mukheshpugal

import argparse

alu = {
	'0000':'add',	'1000':'sub',
	'0001':'sll',
	'0010':'slt',
	'0011':'sltu',
	'0100':'xor',
	'0101':'srl',	'1101':'sra',
	'0110':'or',

mukheshpugal

Keybase proof

I hereby claim:

• I am mukheshpugal on github.
• I am mukheshpugal (https://keybase.io/mukheshpugal) on keybase.
• I have a public key ASAnzPkYW8hqdYC1cCo_wcSkxtGqXSd900rQo6Zl0NCMJQo

To claim this, I am signing this object:

mukheshpugal

class Exp_SE3(Function):
    """
    Implements exp SO3 as a torch fuction
    """

    @staticmethod
    def forward(ctx, vec: "Tensor[6]") -> "Tensor[4,4]":
        """
        :param ctx: context, to store some tensors
        for backward

mukheshpugal

import numpy as np
import pptk
from mayavi import mlab
import cv2

depthmap  = np.array(cv2.imread('depth.png',0));
depthmap = depthmap[300:340, 220:260]
intrinsics = np.array([[525.0, 0, 319.5],
                       [0, 525.0, 239.5],
                       [0, 0, 1]])

mukheshpugal

def hat(w):
     return torch.tensor([[0, -w[2], w[1]],
                    [w[2], 0, -w[0]],
                    [-w[1], w[0], 0]])


def hat_inv(R):
     return torch.tensor([R[2][1], R[0][2], R[1][0]])

mukheshpugal

def matrix_from_euler(euler):
   rotation_x = torch.tensor([[1, 0, 0], [0, torch.cos(euler[0]), -torch.sin(euler[0])], [0, torch.sin(euler[0]), torch.cos(euler[0])]])
   rotation_y = torch.tensor([[torch.cos(euler[1]), 0, torch.sin(euler[1])], [0, 1, 0], [-torch.sin(euler[1]), 0, torch.cos(euler[1])]])
   rotation_z = torch.tensor([[torch.cos(euler[2]), -torch.sin(euler[2]), 0], [torch.sin(euler[2]), torch.cos(euler[2]), 0], [0, 0, 1]])
  
   rotation_3d = torch.matmul(torch.matmul(rotation_x, rotation_y), rotation_z)
     
   return rotation_3d

mukheshpugal

from scipy.optimize import least_squares as ls
import math
import numpy as np
from PIL import Image
import cv2
from matplotlib import pyplot as plt

def huber(r, delta):
     if math.sqrt(r) < delta:
          return r / (2 * delta)