | total_loss = weight * seg_loss + mat_diff_loss * 1e-3 144 | 145 | return total_loss, seg_loss, per_instance_seg_loss, per_instance_seg_pred_res 146 | 147 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import tensorflow as tf 3 | import json 4 | import numpy as np 5 | import os 6 | import sys 7 | BASE_DIR = os.path.dirname(os.path.abspath(__file__)) 8 | sys.path.append(BASE_DIR) 9 | sys.path.append(os.path.dirname(BASE_DIR)) 10 | import provider 11 | 12 | parser = argparse.ArgumentParser() 13 | parser.add_argument('--model_path', default='train_results/trained_models/epoch_100.ckpt', help='Model checkpoint path') 14 | parser.add_argument('--model', choices=["1", "2"], default="1", help='Model to use [1/2]') 15 | FLAGS = parser.parse_args() 16 | 17 | 18 | # DEFAULT SETTINGS 19 | pretrained_model_path = FLAGS.model_path # os.path.join(BASE_DIR, './pretrained_model/model.ckpt') 20 | hdf5_data_dir = os.path.join(BASE_DIR, './data/hdf5_data') 21 | ply_data_dir = os.path.join(BASE_DIR, './data') 22 | gpu_to_use = 0 23 | output_dir = os.path.join(BASE_DIR, './test_results') 24 | output_verbose = True # If true, output all color-coded part segmentation obj files 25 | 26 | if FLAGS.model == "1": 27 | model = __import__('pointnet_plane_detection') 28 | if FLAGS.model == "2": 29 | model = __import__('pointnet_plane_detection2') 30 | 31 | # MAIN SCRIPT 32 | point_num = 2048 # the max number of points in the all testing data shapes 33 | batch_size = 1 34 | 35 | test_file_list = os.path.join(BASE_DIR, 'testing_ply_file_list') 36 | 37 | color_map_file = os.path.join(hdf5_data_dir, 'plane_color_mapping.json') 38 | color_map = json.load(open(color_map_file, 'r')) 39 | 40 | NUM_PART_CATS = 2 41 | 42 | def printout(flog, data): 43 | print(data) 44 | flog.write(data + '\n') 45 | 46 | def output_color_point_cloud(data, seg, out_file): 47 | with open(out_file, 'w') as f: 48 | l = len(seg) 49 | for i in range(l): 50 | color = color_map[seg[i]] 51 | f.write('v %f %f %f %f %f %f\n' % (data[i][0], data[i][1], data[i][2], color[0], color[1], color[2])) 52 | 53 | def output_color_point_cloud_red_blue(data, seg, out_file): 54 | with open(out_file, 'w') as f: 55 | l = len(seg) 56 | for i in range(l): 57 | if seg[i] == 1: 58 | color = [0, 0, 1] 59 | elif seg[i] == 0: 60 | color = [1, 0, 0] 61 | else: 62 | color = [0, 0, 0] 63 | 64 | f.write('v %f %f %f %f %f %f\n' % (data[i][0], data[i][1], data[i][2], color[0], color[1], color[2])) 65 | 66 | 67 | def pc_normalize(pc): 68 | l = pc.shape[0] 69 | centroid = np.mean(pc, axis=0) 70 | pc = pc - centroid 71 | m = np.max(np.sqrt(np.sum(pc**2, axis=1))) 72 | pc = pc / m 73 | return pc 74 | 75 | def placeholder_inputs(): 76 | pointclouds_ph = tf.placeholder(tf.float32, shape=(batch_size, point_num, 3)) 77 | return pointclouds_ph 78 | 79 | def output_color_point_cloud(data, seg, out_file): 80 | with open(out_file, 'w') as f: 81 | l = len(seg) 82 | for i in range(l): 83 | color = color_map[seg[i]] 84 | f.write('v %f %f %f %f %f %f\n' % (data[i][0], data[i][1], data[i][2], color[0], color[1], color[2])) 85 | 86 | def load_pts_seg_files(pts_file, seg_file): 87 | with open(pts_file, 'r') as f: 88 | pts_str = [item.rstrip() for item in f.readlines()] 89 | pts = np.array([np.float32(s.split()) for s in pts_str], dtype=np.float32) 90 | with open(seg_file, 'r') as f: 91 | part_ids = np.array([int(item.rstrip()) for item in f.readlines()], dtype=np.uint8) 92 | seg = np.array([x for x in part_ids]) 93 | return pts, seg 94 | 95 | def pc_augment_to_point_num(pts, pn): 96 | assert(pts.shape[0] <= pn) 97 | cur_len = pts.shape[0] 98 | res = np.array(pts) 99 | while cur_len < pn: 100 | res = np.concatenate((res, pts)) 101 | cur_len += pts.shape[0] 102 | return res[:pn, :] 103 | 104 | def predict(): 105 | is_training = False 106 | with tf.device('/gpu:'+str(gpu_to_use)): 107 | pointclouds_ph = placeholder_inputs() 108 | is_training_ph = tf.placeholder(tf.bool, shape=()) 109 | 110 | # simple model 111 | seg_pred, end_points = model.get_model(pointclouds_ph, \ 112 | part_num=NUM_PART_CATS, is_training=is_training_ph, \ 113 | batch_size=batch_size, num_point=point_num, weight_decay=0.0, bn_decay=None) 114 | 115 | # Add ops to save and restore all the variables. 116 | saver = tf.train.Saver() 117 | 118 | # Later, launch the model, use the saver to restore variables from disk, and 119 | # do some work with the model. 120 | 121 | config = tf.ConfigProto() 122 | config.gpu_options.allow_growth = True 123 | config.allow_soft_placement = True 124 | 125 | with tf.Session(config=config) as sess: 126 | if not os.path.exists(output_dir): 127 | os.mkdir(output_dir) 128 | 129 | flog = open(os.path.join(output_dir, 'log.txt'), 'w') 130 | 131 | # Restore variables from disk. 132 | printout(flog, 'Loading model %s' % pretrained_model_path) 133 | saver.restore(sess, pretrained_model_path) 134 | printout(flog, 'Model restored.') 135 | 136 | # Note: the evaluation for the model with BN has to have some statistics 137 | # Using some test datas as the statistics 138 | batch_data = np.zeros([batch_size, point_num, 3]).astype(np.float32) 139 | 140 | total_acc = 0.0 141 | total_seen = 0 142 | total_acc_iou = 0.0 143 | 144 | ffiles = open(test_file_list, 'r') 145 | lines = [line.rstrip() for line in ffiles.readlines()] 146 | pts_files = [line.split()[0] for line in lines] 147 | seg_files = [line.split()[1] for line in lines] 148 | ffiles.close() 149 | 150 | len_pts_files = len(pts_files) 151 | for shape_idx in range(len_pts_files): 152 | if shape_idx % 100 == 0: 153 | printout(flog, '%d/%d ...' % (shape_idx, len_pts_files)) 154 | 155 | pts_file_to_load = os.path.join(ply_data_dir, pts_files[shape_idx]) 156 | seg_file_to_load = os.path.join(ply_data_dir, seg_files[shape_idx]) 157 | 158 | pts, seg = load_pts_seg_files(pts_file_to_load, seg_file_to_load) 159 | ori_point_num = len(seg) 160 | 161 | batch_data[0, ...] = pc_augment_to_point_num(pc_normalize(pts), point_num) 162 | 163 | seg_pred_res = sess.run([seg_pred], feed_dict={ 164 | pointclouds_ph: batch_data, 165 | is_training_ph: is_training, 166 | }) 167 | 168 | seg_pred_res = np.array(seg_pred_res)[0, ...][0, ...] 169 | 170 | iou_oids = [0, 1] 171 | 172 | seg_pred_val = np.argmax(seg_pred_res, axis=1)[:ori_point_num] 173 | 174 | print seg_pred_val 175 | 176 | seg_acc = np.mean(seg_pred_val == seg) 177 | 178 | total_acc += seg_acc 179 | total_seen += 1 180 | 181 | mask = np.int32(seg_pred_val == seg) 182 | 183 | total_iou = 0.0 184 | iou_log = '' 185 | for oid in iou_oids: 186 | n_pred = np.sum(seg_pred_val == oid) 187 | n_gt = np.sum(seg == oid) 188 | n_intersect = np.sum(np.int32(seg == oid) * mask) 189 | n_union = n_pred + n_gt - n_intersect 190 | iou_log += '_' + str(n_pred)+'_'+str(n_gt)+'_'+str(n_intersect)+'_'+str(n_union)+'_' 191 | if n_union == 0: 192 | total_iou += 1 193 | iou_log += '_1\n' 194 | else: 195 | total_iou += n_intersect * 1.0 / n_union 196 | iou_log += '_'+str(n_intersect * 1.0 / n_union)+'\n' 197 | 198 | avg_iou = total_iou / len(iou_oids) 199 | total_acc_iou += avg_iou 200 | 201 | if output_verbose: 202 | output_color_point_cloud(pts, seg, os.path.join(output_dir, str(shape_idx)+'_gt.obj')) 203 | output_color_point_cloud(pts, seg_pred_val, os.path.join(output_dir, str(shape_idx)+'_pred.obj')) 204 | output_color_point_cloud_red_blue(pts, np.int32(seg == seg_pred_val), 205 | os.path.join(output_dir, str(shape_idx)+'_diff.obj')) 206 | 207 | with open(os.path.join(output_dir, str(shape_idx)+'.log'), 'w') as fout: 208 | fout.write('Total Point: %d\n\n' % ori_point_num) 209 | fout.write('Accuracy: %f\n' % seg_acc) 210 | fout.write('IoU: %f\n\n' % avg_iou) 211 | fout.write('IoU details: %s\n' % iou_log) 212 | 213 | printout(flog, 'Accuracy: %f' % (total_acc / total_seen)) 214 | printout(flog, 'IoU: %f' % (total_acc_iou / total_seen)) 215 | 216 | 217 | with tf.Graph().as_default(): 218 | predict() 219 | -------------------------------------------------------------------------------- /testing_ply_file_list: -------------------------------------------------------------------------------- 1 | points/table1.pts points_label/table1.seg 2 | points/chair1.pts points_label/chair1.seg 3 | points/table2.pts points_label/table2.seg 4 | points/chair2.pts points_label/chair2.seg 5 | points/table3.pts points_label/table3.seg 6 | points/chair3.pts points_label/chair3.seg 7 | points/table4.pts points_label/table4.seg 8 | points/chair4.pts points_label/chair4.seg 9 | points/chair5.pts points_label/chair5.seg 10 | points/table5.pts points_label/table5.seg 11 | points/table6.pts points_label/table6.seg 12 | points/chair6.pts points_label/chair6.seg 13 | points/chair7.pts points_label/chair7.seg 14 | points/table7.pts points_label/table7.seg 15 | points/table8.pts points_label/table8.seg 16 | points/chair8.pts points_label/chair8.seg 17 | -------------------------------------------------------------------------------- /testing_result.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IsaacGuan/PointNet-Plane-Detection/d53acc9cb42d05ddcd1c6e4e45976ae5b8aefad4/testing_result.jpg -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import subprocess 3 | import tensorflow as tf 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | from datetime import datetime 7 | import json 8 | import os 9 | import sys 10 | BASE_DIR = os.path.dirname(os.path.abspath(__file__)) 11 | sys.path.append(BASE_DIR) 12 | sys.path.append(os.path.dirname(BASE_DIR)) 13 | import provider 14 | 15 | # DEFAULT SETTINGS 16 | parser = argparse.ArgumentParser() 17 | parser.add_argument('--gpu', type=int, default=1, help='GPU to use [default: GPU 0]') 18 | parser.add_argument('--model', choices=["1", "2"], default="1", help='Model to use [1/2]') 19 | parser.add_argument('--batch', type=int, default=16, help='Batch Size during training [default: 16]') 20 | parser.add_argument('--epoch', type=int, default=100, help='Epoch to run [default: 100]') 21 | parser.add_argument('--point_num', type=int, default=2048, help='Point Number [256/512/1024/2048]') 22 | parser.add_argument('--output_dir', type=str, default='train_results', help='Directory that stores all training logs and trained models') 23 | parser.add_argument('--wd', type=float, default=0, help='Weight Decay [Default: 0.0]') 24 | FLAGS = parser.parse_args() 25 | 26 | hdf5_data_dir = os.path.join(BASE_DIR, './data/hdf5_data') 27 | 28 | # MAIN SCRIPT 29 | point_num = FLAGS.point_num 30 | batch_size = FLAGS.batch 31 | output_dir = FLAGS.output_dir 32 | 33 | if FLAGS.model == "1": 34 | model = __import__('pointnet_plane_detection') 35 | if FLAGS.model == "2": 36 | model = __import__('pointnet_plane_detection2') 37 | 38 | if not os.path.exists(output_dir): 39 | os.mkdir(output_dir) 40 | 41 | NUM_PART_CATS = 2 42 | 43 | print('#### Batch Size: {0}'.format(batch_size)) 44 | print('#### Point Number: {0}'.format(point_num)) 45 | print('#### Training using GPU: {0}'.format(FLAGS.gpu)) 46 | 47 | DECAY_STEP = 16881 * 20 48 | DECAY_RATE = 0.5 49 | 50 | LEARNING_RATE_CLIP = 1e-5 51 | 52 | BN_INIT_DECAY = 0.5 53 | BN_DECAY_DECAY_RATE = 0.5 54 | BN_DECAY_DECAY_STEP = float(DECAY_STEP * 2) 55 | BN_DECAY_CLIP = 0.99 56 | 57 | BASE_LEARNING_RATE = 0.001 58 | MOMENTUM = 0.9 59 | TRAINING_EPOCHES = FLAGS.epoch 60 | print('### Training epoch: {0}'.format(TRAINING_EPOCHES)) 61 | 62 | MODEL_STORAGE_PATH = os.path.join(output_dir, 'trained_models') 63 | if not os.path.exists(MODEL_STORAGE_PATH): 64 | os.mkdir(MODEL_STORAGE_PATH) 65 | 66 | LOG_STORAGE_PATH = os.path.join(output_dir, 'logs') 67 | if not os.path.exists(LOG_STORAGE_PATH): 68 | os.mkdir(LOG_STORAGE_PATH) 69 | 70 | SUMMARIES_FOLDER = os.path.join(output_dir, 'summaries') 71 | if not os.path.exists(SUMMARIES_FOLDER): 72 | os.mkdir(SUMMARIES_FOLDER) 73 | 74 | DIAGRAMS_FOLDER = os.path.join(output_dir, 'diagrams') 75 | if not os.path.exists(DIAGRAMS_FOLDER): 76 | os.mkdir(DIAGRAMS_FOLDER) 77 | 78 | def printout(flog, data): 79 | print(data) 80 | flog.write(data + '\n') 81 | 82 | def placeholder_inputs(): 83 | pointclouds_ph = tf.placeholder(tf.float32, shape=(batch_size, point_num, 3)) 84 | seg_ph = tf.placeholder(tf.int32, shape=(batch_size, point_num)) 85 | return pointclouds_ph, seg_ph 86 | 87 | def train(): 88 | training_loss_value = [] 89 | 90 | with tf.Graph().as_default(): 91 | with tf.device('/gpu:'+str(FLAGS.gpu)): 92 | pointclouds_ph, seg_ph = placeholder_inputs() 93 | is_training_ph = tf.placeholder(tf.bool, shape=()) 94 | 95 | batch = tf.Variable(0, trainable=False) 96 | learning_rate = tf.train.exponential_decay( 97 | BASE_LEARNING_RATE, # base learning rate 98 | batch * batch_size, # global_var indicating the number of steps 99 | DECAY_STEP, # step size 100 | DECAY_RATE, # decay rate 101 | staircase=True # Stair-case or continuous decreasing 102 | ) 103 | learning_rate = tf.maximum(learning_rate, LEARNING_RATE_CLIP) 104 | 105 | bn_momentum = tf.train.exponential_decay( 106 | BN_INIT_DECAY, 107 | batch*batch_size, 108 | BN_DECAY_DECAY_STEP, 109 | BN_DECAY_DECAY_RATE, 110 | staircase=True) 111 | bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) 112 | 113 | lr_op = tf.summary.scalar('learning_rate', learning_rate) 114 | batch_op = tf.summary.scalar('batch_number', batch) 115 | bn_decay_op = tf.summary.scalar('bn_decay', bn_decay) 116 | 117 | seg_pred, end_points = model.get_model(pointclouds_ph, is_training=is_training_ph, \ 118 | bn_decay=bn_decay, part_num=NUM_PART_CATS, batch_size=batch_size, \ 119 | num_point=point_num, weight_decay=FLAGS.wd) 120 | 121 | # model.py defines both classification net and segmentation net, which share the common global feature extractor network. 122 | # In model.get_loss, we define the total loss to be weighted sum of the classification and segmentation losses. 123 | # Here, we only train for segmentation network. Thus, we set weight to be 1.0. 124 | loss, seg_loss, per_instance_seg_loss, per_instance_seg_pred_res \ 125 | = model.get_loss(seg_pred, seg_ph, 1.0, end_points) 126 | 127 | total_training_loss_ph = tf.placeholder(tf.float32, shape=()) 128 | total_testing_loss_ph = tf.placeholder(tf.float32, shape=()) 129 | 130 | seg_training_loss_ph = tf.placeholder(tf.float32, shape=()) 131 | seg_testing_loss_ph = tf.placeholder(tf.float32, shape=()) 132 | 133 | seg_training_acc_ph = tf.placeholder(tf.float32, shape=()) 134 | seg_testing_acc_ph = tf.placeholder(tf.float32, shape=()) 135 | seg_testing_acc_avg_cat_ph = tf.placeholder(tf.float32, shape=()) 136 | 137 | total_train_loss_sum_op = tf.summary.scalar('total_training_loss', total_training_loss_ph) 138 | total_test_loss_sum_op = tf.summary.scalar('total_testing_loss', total_testing_loss_ph) 139 | 140 | seg_train_loss_sum_op = tf.summary.scalar('seg_training_loss', seg_training_loss_ph) 141 | seg_test_loss_sum_op = tf.summary.scalar('seg_testing_loss', seg_testing_loss_ph) 142 | 143 | seg_train_acc_sum_op = tf.summary.scalar('seg_training_acc', seg_training_acc_ph) 144 | seg_test_acc_sum_op = tf.summary.scalar('seg_testing_acc', seg_testing_acc_ph) 145 | seg_test_acc_avg_cat_op = tf.summary.scalar('seg_testing_acc_avg_cat', seg_testing_acc_avg_cat_ph) 146 | 147 | train_variables = tf.trainable_variables() 148 | 149 | trainer = tf.train.AdamOptimizer(learning_rate) 150 | train_op = trainer.minimize(loss, var_list=train_variables, global_step=batch) 151 | 152 | saver = tf.train.Saver() 153 | 154 | config = tf.ConfigProto() 155 | config.gpu_options.allow_growth = True 156 | config.allow_soft_placement = True 157 | sess = tf.Session(config=config) 158 | 159 | init = tf.global_variables_initializer() 160 | sess.run(init) 161 | 162 | train_writer = tf.summary.FileWriter(SUMMARIES_FOLDER + '/train', sess.graph) 163 | test_writer = tf.summary.FileWriter(SUMMARIES_FOLDER + '/test') 164 | 165 | fcmd = open(os.path.join(LOG_STORAGE_PATH, 'cmd.txt'), 'w') 166 | fcmd.write(str(FLAGS)) 167 | fcmd.close() 168 | 169 | # write logs to the disk 170 | flog = open(os.path.join(LOG_STORAGE_PATH, 'log.txt'), 'w') 171 | 172 | def train_one_epoch(epoch_num): 173 | is_training = True 174 | 175 | train_filename = os.path.join(hdf5_data_dir, "data_training.h5") 176 | printout(flog, 'Loading train file ' + train_filename) 177 | 178 | cur_data, cur_seg = provider.loadDataFile_with_seg(train_filename) 179 | 180 | num_data = len(cur_data) 181 | num_batch = num_data // batch_size 182 | 183 | total_loss = 0.0 184 | total_seg_loss = 0.0 185 | total_seg_acc = 0.0 186 | 187 | for i in range(num_batch): 188 | begidx = i * batch_size 189 | endidx = (i + 1) * batch_size 190 | 191 | feed_dict = { 192 | pointclouds_ph: cur_data[begidx: endidx, ...], 193 | seg_ph: cur_seg[begidx: endidx, ...], 194 | is_training_ph: is_training, 195 | } 196 | 197 | _, loss_val, seg_loss_val, per_instance_seg_loss_val, seg_pred_val, pred_seg_res \ 198 | = sess.run([train_op, loss, seg_loss, per_instance_seg_loss, seg_pred, \ 199 | per_instance_seg_pred_res], feed_dict=feed_dict) 200 | 201 | ''' 202 | seg_pred_res = sess.run([seg_pred], feed_dict={ 203 | pointclouds_ph: cur_data[begidx: endidx, ...], 204 | is_training_ph: is_training, 205 | }) 206 | seg_pred_res = np.array(seg_pred_res)[0, ...][0, ...] 207 | 208 | seg_pred_val = np.argmax(seg_pred_res, axis=1)[:batch_size*point_num] 209 | print seg_pred_val 210 | ''' 211 | 212 | per_instance_part_acc = np.mean(pred_seg_res == cur_seg[begidx: endidx, ...], axis=1) 213 | average_part_acc = np.mean(per_instance_part_acc) 214 | 215 | total_loss += loss_val 216 | total_seg_loss += seg_loss_val 217 | 218 | total_seg_acc += average_part_acc 219 | 220 | total_loss = total_loss * 1.0 / num_batch 221 | total_seg_loss = total_seg_loss * 1.0 / num_batch 222 | total_seg_acc = total_seg_acc * 1.0 / num_batch 223 | 224 | lr_sum, bn_decay_sum, batch_sum, train_loss_sum, train_seg_loss_sum, train_seg_acc_sum \ 225 | = sess.run([lr_op, bn_decay_op, batch_op, total_train_loss_sum_op, \ 226 | seg_train_loss_sum_op, seg_train_acc_sum_op], feed_dict={total_training_loss_ph: total_loss, \ 227 | seg_training_loss_ph: total_seg_loss, seg_training_acc_ph: total_seg_acc}) 228 | 229 | train_writer.add_summary(train_loss_sum, epoch_num) 230 | train_writer.add_summary(train_seg_loss_sum, epoch_num) 231 | train_writer.add_summary(lr_sum, epoch_num) 232 | train_writer.add_summary(bn_decay_sum, epoch_num) 233 | train_writer.add_summary(train_seg_acc_sum, epoch_num) 234 | train_writer.add_summary(batch_sum, epoch_num) 235 | 236 | printout(flog, '\tTraining Total Mean_loss: %f' % total_loss) 237 | printout(flog, '\t\tTraining Seg Mean_loss: %f' % total_seg_loss) 238 | printout(flog, '\t\tTraining Seg Accuracy: %f' % total_seg_acc) 239 | 240 | training_loss_value.append(total_loss) 241 | 242 | def eval_one_epoch(epoch_num): 243 | is_training = False 244 | 245 | total_loss = 0.0 246 | total_seg_loss = 0.0 247 | total_seg_acc = 0.0 248 | total_seen = 0 249 | 250 | test_filename = os.path.join(hdf5_data_dir, "data_testing.h5") 251 | printout(flog, 'Loading test file ' + test_filename) 252 | 253 | cur_data, cur_seg = provider.loadDataFile_with_seg(test_filename) 254 | 255 | num_data = len(cur_data) 256 | num_batch = num_data // batch_size 257 | 258 | for i in range(num_batch): 259 | begidx = i * batch_size 260 | endidx = (i + 1) * batch_size 261 | feed_dict = { 262 | pointclouds_ph: cur_data[begidx: endidx, ...], 263 | seg_ph: cur_seg[begidx: endidx, ...], 264 | is_training_ph: is_training, 265 | } 266 | 267 | loss_val, seg_loss_val, per_instance_seg_loss_val, seg_pred_val, pred_seg_res \ 268 | = sess.run([loss, seg_loss, per_instance_seg_loss, seg_pred, \ 269 | per_instance_seg_pred_res], feed_dict=feed_dict) 270 | 271 | ''' 272 | seg_pred_res = sess.run([seg_pred], feed_dict={ 273 | pointclouds_ph: cur_data[begidx: endidx, ...], 274 | is_training_ph: is_training, 275 | }) 276 | seg_pred_res = np.array(seg_pred_res)[0, ...][0, ...] 277 | 278 | seg_pred_val = np.argmax(seg_pred_res, axis=1)[:batch_size*point_num] 279 | print seg_pred_val 280 | ''' 281 | 282 | per_instance_part_acc = np.mean(pred_seg_res == cur_seg[begidx: endidx, ...], axis=1) 283 | average_part_acc = np.mean(per_instance_part_acc) 284 | 285 | total_seen += 1 286 | total_loss += loss_val 287 | total_seg_loss += seg_loss_val 288 | 289 | total_seg_acc += average_part_acc 290 | 291 | total_loss = total_loss * 1.0 / total_seen 292 | total_seg_loss = total_seg_loss * 1.0 / total_seen 293 | total_seg_acc = total_seg_acc * 1.0 / total_seen 294 | 295 | test_loss_sum, test_seg_loss_sum, test_seg_acc_sum \ 296 | = sess.run([total_test_loss_sum_op, seg_test_loss_sum_op, seg_test_acc_sum_op], \ 297 | feed_dict={total_testing_loss_ph: total_loss, seg_testing_loss_ph: total_seg_loss, seg_testing_acc_ph: total_seg_acc}) 298 | 299 | test_writer.add_summary(test_loss_sum, epoch_num+1) 300 | test_writer.add_summary(test_seg_loss_sum, epoch_num+1) 301 | test_writer.add_summary(test_seg_acc_sum, epoch_num+1) 302 | 303 | printout(flog, '\tTesting Total Mean_loss: %f' % total_loss) 304 | printout(flog, '\t\tTesting Seg Mean_loss: %f' % total_seg_loss) 305 | printout(flog, '\t\tTesting Seg Accuracy: %f' % total_seg_acc) 306 | 307 | if not os.path.exists(MODEL_STORAGE_PATH): 308 | os.mkdir(MODEL_STORAGE_PATH) 309 | 310 | for epoch in range(TRAINING_EPOCHES): 311 | printout(flog, '\n>>> Training for the epoch %d/%d ...' % (epoch+1, TRAINING_EPOCHES)) 312 | train_one_epoch(epoch) 313 | 314 | printout(flog, '\n<<< Testing on the test dataset ...') 315 | eval_one_epoch(epoch) 316 | 317 | if (epoch+1) % 10 == 0: 318 | cp_filename = saver.save(sess, os.path.join(MODEL_STORAGE_PATH, 'epoch_' + str(epoch+1)+'.ckpt')) 319 | printout(flog, 'Successfully store the checkpoint model into ' + cp_filename) 320 | 321 | flog.flush() 322 | 323 | plt.plot(np.arange(1, TRAINING_EPOCHES + 1), training_loss_value, 'ro') 324 | plt.plot(np.arange(1, TRAINING_EPOCHES + 1), training_loss_value) 325 | plt.ylabel('Total Mean Loss') 326 | plt.xlabel('Epoch') 327 | plt.title('Total Mean Loss per Epoch') 328 | plt.savefig(DIAGRAMS_FOLDER + '/total_mean_loss.png') 329 | 330 | flog.close() 331 | 332 | if __name__=='__main__': 333 | train() 334 | -------------------------------------------------------------------------------- /training_data.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IsaacGuan/PointNet-Plane-Detection/d53acc9cb42d05ddcd1c6e4e45976ae5b8aefad4/training_data.jpg --------------------------------------------------------------------------------