insaneyilin

import argparse

from PIL import Image

def embed_message(image_path, message, output_path):
    org_img = Image.open(image_path)
    org_pixelMap = org_img.load()

    enc_img = Image.new(org_img.mode, org_img.size)
    enc_pixelsMap = enc_img.load()

insaneyilin

#!/bin/bash

# Check number of arguments
if [ $# -ne 3 ]; then
    echo "Usage: $0 <file1> <file2> <output_file>"
    echo "Example: $0 file1.txt file2.txt differences.txt"
    exit 1
fi

# Get command line arguments

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ffmpeg -ss 0 -t 3 -i input.mp4 \
    -vf "fps=10,scale=320:-1:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse" \
    -loop 0 output.gif

insaneyilin

# 保存到 ~/.gdbinit
python
import sys 
sys.path.insert(0, '/usr/share/gcc-4.8.2/python') # 这个路径以实际情况为准
from libstdcxx.v6.printers import register_libstdcxx_printers
register_libstdcxx_printers (None)
end

#                                                                                                        
#   STL GDB evaluators/views/utilities - 1.03

insaneyilin

def tile_along_axis(x, dim, n_tile):
    init_dim = x.size(dim)
    repeat_idx = [1] * x.dim()
    repeat_idx[dim] = n_tile
    x = x.repeat(*(repeat_idx))
    order_index = torch.tensor(
        torch.cat([init_dim * torch.arange(n_tile, device=x.device) + i for i in range(init_dim)]),
        dtype=torch.long, device=x.device)
    return torch.index_select(x, dim, order_index)

insaneyilin

#!/bin/bash
while true
do
    x=`xdotool getmouselocation | grep -oP '(?<=x:)\d+'`
    y=`xdotool getmouselocation | grep -oP '(?<=y:)\d+'`
    xdotool mousemove $x $y
    xdotool click 1
    sleep 1
done

insaneyilin

vector <string> Names {"Karl", "Martin", "Paul", "Jennie"};
vector <int> Score{45, 5, 14, 24};

std::vector<int> indices(Names.size());
 std::iota(indices.begin(), indices.end(), 0);
 std::sort(indices.begin(), indices.end(),
           [&](int A, int B) -> bool {
                return Score[A] < Score[B];
            });

insaneyilin

import numpy as np
from scipy.spatial import KDTree


def chamfer_distance(s1, s2, direction='s2_to_s1'):
    """Chamfer distance between two point sets.

    Args:
        s1 (np.ndarray): [n_points_s1, n_dims]
        s2 (np.ndarray): [n_points_s2, n_dims]

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def masked_log_softmax(input, mask, dim=1):
    masked_input = input * mask.float()
    max_input = torch.max(masked_input, dim=dim, keepdim=True)[0]
    exps = torch.exp(masked_input - max_input)
    masked_exps = exps * mask.float()
    masked_sums = masked_exps.sum(dim, keepdim=True)
    zeros = (masked_sums == 0)
    masked_sums += zeros.float()
    masked_exps += 1e-6  # avoid zero input of log.
    return torch.log(masked_exps / masked_sums)

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# reference: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

import copy
import numpy as np
from scipy.linalg import block_diag
from filterpy.kalman import IMMEstimator
from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise

def make_cv_filter(dt):