Use k-means clustering algorithm to identify dominant image tones

Because it is too troublesome to use php to write the image's primary color recognition function, I would like to introduce how to use the k-means clustering algorithm to identify the Dominant Color of the image, which is 100 times better than php.

There are several methods on the Internet to identify dominant image tones. However, the most accurate and elegant solution is to use clustering algorithms...

Directly go to the code .... However, my test results show that PHP is not efficient, and PHP is not suitable for such large-scale operations ~~~, The efficiency of using nodejs is about 100 times higher...

The Code is as follows:

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<? 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 ('wipe, What image? It cannot be parsed '); }   $ 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 '<br> <span style = "background-color :'. RGBToHex ($ value [0]). '"> '. RGBToHex ($ value [0]). '</span> '; }   }   Function run_time () { Global $ start; Echo '<br/> 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 I <256; $ I ++) { $ Cache [$ I] = $ I * $ I; }   # Random k value generation $ 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;   # Finding 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) { $ Smallest_distance = $ distance; $ Index = $ I; } } $ Point_list [$ index] [] = $ value; }   $ Diff = 0; #1 iteration K value $ I = $ k; While ($ I -) { $ Old = $ clusters [$ I];   # Move to queue Center $ Center = calculateCenter ($ point_list [$ I], 3 ); # Forming a new K value set queue $ New_cluster = array ($ center, $ point_list [$ I]); $ Clusters [$ I] = $ new_cluster;   # Calculate the new K value and the position of the queue point $ Diff = euclidean ($ old [0], $ center ); }   # Determine whether aggregation is sufficient If ($ diff <$ min_diff) { Break; > }   } Echo '->'. $ ii;   Return $ clusters; }   # Calculate the 2-point distance $ 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 vertices 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; } ?>