provider.py

import os
import sys
import numpy as np
import h5py
import transforms3d.euler as t3d
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)

def shuffle_data(data, labels):
    """ Shuffle data and labels.
        Input:
          data: B,N,... numpy array
          label: B,... numpy array
        Return:
          shuffled data, label and shuffle indices
    """
    idx = np.arange(len(labels))
    np.random.shuffle(idx)
    return data[idx, ...], labels[idx], idx

def shuffle_points(batch_data):
    """ Shuffle orders of points in each point cloud -- changes FPS behavior.
        Use the same shuffling idx for the entire batch.
        Input:
            BxNxC array
        Output:
            BxNxC array
    """
    idx = np.arange(batch_data.shape[1])
    np.random.shuffle(idx)
    return batch_data[:,idx,:]

def rotate_point_cloud(batch_data):
    """ Randomly rotate the point clouds to augument the dataset
        rotation is per shape based along up direction
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, rotated batch of point clouds
    """
    rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
    for k in range(batch_data.shape[0]):
        rotation_angle = np.random.uniform() * 2 * np.pi
        cosval = np.cos(rotation_angle)
        sinval = np.sin(rotation_angle)
        rotation_matrix = np.array([[cosval, 0, sinval],
                                    [0, 1, 0],
                                    [-sinval, 0, cosval]])
        shape_pc = batch_data[k, ...]
        rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
    return rotated_data

def rotate_point_cloud_pose(batch_data, angle_sigma=0.06, angle_clip=0.18):
#def rotate_point_cloud_pose(batch_data, poses):
    """ Randomly perturb the point clouds by small rotations
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, rotated batch of point clouds
    """
    rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
    #angles=poses
    #print(poses)
    poses=[]
    for k in range(batch_data.shape[0]):
        angles = np.clip(angle_sigma, -angle_clip, angle_clip)
        #angles = np.clip(angle_sigma*np.ones((3)), -angle_clip, angle_clip)
        #angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
        poses.append([0,0,0,angles[0],angles[1],angles[2]])
        #angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
        Rx = np.array([[1,0,0],
                       [0,np.cos(angles[0]),-np.sin(angles[0])],
                       [0,np.sin(angles[0]),np.cos(angles[0])]])
        Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])],
                       [0,1,0],
                       [-np.sin(angles[1]),0,np.cos(angles[1])]])
        Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0],
                       [np.sin(angles[2]),np.cos(angles[2]),0],
                       [0,0,1]])
        R = np.dot(Rz, np.dot(Ry,Rx))
        #print(R)
        R=t3d.euler2mat(angles[0],angles[1],angles[2],'sxyz')
        #print(R)
        #assert False
        shape_pc = batch_data[k, ...]
        rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R)
        #rotated_data[k, ...] = np.dot(R,shape_pc.reshape((3,-1))).T
    poses=np.concatenate(poses,axis=0)
    return rotated_data,poses
def rotate_point_cloud_with_normal(batch_xyz_normal):
    ''' Randomly rotate XYZ, normal point cloud.
        Input:
            batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal
        Output:
            B,N,6, rotated XYZ, normal point cloud
    '''
    for k in range(batch_xyz_normal.shape[0]):
        rotation_angle = np.random.uniform() * 2 * np.pi
        cosval = np.cos(rotation_angle)
        sinval = np.sin(rotation_angle)
        rotation_matrix = np.array([[cosval, 0, sinval],
                                    [0, 1, 0],
                                    [-sinval, 0, cosval]])
        shape_pc = batch_xyz_normal[k,:,0:3]
        shape_normal = batch_xyz_normal[k,:,3:6]
        batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
        batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix)
    return batch_xyz_normal

def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18):
    """ Randomly perturb the point clouds by small rotations
        Input:
          BxNx6 array, original batch of point clouds and point normals
        Return:
          BxNx3 array, rotated batch of point clouds
    """
    rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
    for k in range(batch_data.shape[0]):
        angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
        Rx = np.array([[1,0,0],
                       [0,np.cos(angles[0]),-np.sin(angles[0])],
                       [0,np.sin(angles[0]),np.cos(angles[0])]])
        Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])],
                       [0,1,0],
                       [-np.sin(angles[1]),0,np.cos(angles[1])]])
        Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0],
                       [np.sin(angles[2]),np.cos(angles[2]),0],
                       [0,0,1]])
        R = np.dot(Rz, np.dot(Ry,Rx))
        shape_pc = batch_data[k,:,0:3]
        shape_normal = batch_data[k,:,3:6]
        rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R)
        rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R)
    return rotated_data


def rotate_point_cloud_by_angle(batch_data, rotation_angle):
    """ Rotate the point cloud along up direction with certain angle.
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, rotated batch of point clouds
    """
    rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
    for k in range(batch_data.shape[0]):
        #rotation_angle = np.random.uniform() * 2 * np.pi
        cosval = np.cos(rotation_angle)
        sinval = np.sin(rotation_angle)
        rotation_matrix = np.array([[cosval, 0, sinval],
                                    [0, 1, 0],
                                    [-sinval, 0, cosval]])
        shape_pc = batch_data[k,:,0:3]
        rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
    return rotated_data

def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle):
    """ Rotate the point cloud along up direction with certain angle.
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, rotated batch of point clouds
    """
    rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
    for k in range(batch_data.shape[0]):
        #rotation_angle = np.random.uniform() * 2 * np.pi
        cosval = np.cos(rotation_angle)
        sinval = np.sin(rotation_angle)
        rotation_matrix = np.array([[cosval, 0, sinval],
                                    [0, 1, 0],
                                    [-sinval, 0, cosval]])
        shape_pc = batch_data[k, ...]
        shape_normal = batch_data[k,:,3:6]
        rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
        rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix)
    return rotated_data



def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18):
    """ Randomly perturb the point clouds by small rotations
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, rotated batch of point clouds
    """
    rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
    for k in range(batch_data.shape[0]):
        #angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
        angles = np.clip(angle_sigma*(2*np.random.rand(3)-1), -angle_clip, angle_clip)
        #print(angles)

        Rx = np.array([[1,0,0],
                       [0,np.cos(angles[0]),-np.sin(angles[0])],
                       [0,np.sin(angles[0]),np.cos(angles[0])]])
        Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])],
                       [0,1,0],
                       [-np.sin(angles[1]),0,np.cos(angles[1])]])
        Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0],
                       [np.sin(angles[2]),np.cos(angles[2]),0],
                       [0,0,1]])
        R = np.dot(Rz, np.dot(Ry,Rx))
        shape_pc = batch_data[k, ...]
        rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R)
    return rotated_data


def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05):
    """ Randomly jitter points. jittering is per point.
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, jittered batch of point clouds
    """
    B, N, C = batch_data.shape
    assert(clip > 0)
    #noise=2*np.random.uniform(size=(B, N, C))-1
    noise=np.random.randn(B, N, C)
    jittered_data = np.clip(sigma * noise, -1*clip, clip)
    jittered_data += batch_data
    return jittered_data

def shift_point_cloud(batch_data, shift_range=0.1):
    """ Randomly shift point cloud. Shift is per point cloud.
        Input:
          BxNx3 array, original batch of point clouds
        Return:
          BxNx3 array, shifted batch of point clouds
    """
    B, N, C = batch_data.shape
    shifts = np.random.uniform(-shift_range, shift_range, (B,3))
    for batch_index in range(B):
        batch_data[batch_index,:,:] += shifts[batch_index,:]
    return batch_data


def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25):
    """ Randomly scale the point cloud. Scale is per point cloud.
        Input:
            BxNx3 array, original batch of point clouds
        Return:
            BxNx3 array, scaled batch of point clouds
    """
    B, N, C = batch_data.shape
    scales = np.random.uniform(scale_low, scale_high, B)
    for batch_index in range(B):
        batch_data[batch_index,:,:] *= scales[batch_index]
    return batch_data

def random_point_dropout(batch_pc, max_dropout_ratio=0.875):
    ''' batch_pc: BxNx3 '''
    for b in range(batch_pc.shape[0]):
        dropout_ratio =  np.random.random()*max_dropout_ratio # 0~0.875
        drop_idx = np.where(np.random.random((batch_pc.shape[1]))<=dropout_ratio)[0]
        if len(drop_idx)>0:
            batch_pc[b,drop_idx,:] = batch_pc[b,0,:] # set to the first point
    return batch_pc


def getDataFiles(list_filename):
    return [line.rstrip() for line in open(list_filename)]

def load_h5(h5_filename):
    f = h5py.File(h5_filename)
    data = f['data'][:]
    label = f['label'][:]
    return (data, label)

def loadDataFile(filename):
    return load_h5(filename)