Margus Roo –

It took me some time to understand Solidity events. But in the end I think I can explain it.

So the example contract:

contract CallCalc {
address calcReg =0xd794EaF737549c2698e2232c07A9b666934584f8;
event TestEvent(address indexed _from , uint);

function call(uint256 input) returns (uint) {
TestEvent(msg.sender, input);
if (!calcReg.call(bytes4(sha3(“add(uint256)”)), input))
throw;
}
}

After executing method call I can see event in my client

 

Create a simple contract:

contract Storage {
    uint pos0;
    mapping(address => uint) pos1;
    
    function Storage() {
        pos0 = 1234;
        pos1[msg.sender] = 5678;
    }
}

submit it and get back address. In example 0x9a5CdfCb1132dcbEca55b213372224D9bd0209c2

Now lets execute it under one account. In example 0xa2213890a81042692B4716025D6e98349b432349

Lets see how we can get what is in storage related with contract.

We can use JSON_RPC eth_getStorageAt or web3.eth.getStorageAt from geth command line. In this example I’ll use JSON_RPC eth_getStorageAt.

Contracts storage is basically key-value storage.

To get pos0 is simple:

margusja@IRack:~$ curl -X POST --data '{"jsonrpc":"2.0", "method": "eth_getStorageAt", "params": ["0x9a5CdfCb1132dcbEca55b213372224D9bd0209c2", "0x0", "latest"], "id": 1}' localhost:8545
{"jsonrpc":"2.0","id":1,"result":"0x00000000000000000000000000000000000000000000000000000000000004d2"}


hex value 4d2 to decimal is 1234.

TO get pos1 is more tricky. First we have to calculate index position using contract executor address and index. Go to geth commandline and:

var key = “000000000000000000000000a2213890a81042692B4716025D6e98349b432349″ + “0000000000000000000000000000000000000000000000000000000000000001”

We added zeros to get 64 bit value. Next in geth command line:

web3.sha3(key, {“encoding”: “hex”}) – it returns aadress: 0x790e4fae970c427bd6d93e3f64ba898c69fdead01d68e500efb6f3abc672d632

Now we can get value from storage:

margusja@IRack:~$ curl -X POST --data '{"jsonrpc":"2.0", "method": "eth_getStorageAt", "params": ["0x9a5CdfCb1132dcbEca55b213372224D9bd0209c2", "0x790e4fae970c427bd6d93e3f64ba898c69fdead01d68e500efb6f3abc672d632", "latest"], "id": 1}' localhost:8545
{"jsonrpc":"2.0","id":1,"result":"0x000000000000000000000000000000000000000000000000000000000000162e"}

hex value 162e to decimal is 5678

Source – https://github.com/ethereum/wiki/wiki/JSON-RPC#web3_clientversion

Fun with ethereum. Build a private network and collecting huge amount ether 🙂

Pilt oma olemuselt on lihtsalt kogum teatud formaadis numbreid.

Näiteks allolev pilt (600×338) koosneb 202800 täpist (pixel), kus ühe elemendi väärtus on vahemikus 0…255 (gray scale)

 

 

 

 

 

 

 

 

 

 

 

Arvutis asub pildi info kujul [row1;row2;row3…row600] kus row koosneb 338’t elemendist.

Image =

[143, 138, 139, 139, 143, 140, 142, 142, 143, 141, 141, 143, 145, 145, 144, 143, 143, 149, 150, 147, 147, 150, 151, 151, 151, 151, 151, 152, 154, 154, 152, 149, 153, 151, 152, 154, 155, 154, 153, 154, 159, 158, 157, 157, 156, 156, 156, 156, 156, 157, 157, 154, 153, 154, 157, 159, 158, 155, 156, 157, 157, 157, 158, 158, 155, 157, 159, 159, 157, 156, 157, 160, 163, 159, 160, 162, 159, 159, 161, 159, 161, 163, 163, 164, 165, 166, 166, 165, 165, 167, 168, 167, 165, 163, 163, 164, 164, 162, 161, 161, 162, 163, 163, 162, 161, 164, 164, 163, 165, 170, 169, 166, 168, 168, 166, 167, 167, 166, 168, 166, 166, 163, 162, 165, 167, 168, 167, 167, 166, 167, 168, 168, 166, 166, 168, 170, 167, 166, 167, 148,  91,  57,  56, 143, 168, 169, 161,  78,  17,  42,  34,  35,  30,  24,  21,  22,  24,  23,  22,  23,  21,  28,  29,  27,  26,  27,  30,  28,  24,  27,  28,  26,  27,  29,  28,  25,  29,  27,  27,  27,  26,  25,  26,  25,  27,  20,  19,  23,  20,  23,  24,  28,  27,  31,  34,  34,  35,  34,  32,  31,  32,  27,  27,  29,  31,  30,  28,  25,  21,  23,  22,  27,  23,  21,  21,  23,  25,  27,  27,  23,  20,  21,  23,  23,  23,  27,  20,  22,  23,  18,  23,  24,  27,  16,  30,  40,  33,  38,  10,  61, 154, 122, 137, 145, 146, 130, 130, 133, 130, 125,  94,  86,  99, 108,  96,  98,  95, 105, 100,  82,  66,  62,  61,  61,  73,  79,  72,  66,  73,  77,  68,  57,  44,  47,  70,  87,  77,  59,  55,  63,  57,  55,  58,  46,  52,  57,  56,  57,  64,  62,  62,  82, 113, 117, 119, 127, 116, 114, 113, 111, 105,  49,  34,  50, 136, 156, 156, 163, 164, 160, 158, 153, 158, 164, 166, 163, 161, 162, 160, 158, 153, 150, 146, 139, 138, 133, 119, 114,  75,  17,  33,  30,  63,  67,  69,  72,  72,  73,  67,  65,  59, 144, 159, 156, 156, 156, 159, 147, 125….

52,  54,  57,  57,  55,  60,  86,  90,  98, 111, 115, 112, 100, 103, 106, 119, 141, 158, 159, 158, 157, 158, 159, 161, 164, 172, 178, 180, 177, 176, 181, 185, 184, 183, 177, 160, 140, 135, 134, 135, 145, 151, 149, 147, 142, 143, 144, 160, 179, 183, 173, 178, 186, 186, 187, 188, 189, 183, 178, 181, 182, 180, 181, 179, 176, 174, 172, 170, 171, 171, 170, 172, 169, 174, 173, 179, 181, 182, 187, 182, 174, 169, 166, 162, 161, 164, 166, 169, 172, 174, 176, 179, 180, 168, 157, 160, 165, 176, 174, 106,   0,  22,  19,  18,  20,  11,   4,   5,   6,   4,   3,   2,   2]

Kuna meil on võimalik iga elemendiga omi tehteid teha, siis teeme näiteks lihtsa tehte, kus me muudame kõik elemendid väärtusega 183 väärtuseks 255 (valge):

 

#include
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

int main(int argc, const char * argv[]) {

Mat image;

image = imread( “image.jpg”, 0 );

int channels = image.channels();
int cols = image.cols;
int rows = image.rows;

cout << “Image = ” << endl << ” ” << image << endl << endl;

cout << “Channels = ” << endl << ” ” << channels << endl << endl;

cout << “Rows = ” << endl << ” ” << rows << endl << endl;
cout << “Cols = ” << endl << ” ” << cols << endl << endl;
cout << “Size = ” << endl << ” ” << image.total() << endl << endl;

for(int i = 0; i < image.rows; i++){
for(int j=0; j < image.cols; j++){
if (image.at(i,j) == 183) {
image.at(i,j) = 255;
}
}
}

// visualize image
namedWindow( “demo”, WINDOW_AUTOSIZE );
imshow( “Demo image”, image );
waitKey(0);

return 0;
}

 

Tulemuseks saame uue pildi:

 

Object tracking is much more faster than object detecting.
Source from – https://www.learnopencv.com/object-tracking-using-opencv-cpp-python/

 

FOUND 7831 keypoints on first image
FOUND 2606 keypoints on second image
SURF run time: 1780.93 ms
Max distance: 0.500609
Min distance: 0.0160885
Calculating homography using 50 point pairs.

 

Source code – https://github.com/opencv/opencv_contrib/blob/master/modules/xfeatures2d/samples/surf_matcher.cpp