okld

import streamlit as st
from persist import persist, load_widget_state


def main():
    if "page" not in st.session_state:
        # Initialize session state.
        st.session_state.update({
            # Default page.
            "page": "home",

raulqf

Install OpenCV 4.10 with CUDA 12.6 and CUDNN 8.9 in Ubuntu 24.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

psa-jforestier

###
### This gist contains 2 files : settings.json and lambda_function.py
###

### settings.json
{
    "extensions" : ["*.hdr", "*.glb", "*.wasm"]
}

### lambda_function.py

GuruMulay

import av
import PIL
import skimage.io
from skimage.transform import resize, pyramid_reduce

import numpy as np
import os
import argparse

"""

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

ZijiaLewisLu

In most of deep learning projects, the training scripts always start with lines to load in data, which can easily take a handful minutes. Only after data ready can start testing my buggy code. It is so frustratingly often that I wait for ten minutes just to find I made a stupid typo, then I have to restart and wait for another ten minutes hoping no other typos are made.

In order to make my life easy, I devote lots of effort to reduce the overhead of I/O loading. Here I list some useful tricks I found and hope they also save you some time.

1. use Numpy Memmap to load array and say goodbye to HDF5.

   I used to relay on HDF5 to read/write data, especially when loading only sub-part of all data. Yet that was before I realized how fast and charming Numpy Memmapfile is. In short, Memmapfile does not load in the whole array at open, and only later "lazily" load in the parts that are required for real operations.

Sometimes I may want to copy the full array to memory at once, as it makes later operations

mkocabas

mkdir coco
cd coco
mkdir images
cd images

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/test2017.zip
wget http://images.cocodataset.org/zips/unlabeled2017.zip

loretoparisi

# Output a single frame from the video into an image file:
ffmpeg -i input.mov -ss 00:00:14.435 -vframes 1 out.png

# Output one image every second, named out1.png, out2.png, out3.png, etc.
# The %01d dictates that the ordinal number of each output image will be formatted using 1 digits.
ffmpeg -i input.mov -vf fps=1 out%d.png

# Output one image every minute, named out001.jpg, out002.jpg, out003.jpg, etc. 
# The %02d dictates that the ordinal number of each output image will be formatted using 2 digits.
ffmpeg -i input.mov -vf fps=1/60 out%02d.jpg

hadware

from scipy.io.wavfile import read, write
import io

## This may look a bit intricate/useless, considering the fact that scipy's read() and write() function already return a
## numpy ndarray, but the BytesIO "hack" may be useful in case you get the wav not through a file, but trough some websocket or
## HTTP Post request. This should obviously work with any other sound format, as long as you have the proper decoding function

with open("input_wav.wav", "rb") as wavfile:
    input_wav = wavfile.read()

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',