vadimkantorov

import base64
import random
import cv2
import torch
import torchvision

def svg(points, labels, thumbnails, legend_size = 1e-1, legend_font_size = 5e-2, circle_radius = 5e-3):
	points = (points - points.min(0)[0]) / (points.max(0)[0] - points.min(0)[0])
	class_index = sorted(set(labels))
	class_colors = [360.0 * i / len(class_index) for i in range(len(class_index))]

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

jensenb

import cv2
import numpy as np


def in_front_of_both_cameras(first_points, second_points, rot, trans):
    # check if the point correspondences are in front of both images
    rot_inv = rot
    for first, second in zip(first_points, second_points):
        first_z = np.dot(rot[0, :] - second[0]*rot[2, :], trans) / np.dot(rot[0, :] - second[0]*rot[2, :], second)
        first_3d_point = np.array([first[0] * first_z, second[0] * first_z, first_z])

hrldcpr

One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!