[導(dǎo)讀] 由于使用php來寫圖片主色調(diào)識別功能太麻煩了，所以我給大家介紹利用利用k-means聚類算法識別圖片主色調(diào)方法，比php要己100倍哦。識別圖片主色調(diào)這個，網(wǎng)上貌似有幾種方法，不過，最準(zhǔn)確，最優(yōu)雅的解決方案還是利由于使用php來寫圖片主色調(diào)識別功能太麻煩了，所以我給大家介紹利用利用k-means聚類算法識別圖片主色調(diào)方法，比php要己100倍哦。
識別圖片主色調(diào)這個，網(wǎng)上貌似有幾種方法，不過，最準(zhǔn)確，最優(yōu)雅的解決方案還是利用聚類算法來做。。。
直接上代碼。。。。不過，我測試結(jié)果表示，用PHP來做，效率不佳，PHP不適合做這種大規(guī)模運(yùn)算~~~，用nodejs做 效率可以高出100倍左右。。。
代碼如下 復(fù)制代碼
<?php
$start = microtime(TRUE);
main();
function main($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(‘擦，什么圖像？解析不了啊’);
}
$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(http://www.sm136.com);
$i = $new_width;
while ($i–)
{
# reset高度
$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 => $value)
&nb
sp; {
echo ‘<br><spanstyle=“background-color:’ . RGBToHex($value[0]) . ‘” >’ .RGBToHex($value[0]) . ‘</span>’;
}
}
function run_time()
{
global $start;
echo ‘<br/>消耗:’,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 < 256; $i++)
{
$cache[$i] = $i*$i;
}
# 隨機(jī)生成k值
$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;
# 求出距離最小的點(diǎn)
# index用于保存point最靠近的k值
$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)
{
$smallest_distance = $distance;
$index = $i;
}
}
$point_list[$index][] = $value;
}
$diff = 0;
# 1個1個迭代k值
$i = $k;
while ($i–)
{
$old = $clusters[$i];
# 移到到隊列中心
$center = calculateCenter($point_list[$i],3);
# 形成新的k值集合隊列
$new_cluster = array($center,$point_list[$i]);
$clusters[$i] = $new_cluster;
# 計算新的k值與隊列所在點(diǎn)的位置
$diff = euclidean($old[0], $center);
}
# 判斷是否已足夠聚合
if ($diff < $min_diff)
{
break;
>
}
}
echo ‘—>’.$ii;
return $clusters;
}
# 計算2點(diǎn)距離
$ii = 0;
function euclidean($p1, $p2)
{
$s = 0;
foreach ($p1 as $key => $value)
{
$temp = ($value – $p2[$key]);
$s += $temp*$temp;
}
return sqrt($s);
}
# 移動k值到所有點(diǎn)的中心
function calculateCenter($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;
}
?>
