Use k-means clustering algorithm to identify the main color of the picture_PHP tutorial

Since it is too troublesome to use PHP to write the main color identification function of pictures, I will introduce to you the method of using k-means clustering algorithm to identify the main colors of pictures, which is 100 times faster than PHP.

There seem to be several ways to identify the main color of an image online, but the most accurate and elegant solution is to use clustering algorithms. . .

Upload the code directly. . . . However, my test results show that using PHP to do it is not efficient. PHP is not suitable for such large-scale operations~~~. Using nodejs can be about 100 times more efficient. . .

The code is as follows

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$start = microtime(TRUE); main(); function main($img = 'colors_files/T1OX3eXldXXXcqfYM._111424.jpg') { list($width, $height, $mime_code) = getimagesize($img); $im = null; $point = array(); switch ($mime_code) { # jpg case 2 : $im =imagecreatefromjpeg($img); break; # png case 3: default: exit('What image? Can't parse it'); } $new_width = 100; $new_height = 100; $pixel = imagecreatetruecolor($new_width, $new_height); imagecopyresampled($pixel, $im, 0, 0, 0, 0, $new_width, $new_height, $width, $height); run_time(); $i = $new_width; while ($i–) { # reset height $k = $new_height; while ($k–) { $rgb = ImageColorAt($im, $i, $k); array_push($point, array('r'= >($rgb >> 16) & 0xFF, 'g'=>($rgb >> 8) & 0xFF, 'b'=>$rgb & 0xFF)); } } imagedestroy($im); imagedestroy($pixel); run_time(); $color = kmeans($point); run_time(); foreach ( $color as $key => $value) &nb sp; { echo ' ' . RGBToHex($value[0]) . ''; } } function run_time() { global $start; echo ' Consumption:' , microtime(TRUE) – $start; } function kmeans($point=array(), $k=3, $min_diff=1) { global $ii; $point_len = count($point); $clusters = array(); $ cache = array(); for ($i=0; $i { $cache[$i] = $i*$i; } # Randomly generate k value $i = $ k; $index = 0; while ($i–) { $index = mt_rand(1,$point_len-100) ; array_push($clusters, array($point[$index], array($point[$index]))); } run_time(); $point_list = array(); $run_num = 0; while (TRUE) { foreach ($point as $value) { $smallest_distance = 10000000; # Find the point with the smallest distance # index is used to save the k value closest to the point $index = 0; $i = $k; while ($i–) { $distance = 0; foreach ($value as $key = > $p1) { &n bsp; if ($p1 > $clusters[$i][0][$key]) { $distance += $cache[$p1 - $clusters[$i][0][$key]]; } else { $distance += $cache[$clusters[$i][0][$key] – $p1]; } } $ii++; if ($distance { $smallest_distance = $distance; $index = $i; } } $point_list[$index][] = $value; } $diff = 0; # 1 1 iteration k value $i = $k; while ($i–) { $old = $clusters[$i]; # Move to the center of the queue $center = calculateCenter($point_list[$i], 3); # Form a new k value set queue $new_cluster = array($center, $point_list[$i]); $clusters[$i] = $new_cluster; # Calculate new The k value and the position of the queue point $diff = euclidean($old[0], $center); } # Determine whether sufficient aggregation is achieved if ($diff { break; > } } echo '—>'.$ii; return $clusters; } # Calculate the distance between 2 points $ii = 0; function euclidean($p1, $p2) { $s = 0; foreach ($p1 as $key => $value) { $temp = ($value – $p2[$key]); $s += $temp*$temp; } return sqrt($s); } # Move the k value to the center of all points function calculateCenter($point_list, $attr_num) { $vals = array(); $point_num = 0; $keys = array_keys($point_list[0]); foreach($keys as $value) { $vals[$value] = 0; } foreach ($point_list as $arr) { $point_num++; foreach ($arr as $ key => $value) { $vals[$key] += $value; } } foreach ($keys as $index) { $vals[$index] = $vals[$index] / $point_num ; } return $vals; } function RGBToHex($r, $g=", $b=") { if (is_array($r)) { $b = $r['b']; $g = $r['g']; $r = $r['r ']; } $hex = “#”; $hex.= str_pad(dechex($r), 2, ' 0′, STR_PAD_LEFT); $hex.= str_pad(dechex($g), 2, '0′, STR_PAD_LEFT); $hex.= str_pad(dechex($b) , 2, '0′, STR_PAD_LEFT); return $hex; } ?> >

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www.bkjia.comtruehttp: //www.bkjia.com/PHPjc/444594.htmlTechArticleSince it is too troublesome to use php to write the main color recognition function of pictures, I will introduce to you how to use k-means The clustering algorithm to identify the main color of the picture is 100 times faster than PHP. Recognize pictures...