data_process/lib/models/networks/util.py

"""This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
from math import *

def P2sRt(P):
    ''' decompositing camera matrix P.
    Args:
        P: (3, 4). Affine Camera Matrix.
    Returns:
        s: scale factor.
        R: (3, 3). rotation matrix.
        t2d: (2,). 2d translation.
    '''
    t3d = P[:, 3]
    R1 = P[0:1, :3]
    R2 = P[1:2, :3]
    s = (np.linalg.norm(R1) + np.linalg.norm(R2)) / 2.0
    r1 = R1 / np.linalg.norm(R1)
    r2 = R2 / np.linalg.norm(R2)
    r3 = np.cross(r1, r2)

    R = np.concatenate((r1, r2, r3), 0)
    return s, R, t3d

def matrix2angle(R):
    ''' compute three Euler angles from a Rotation Matrix. Ref: http://www.gregslabaugh.net/publications/euler.pdf
    Args:
        R: (3,3). rotation matrix
    Returns:
        x: yaw
        y: pitch
        z: roll
    '''
    # assert(isRotationMatrix(R))

    if R[2, 0] != 1 and R[2, 0] != -1:
        x = -asin(max(-1, min(R[2, 0], 1)))
        y = atan2(R[2, 1] / cos(x), R[2, 2] / cos(x))
        z = atan2(R[1, 0] / cos(x), R[0, 0] / cos(x))

    else:  # Gimbal lock
        z = 0  # can be anything
        if R[2, 0] == -1:
            x = np.pi / 2
            y = z + atan2(R[0, 1], R[0, 2])
        else:
            x = -np.pi / 2
            y = -z + atan2(-R[0, 1], -R[0, 2])

    return [x, y, z]

def angle2matrix(angles):
    ''' get rotation matrix from three rotation angles(radian). The same as in 3DDFA.
    Args:
        angles: [3,]. x, y, z angles
        x: yaw.
        y: pitch.
        z: roll.
    Returns:
        R: 3x3. rotation matrix.
    '''
    # x, y, z = np.deg2rad(angles[0]), np.deg2rad(angles[1]), np.deg2rad(angles[2])
    # x, y, z = angles[0], angles[1], angles[2]
    y, x, z = angles[0], angles[1], angles[2]

    # x
    Rx=np.array([[1,      0,       0],
                 [0, cos(x),  -sin(x)],
                 [0, sin(x),  cos(x)]])
    # y
    Ry=np.array([[ cos(y), 0, sin(y)],
                 [      0, 1,      0],
                 [-sin(y), 0, cos(y)]])
    # z
    Rz=np.array([[cos(z), -sin(z), 0],
                 [sin(z),  cos(z), 0],
                 [     0,       0, 1]])
    R = Rz.dot(Ry).dot(Rx)
    return R.astype(np.float32)

def tensor2im(input_image, imtype=np.uint8):
    """"Converts a Tensor array into a numpy image array.

    Parameters:
        input_image (tensor) --  the input image tensor array
        imtype (type)        --  the desired type of the converted numpy array
    """
    if not isinstance(input_image, np.ndarray):
        if isinstance(input_image, torch.Tensor):  # get the data from a variable
            image_tensor = input_image.data
        else:
            return input_image
        image_numpy = image_tensor[0].cpu().float().numpy()  # convert it into a numpy array
        if image_numpy.shape[0] == 1:  # grayscale to RGB
            image_numpy = np.tile(image_numpy, (3, 1, 1))
        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0  # post-processing: tranpose and scaling
    else:  # if it is a numpy array, do nothing
        image_numpy = input_image
    return image_numpy.astype(imtype)


def diagnose_network(net, name='network'):
    """Calculate and print the mean of average absolute(gradients)

    Parameters:
        net (torch network) -- Torch network
        name (str) -- the name of the network
    """
    mean = 0.0
    count = 0
    for param in net.parameters():
        if param.grad is not None:
            mean += torch.mean(torch.abs(param.grad.data))
            count += 1
    if count > 0:
        mean = mean / count
    print(name)
    print(mean)


def save_image(image_numpy, image_path):
    """Save a numpy image to the disk

    Parameters:
        image_numpy (numpy array) -- input numpy array
        image_path (str)          -- the path of the image
    """
    image_pil = Image.fromarray(image_numpy)
    image_pil.save(image_path)


def print_numpy(x, val=True, shp=False):
    """Print the mean, min, max, median, std, and size of a numpy array

    Parameters:
        val (bool) -- if print the values of the numpy array
        shp (bool) -- if print the shape of the numpy array
    """
    x = x.astype(np.float64)
    if shp:
        print('shape,', x.shape)
    if val:
        x = x.flatten()
        print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
            np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))


def mkdirs(paths):
    """create empty directories if they don't exist

    Parameters:
        paths (str list) -- a list of directory paths
    """
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    """create a single empty directory if it didn't exist

    Parameters:
        path (str) -- a single directory path
    """
    if not os.path.exists(path):
        os.makedirs(path)