A couple of weeks ago I played (and finished) A Plague Tale, a game by Asobo Studio. I was really captivated by the game, not only by the beautiful graphics but also by the story and the locations in the game. I decided to investigate a bit about the game tech and I was surprised to see it was developed with a custom engine by a relatively small studio. I know there are some companies using custom engines but it's very difficult to find a detailed market study with that kind of information curated and updated. So this article.
Nowadays lots of companies choose engines like Unreal or Unity for their games (or that's what lot of people think) because d
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/* global chrome, MediaRecorder, FileReader */ | |
chrome.runtime.onConnect.addListener(port => { | |
let recorder = null | |
port.onMessage.addListener(msg => { | |
console.log(msg); | |
switch (msg.type) { | |
case 'REC_STOP': | |
console.log('Stopping recording') | |
if (!port.recorderPlaying || !recorder) { |
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/* | |
Create this as a file ~/Library/KeyBindings/DefaultKeyBinding.dict | |
Then restart your computer. | |
NOTE: ~ means alt/option | |
^ means ctrl | |
*/ | |
{ | |
"~f"="moveWordForward:"; | |
"~b"="moveWordBackward:"; | |
"~<"="moveToBeginningOfDocument:"; |
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WEBGL // p5 WEBGL rendering mode. | |
createCanvas(w, h, renderer) // Creates a 3D canvas (if renderer is WEBGL). | |
// Primitives | |
plane(width, height) // Creates a plane in 3D space. Equivalent to rect() in the default rendering mode. | |
plane(width, height, detailX, detailY) // Creates a plane in 3D space with the number of triangle subdivisions specified. | |
box(width) // Creates a cube in 3D space. | |
box(width, height, depth) // Creates a cuboid in 3D space. | |
box(width, height, depth, detailX, detailY) // Creates a cuboid in 3D space with triangle subdivisions. | |
sphere(radius) // Creates a sphere in 3D space. |
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""" | |
DQN in PyTorch | |
""" | |
import argparse | |
import torch | |
import torch.nn | |
import numpy as np | |
import random | |
import gym |
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import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.nn.parameter import Parameter | |
def debug(debug_open, x, layername): | |
if debug_open: | |
print x.size(), 'after', layername | |
class PVANet(nn.Module): |
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import argparse | |
from collections import Counter | |
import csv | |
import os | |
import torch | |
from torch.autograd import Variable | |
import torch.nn as nn | |
import torch.optim as optim | |
import torch.utils.data as data | |
import tarfile |
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import torch.utils.data | |
from torchvision import datasets, transforms | |
class PartialDataset(torch.utils.data.Dataset): | |
def __init__(self, parent_ds, offset, length): | |
self.parent_ds = parent_ds | |
self.offset = offset | |
self.length = length | |
assert len(parent_ds)>=offset+length, Exception("Parent Dataset not long enough") | |
super(PartialDataset, self).__init__() |
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from batcountry import BatCountry | |
import numpy as np | |
from PIL import Image | |
from glob import glob | |
import os | |
import random | |
CAFFE_ROOT = '../caffe' | |
INPUT_PATH = 'input.jpg' |
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