CMake를 왜 쓰는거죠?
좋은 툴은 Visual Studio 뿐입니다. 그 이외에는 전부 사도(邪道)입니다 사도! - 작성자
- 이 문서는 CMake를 주관적으로 서술합니다
- 이 문서를 통해 CMake를 시작하기엔 적합하지 않습니다
https://cgold.readthedocs.io/en/latest/ 3.1 챕터까지 따라해본 이후 기본사항들을 속성으로 익히는 것을 돕기위한 보조자료로써 작성되었습니다
""" This simulates converting 24-bit RGB values to YUV, then back to 24-bit RGB. | |
Using BT.709 transfer functions: | |
https://en.wikipedia.org/wiki/Rec._709 | |
It demonstrates that converting to 30-bit YUV then back to 24-bit RGB is lossy. | |
Using 10 bits per YUV value appears to be lossless. | |
Converting RGB (24-bit) -> YUV (64-bit floats per channel, normalized [0-1]) -> RGB (24-bit) | |
Found 0 inaccurate conversions out of 16581375 RGB values |
import numpy as np | |
import cv2 | |
import sys | |
cap = cv2.VideoCapture(0) | |
face_cascade = cv2.CascadeClassifier('<PATH_TO_CASCADES_FOLDER>/haarcascade_frontalface_default.xml') | |
while(True): | |
# Capture frame-by-frame |
import neuralnet_pytorch as nnt | |
import torch as T | |
from torch_scatter import scatter_add | |
def pointcloud2voxel_fast(pc: T.Tensor, voxel_size: int, grid_size=1., filter_outlier=True): | |
b, n, _ = pc.shape | |
half_size = grid_size / 2. | |
valid = (pc >= -half_size) & (pc <= half_size) | |
valid = T.all(valid, 2) |
sudo rm /var/lib/ubuntu-release-upgrader/release-upgrade-available | |
sudo /usr/lib/ubuntu-release-upgrader/release-upgrade-motd |
CMake를 왜 쓰는거죠?
좋은 툴은 Visual Studio 뿐입니다. 그 이외에는 전부 사도(邪道)입니다 사도! - 작성자
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
'''Generates 3D Histogram of Wallaby image and renders to screen using vispy | |
Requires: | |
vispy | |
scipy | |
numpy |
#!/usr/bin/env python3 | |
from __future__ import print_function | |
from tempfile import TemporaryFile | |
from binascii import hexlify | |
from ctypes import * | |
class StructHelper(object): | |
def __get_value_str(self, name, fmt='{}'): | |
val = getattr(self, name) |
import tensorflow as tf | |
#aplly exponential decay on learning rate | |
global_step = tf.Variable(0, trainable=False) | |
stater_learning_rate = lr #for start | |
learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, | |
decay_steps, decay_rate, staircase=True) | |
optimizer = tf.train.AdamOptimizer(learning_rate) |
This gist demonstrates how to setup a python project that process a numpy array from C language.
To compile the project, run
make all
To test it, run
make test