TheSalarKhan

# This script does a perspective transformation with an A4 paper
# Get an A4 sheet, put it on a table in front of your webcam 
# just close enough so that all its corners are visible.
# Run this script, you will be given a frame from the webcam.
# Pick the corners of the A4 sheet for the software, by double clicking on its corners
# one by one in the following order: top left, top right, bottom left, bottom right.
# Just as you select the last corner, a live feed without the perspective distortion
# shows up and now you can write something on it, and have a view of it as if
# the camera was right on top of it. Cheers!
# visit: 

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

keithweaver

'''
Using OpenCV takes a mp4 video and produces a number of images.

Requirements
----
You require OpenCV 3.2 to be installed.

Run
----
Open the main.py and edit the path to the video. Then run:

QCaudron

CUDA installation on Ubuntu

Made based on CUDA 9 installation

Download CUDA https://developer.nvidia.com/cuda-downloads Run commands:

sudo dpkg -i cuda-repo-<version>.deb
sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
sudo apt-get update

kittinan

import cv2
import io
import socket
import struct
import time
import pickle
import zlib

client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect(('192.168.1.124', 8485))

nfsrules

# Create video writer
writer = cv2.VideoWriter("sample_output.avi", 
                         cv2.VideoWriter_fourcc(*"MJPG"), 
                         30,(1800,480))

for frame in tqdm(merged_maps):
    writer.write(frame.astype('uint8'))
    
writer.release()

ccj5351

%Official Matlab version can be found in the PASCAL VOC devkit 
%http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit

% VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different
% colors.  Useful for reading and writing index images which contain large indices,
% by encoding them as RGB images.
%
% CMAP = VOCLABELCOLORMAP(N) creates a label color map with N entries.
function cmap = labelcolormap(N)

Amir22010

Convert PascalVOC Annotations to YOLO

This script reads PascalVOC xml files, and converts them to YOLO txt files.

Note: This script was written and tested on Ubuntu. YMMV on other OS's.

Disclaimer: This code is a modified version of Joseph Redmon's voc_label.py

Instructions:

1. Place the convert_voc_to_yolo.py file into your data folder.

luuil

import cv2
import os
import numpy as np
from PIL import Image, ImageFont, ImageDraw
from typing import Tuple, Optional, Union, List
from dataclasses import dataclass, InitVar
import sys
from pathlib import Path
from enum import Enum
import logging