Trying to convert the following PHP function to Python but getting the following error. What would be the working Python equivalent to the below PHP function?
line 140, in doDetectBigToSmall
for scale in xrange(start_scale, scale > 1,scale = scale* scale_update):
UnboundLocalError: local variable 'scale' referenced before assignment
PHP CODE:
protected function doDetectBigToSmall($ii, $ii2, $width, $height)
{
$s_w = $width/20.0;
$s_h = $height/20.0;
$start_scale = $s_h < $s_w ? $s_h : $s_w;
$scale_update = 1 / 1.2;
for ($scale = $start_scale; $scale > 1; $scale *= $scale_update) {
$w = (20*$scale) >> 0;
$endx = $width - $w - 1;
$endy = $height - $w - 1;
$step = max($scale, 2) >> 0;
$inv_area = 1 / ($w*$w);
for ($y = 0; $y < $endy; $y += $step) {
for ($x = 0; $x < $endx; $x += $step) {
$passed = $this->detectOnSubImage($x, $y, $scale, $ii, $ii2, $w, $width+1, $inv_area);
if ($passed) {
return array('x'=>$x, 'y'=>$y, 'w'=>$w);
}
} // end x
} // end y
} // end scale
return null;
}
PYTHON CODE:
def doDetectBigToSmall(self,ii, ii2, width, height):
s_w = width/20.0
s_h = height/20.0
start_scale = s_h if s_h < s_w else s_w
scale_update = 1 / 1.2
for scale in xrange(start_scale, scale > 1,scale = scale* scale_update):
w = (20*scale) >> 0
endx = width - w - 1
endy = height - w - 1
step = max(scale, 2) >> 0
inv_area = 1 / (w*w)
for y in xrange(0,y < endy,y = y + step):
for x in xrange(0, x < endx, x= x + step):
passed = self.detectOnSubImage(x, y, scale, ii, ii2, w, width+1, inv_area)
if (passed):
return {'x': x, 'y': y, 'w': w}
You have no idea what xrange() does ;-) So read the docs before you try it again. In the meantime, replace:
for scale in xrange(start_scale, scale > 1,scale = scale* scale_update):
with
scale = start_scale
while scale > 1:
and, at the end of the loop, add:
scale *= scale_update
All your other uses of xrange() are similarly broken, but you have to make some effort to learn what it does.
This works for me:
def strpos_r(haystack, needle):
positions = []
position = haystack.rfind(needle)
while position != -1:
positions.append(position)
haystack = haystack[:position]
position = haystack.rfind(needle)
return positions
Also, functions should not really handle input errors for you. You usually just return False or let the function throw an execution error.
This is happening because xrange is a function and you are passing an uninitialized value to it. Scale will not be initialized until after xrange is run and returns a value. Python generally used for loops to iterate over lists. I recommend rewriting your code using while loops.
I have two images(small and big). Big one contains a small one. Like if the small one is a photo and a big one is a page from the photo album.
How do I get coordinates of that small image in the big one using PHP? And also I need to know the size of that image in big one...so just a(x,y) coordinate of any angle and sizes of sides of that presentation of the small image...
(x,y, width, height)
I've already asked the question like that and got a brilliant answer (here) but I've forgot to mention over there that the size of a small image could be different from the the size of that image in the big image...
And also if it is possible to deal with a presentation of that small image in the big image can have something covering one of its angles... Like in this example:
Small image:
Big image:
Small image always has just a rectangular shape.
Alright, this answer does not perfectly answer the question, but it should give you a good start! I know I repeat myself in the code, but my goal was simply to get something working so you can build on it, this isn't production code!
Preconditions
Starting with the large picture:
We need to find as best as possible the position of this other picture:
I decided to break the process into many substeps, which you could improve or remove depending on what you want the code to do.
For testing purposes, I did test my algorithm on different input images so you'll see a variable defining what file to load...
We start with:
function microtime_float()
{
list($usec, $sec) = explode(" ", microtime());
return ((float)$usec + (float)$sec);
}
$time_start = microtime_float();
$largeFilename = "large.jpg";
$small = imagecreatefromjpeg("small.jpg");
$large = imagecreatefromjpeg($largeFilename);
and
imagedestroy($small);
imagedestroy($large);
$time_end = microtime_float();
echo "in " . ($time_end - $time_start) . " seconds\n";
To have a good idea on our performances. Luckily, most of the algorithm was pretty fast so I didn't have to optimize more.
Background Detection
I started by detecting the background color. I assumed that the background color would be the color most present in the picture. To do this, I only counted how many references of each color I could find in the large picture, sort it with decending values and took the first one as the background color (should allow the code to be adaptable if you changed the source pictures)
function FindBackgroundColor($image)
{
// assume that the color that's present the most is the background color
$colorRefcount = array();
$width = imagesx($image);
$height = imagesy($image);
for($x = 0; $x < $width; ++$x)
{
for($y = 0; $y < $height; ++$y)
{
$color = imagecolorat($image, $x, $y);
if(isset($colorRefcount[$color]))
$colorRefcount[$color] = $colorRefcount[$color] + 1;
else
$colorRefcount[$color] = 1;
}
}
arsort($colorRefcount);
reset($colorRefcount);
return key($colorRefcount);
}
$background = FindBackgroundColor($large); // Should be white
Partitionning
My first step was to try to find all the regions where non background pixels were. With a little padding, I was able to group regions into bigger regions (so that a paragraph would be a single region instead of multiple individual letters). I started with a padding of 5 and got good enough results so I stuck with it.
This is broken into multiple function calls, so here we go:
function FindRegions($image, $backgroundColor, $padding)
{
// Find all regions within image where colors are != backgroundColor, including a padding so that adjacent regions are merged together
$width = imagesx($image);
$height = imagesy($image);
$regions = array();
for($x = 0; $x < $width; ++$x)
{
for($y = 0; $y < $height; ++$y)
{
$color = imagecolorat($image, $x, $y);
if($color == $backgroundColor)
{
continue;
}
if(IsInsideRegions($regions, $x, $y))
{
continue;
}
$region = ExpandRegionFrom($image, $x, $y, $backgroundColor, $padding);
array_push($regions, $region);
}
}
return $regions;
}
$regions = FindRegions($large, $background, 5);
Here, we iterate on every pixel of the picture, if its background color, we discard it, otherwise, we check if its position is already present in a region we found, if that's the case, we skip it too. Now, if we didn't skip the pixel, it means that it's a colored pixel that should be part of a region, so we start ExpandRegionFrom this pixel.
The code to check if we're inside a region is pretty simple:
function IsInsideRegions($regions, $x, $y)
{
foreach($regions as $region)
{
if(($region["left"] <= $x && $region["right"] >= $x) &&
($region["bottom"] <= $y && $region["top"] >= $y))
{
return true;
}
}
return false;
}
Now, the expanding code will try to grow the region in each direction and will do so as long as it found new pixels to add to the region:
function ExpandRegionFrom($image, $x, $y, $backgroundColor, $padding)
{
$width = imagesx($image);
$height = imagesy($image);
$left = $x;
$bottom = $y;
$right = $x + 1;
$top = $y + 1;
$expanded = false;
do
{
$expanded = false;
$newLeft = ShouldExpandLeft($image, $backgroundColor, $left, $bottom, $top, $padding);
if($newLeft != $left)
{
$left = $newLeft;
$expanded = true;
}
$newRight = ShouldExpandRight($image, $backgroundColor, $right, $bottom, $top, $width, $padding);
if($newRight != $right)
{
$right = $newRight;
$expanded = true;
}
$newTop = ShouldExpandTop($image, $backgroundColor, $top, $left, $right, $height, $padding);
if($newTop != $top)
{
$top = $newTop;
$expanded = true;
}
$newBottom = ShouldExpandBottom($image, $backgroundColor, $bottom, $left, $right, $padding);
if($newBottom != $bottom)
{
$bottom = $newBottom;
$expanded = true;
}
}
while($expanded == true);
$region = array();
$region["left"] = $left;
$region["bottom"] = $bottom;
$region["right"] = $right;
$region["top"] = $top;
return $region;
}
The ShouldExpand methods could have been written in a cleaner fashion, but I went for something fast to prototype with:
function ShouldExpandLeft($image, $background, $left, $bottom, $top, $padding)
{
// Find the farthest pixel that is not $background starting at $left - $padding closing in to $left
for($x = max(0, $left - $padding); $x < $left; ++$x)
{
for($y = $bottom; $y <= $top; ++$y)
{
$pixelColor = imagecolorat($image, $x, $y);
if($pixelColor != $background)
{
return $x;
}
}
}
return $left;
}
function ShouldExpandRight($image, $background, $right, $bottom, $top, $width, $padding)
{
// Find the farthest pixel that is not $background starting at $right + $padding closing in to $right
$from = min($width - 1, $right + $padding);
$to = $right;
for($x = $from; $x > $to; --$x)
{
for($y = $bottom; $y <= $top; ++$y)
{
$pixelColor = imagecolorat($image, $x, $y);
if($pixelColor != $background)
{
return $x;
}
}
}
return $right;
}
function ShouldExpandTop($image, $background, $top, $left, $right, $height, $padding)
{
// Find the farthest pixel that is not $background starting at $top + $padding closing in to $top
for($x = $left; $x <= $right; ++$x)
{
for($y = min($height - 1, $top + $padding); $y > $top; --$y)
{
$pixelColor = imagecolorat($image, $x, $y);
if($pixelColor != $background)
{
return $y;
}
}
}
return $top;
}
function ShouldExpandBottom($image, $background, $bottom, $left, $right, $padding)
{
// Find the farthest pixel that is not $background starting at $bottom - $padding closing in to $bottom
for($x = $left; $x <= $right; ++$x)
{
for($y = max(0, $bottom - $padding); $y < $bottom; ++$y)
{
$pixelColor = imagecolorat($image, $x, $y);
if($pixelColor != $background)
{
return $y;
}
}
}
return $bottom;
}
Now, to see if the algorithm was succesful, I added some debug code.
Debug Rendering
I created a second image to store debug info and store it on disk so I could later see my progress.
Using the following code:
$large2 = imagecreatefromjpeg($largeFilename);
$red = imagecolorallocate($large2, 255, 0, 0);
$green = imagecolorallocate($large2, 0, 255, 0);
$blue = imagecolorallocate($large2, 0, 0, 255);
function DrawRegions($image, $regions, $color)
{
foreach($regions as $region)
{
imagerectangle($image, $region["left"], $region["bottom"], $region["right"], $region["top"], $color);
}
}
DrawRegions($large2, $regions, $red);
imagejpeg($large2, "regions.jpg");
I could validate that my partitioning code was doing a decent job:
Aspect Ratio
I decided to filter out some regions based on aspect ratio (the ratio between the width and the height). Other filtering could be applied such as average pixel color or something, but the aspect ratio check was very fast so I used it.
I simply defined a "window" where regions would be kept, if their aspect ration was between a minimum and maximum value;
$smallAspectRatio = imagesx($small) / imagesy($small);
function PruneOutWrongAspectRatio($regions, $minAspectRatio, $maxAspectRatio)
{
$result = array();
foreach($regions as $region)
{
$aspectRatio = ($region["right"] - $region["left"]) / ($region["top"] - $region["bottom"]);
if($aspectRatio >= $minAspectRatio && $aspectRatio <= $maxAspectRatio)
{
array_push($result, $region);
}
}
return $result;
}
$filterOnAspectRatio = true;
if($filterOnAspectRatio == true)
{
$regions = PruneOutWrongAspectRatio($regions, $smallAspectRatio - 0.1 * $smallAspectRatio, $smallAspectRatio + 0.1 * $smallAspectRatio);
DrawRegions($large2, $regions, $blue);
}
imagejpeg($large2, "aspectratio.jpg");
By adding the DrawRegions call, I now paint in blue the regions that are still in the list as potential positions:
As you can see, only 4 position remains!
Finding the Corners
We're almost done! Now, what I'm doing is looking at the colors in the four corners from the small picture, and try to find the best matching pixel in the corners of the remaining regions. This code has the most potential to fail so if you have to invest time in improving the solution, this code would be a good candidate.
function FindCorners($large, $small, $regions)
{
$result = array();
$bottomLeftColor = imagecolorat($small, 0, 0);
$blColors = GetColorComponents($bottomLeftColor);
$bottomRightColor = imagecolorat($small, imagesx($small) - 1, 0);
$brColors = GetColorComponents($bottomRightColor);
$topLeftColor = imagecolorat($small, 0, imagesy($small) - 1);
$tlColors = GetColorComponents($topLeftColor);
$topRightColor = imagecolorat($small, imagesx($small) - 1, imagesy($small) - 1);
$trColors = GetColorComponents($topRightColor);
foreach($regions as $region)
{
$bottomLeft = null;
$bottomRight = null;
$topLeft = null;
$topRight = null;
$regionWidth = $region["right"] - $region["left"];
$regionHeight = $region["top"] - $region["bottom"];
$maxRadius = min($regionWidth, $regionHeight);
$topLeft = RadialFindColor($large, $tlColors, $region["left"], $region["top"], 1, -1, $maxRadius);
$topRight = RadialFindColor($large, $trColors, $region["right"], $region["top"], -1, -1, $maxRadius);
$bottomLeft = RadialFindColor($large, $blColors, $region["left"], $region["bottom"], 1, 1, $maxRadius);
$bottomRight = RadialFindColor($large, $brColors, $region["right"], $region["bottom"], -1, 1, $maxRadius);
if($bottomLeft["found"] && $topRight["found"] && $topLeft["found"] && $bottomRight["found"])
{
$left = min($bottomLeft["x"], $topLeft["x"]);
$right = max($bottomRight["x"], $topRight["x"]);
$bottom = min($bottomLeft["y"], $bottomRight["y"]);
$top = max($topLeft["y"], $topRight["y"]);
array_push($result, array("left" => $left, "right" => $right, "bottom" => $bottom, "top" => $top));
}
}
return $result;
}
$closeOnCorners = true;
if($closeOnCorners == true)
{
$regions = FindCorners($large, $small, $regions);
DrawRegions($large2, $regions, $green);
}
I tried to find the matching color by increasing "radially" (its basically squares) from the corners until I find a matching pixel (within a tolerance):
function GetColorComponents($color)
{
return array("red" => $color & 0xFF, "green" => ($color >> 8) & 0xFF, "blue" => ($color >> 16) & 0xFF);
}
function GetDistance($color, $r, $g, $b)
{
$colors = GetColorComponents($color);
return (abs($r - $colors["red"]) + abs($g - $colors["green"]) + abs($b - $colors["blue"]));
}
function RadialFindColor($large, $color, $startx, $starty, $xIncrement, $yIncrement, $maxRadius)
{
$result = array("x" => -1, "y" => -1, "found" => false);
$treshold = 40;
for($r = 1; $r <= $maxRadius; ++$r)
{
$closest = array("x" => -1, "y" => -1, "distance" => 1000);
for($i = 0; $i <= $r; ++$i)
{
$x = $startx + $i * $xIncrement;
$y = $starty + $r * $yIncrement;
$pixelColor = imagecolorat($large, $x, $y);
$distance = GetDistance($pixelColor, $color["red"], $color["green"], $color["blue"]);
if($distance < $treshold && $distance < $closest["distance"])
{
$closest["x"] = $x;
$closest["y"] = $y;
$closest["distance"] = $distance;
break;
}
}
for($i = 0; $i < $r; ++$i)
{
$x = $startx + $r * $xIncrement;
$y = $starty + $i * $yIncrement;
$pixelColor = imagecolorat($large, $x, $y);
$distance = GetDistance($pixelColor, $color["red"], $color["green"], $color["blue"]);
if($distance < $treshold && $distance < $closest["distance"])
{
$closest["x"] = $x;
$closest["y"] = $y;
$closest["distance"] = $distance;
break;
}
}
if($closest["distance"] != 1000)
{
$result["x"] = $closest["x"];
$result["y"] = $closest["y"];
$result["found"] = true;
return $result;
}
}
return $result;
}
As you can see, I'm no PHP expert, I didn't know there was a built in function to get the rgb channels, oops!
Final Call
So now that the algorithm ran, let's see what it found using the following code:
foreach($regions as $region)
{
echo "Potentially between " . $region["left"] . "," . $region["bottom"] . " and " . $region["right"] . "," . $region["top"] . "\n";
}
imagejpeg($large2, "final.jpg");
imagedestroy($large2);
The output (which is pretty close to the real solution):
Potentially between 108,380 and 867,827
in 7.9796848297119 seconds
Giving this picture (the rectangle between 108,380 and 867,827 is drawn in green)
Hope this helps!
My solution work if there is no color (except white and black around the image, but you can modify the script to get it work differently)
$width = imagesx($this->img_src);
$height = imagesy($this->img_src);
// navigate through pixels of image
for ($y = 0; $y < $height; $y++) {
for ($x=0; $x < $width; $x++) {
list($r, $g, $b) = imagergbat($this->img_src, $x, $y);
$black = 0.1;
$white = 0.9;
// calculate if the color is next to white or black, if not register it as a good pixel
$gs = (($r / 3) + ($g / 3) + ($b / 3);
$first_pixel = array();
if ($gs > $white && $gs < $black) {
// get coordinate of first pixel (left top)
if (empty($first_pixel))
$first_pixel = array($x, $y);
// And save last_pixel each time till the last one
$last_pixel = array($x, $y);
}
}
}
And you get the coordinates of your image. You have just to crop it after this.
What I'd like here is a working, optimized version of my current code. While my function does return an array with actual results, I don't know if they are correct (I'm not a mathematics guru and I don't know Java code to compare my results against known implementations). Secondly, I'd like the function to be able to accept custom table sizes, but I don't know how to do that. Is table size equivalent to resampling the image? Am I applying the coefficients correctly?
// a lot of processing is required for large images
$image = imagecreatetruecolor(21, 21);
$black = imagecolorallocate($image, 0, 0, 0);
$white = imagecolorallocate($image, 255, 255, 255);
imagefilledellipse($image, 10, 10, 15, 15, $white);
print_r(imgDTC($image));
function imgDTC($img, $tableSize){
// m1 = Matrix1, an associative array with pixel data from the image
// m2 = Matrix2, an associative array with DCT Frequencies
// x1, y1 = coordinates in matrix1
// x2, y2 = coordinates in matrix2
$m1 = array();
$m2 = array();
// iw = image width
// ih = image height
$iw = imagesx($img);
$ih = imagesy($img);
// populate matrix1
for ($x1=0; $x1<$iw; $x1++) {
for ($y1=0; $y1<$ih; $y1++) {
$m1[$x1][$y1] = imagecolorat($img, $x1, $y1) & 0xff;
}
}
// populate matrix2
// for each coordinate in matrix2
for ($x2=0;$x2<$iw;$x2++) {
for ($y2=0;$y2<$ih;$y2++) {
// for each coordinate in matrix1
$sum = 1;
for ($x1=0;$x1<$iw;$x1++) {
for ($y1=0;$y1<$ih;$y1++) {
$sum +=
cos(((2*$x1+1)/(2*$iw))*$x2*pi()) *
cos(((2*$y1+1)/(2*$ih))*$y2*pi()) *
$m1[$x1][$y1]
;
}
}
// apply coefficients
$sum *= .25;
if ($x2 == 0 || $y2 == 0) {
$sum *= 1/sqrt(2);
}
$m2[$x2][$y2] = $sum;
}
}
return $m2;
}
My PHP function is a derivitive from this post in Java: Problems with DCT and IDCT algorithm in java. I have rewritten the code for php and readability. Ultimately, I am working on a script which will enable me to compare images and find similarities. The technique is outlined here: http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html.
Thanks!
This is how I performed my DCT what I'm doing here is to perform a 1 dimension DCT on each row. Then I took the result an perform the DTC on each column it's faster.
function dct1D($in) {
$results = array();
$N = count($in);
for ($k = 0; $k < $N; $k++) {
$sum = 0;
for ($n = 0; $n < $N; $n++) {
$sum += $in[$n] * cos($k * pi() * ($n + 0.5) / ($N));
}
$sum *= sqrt(2 / $N);
if ($k == 0) {
$sum *= 1 / sqrt(2);
}
$results[$k] = $sum;
}
return $results;
}
function optimizedImgDTC($img) {
$results = array();
$N1 = imagesx($img);
$N2 = imagesy($img);
$rows = array();
$row = array();
for ($j = 0; $j < $N2; $j++) {
for ($i = 0; $i < $N1; $i++)
$row[$i] = imagecolorat($img, $i, $j);
$rows[$j] = dct1D($row);
}
for ($i = 0; $i < $N1; $i++) {
for ($j = 0; $j < $N2; $j++)
$col[$j] = $rows[$j][$i];
$results[$i] = dct1D($col);
}
return $results;
}
Most algorithm I found on internet assume that the input matrix is 8x8. That's why you multiplyed by 0.25.
In general you should multiply by sqrt(2 / N) a 1D matrix and here we are in 2D so sqrt(2/N1) * sqrt(2/N2). If you do this for N1 = 8 and N2 = 8:
sqrt(2/8)^2 = 2/8 = 1/4 = 0.25
The other thing was to multiply by 1/sqrt(2) X0 it's for 1D matrix here we are in 2D so you multiply when k1 = 0 or k2 = 0. When k1 = 0 and k2 = 0 you have to do it twice.
First you need to test your function so find any working implementation. And compare results from your implementation with the results of the working implementation (with the same input).
If you whant your code to be faster you can look at this paper http://infoscience.epfl.ch/record/34246/files/Vetterli85.pdf (the first 2 parts).
In your case you can't use a custom table size because it should match the image size (can be wrong).
Hello fellow earthlings. A quesion about RGB color and its usefulness in a simple tiny php code:
Imagine I have variable $colorA containning a valid six char color. say B1B100, a greenish natural color. Now If I would like to make a new color from that, which is, say, ten steps lighter thatn that original color, roughly.
$colorA = B1B100 // original color
php code with little color engine lightening stuff up goes here
$colorB = ?????? // original color lightened up
Is there a php ready function that KNOWS rgb colors something like
php function RGB ( input color, what to do, output color)
Where what to do could be +/- 255 values of brightness etc etc.
Is something like this already possible or am I day dreaming?
rgb-hsl($colorA, +10, $colorB);
If this does not exist, what would be the shortest code for doing this? Suggestions, code or ideas are all answers to me. Thanks.
This SO question has a full-blown PHP script that can convert a RGB to a HSL colour, and increase its H component of a HSL colour - it should be trivial to change to increase L instead.
In general if you want a lighter shade of a particular colour, the most accurate process is to convert from RGB to HSL (or HSV), change the 'L' (or 'V') value which represents lightness, and then convert back to RGB.
This will preserve the "hue", which represents where the colour sits on the spectrum, but change the "tint" (if lightening) or "shade" (if darkening) of that colour.
See http://en.wikipedia.org/wiki/HSL_and_HSV for more information.
On this website: http://www.sitepoint.com/forums/showthread.php?t=586223 they are talking about this code which is originally made by opensource Drupal. Seems to work fine in PHP!?
Now, how do I now indermingle myself with this code and change the lightness of an HSL value, before its outputted as RGB again?
<?php
### RGB >> HSL
function _color_rgb2hsl($rgb) {
$r = $rgb[0]; $g = $rgb[1]; $b = $rgb[2];
$min = min($r, min($g, $b)); $max = max($r, max($g, $b));
$delta = $max - $min; $l = ($min + $max) / 2; $s = 0;
if ($l > 0 && $l < 1) {
$s = $delta / ($l < 0.5 ? (2 * $l) : (2 - 2 * $l));
}
$h = 0;
if ($delta > 0) {
if ($max == $r && $max != $g) $h += ($g - $b) / $delta;
if ($max == $g && $max != $b) $h += (2 + ($b - $r) / $delta);
if ($max == $b && $max != $r) $h += (4 + ($r - $g) / $delta);
$h /= 6;
} return array($h, $s, $l);
}
### HSL >> RGB
function _color_hsl2rgb($hsl) {
$h = $hsl[0]; $s = $hsl[1]; $l = $hsl[2];
$m2 = ($l <= 0.5) ? $l * ($s + 1) : $l + $s - $l*$s;
$m1 = $l * 2 - $m2;
return array(_color_hue2rgb($m1, $m2, $h + 0.33333),
_color_hue2rgb($m1, $m2, $h),
_color_hue2rgb($m1, $m2, $h - 0.33333));
}
### Helper function for _color_hsl2rgb().
function _color_hue2rgb($m1, $m2, $h) {
$h = ($h < 0) ? $h + 1 : (($h > 1) ? $h - 1 : $h);
if ($h * 6 < 1) return $m1 + ($m2 - $m1) * $h * 6;
if ($h * 2 < 1) return $m2;
if ($h * 3 < 2) return $m1 + ($m2 - $m1) * (0.66666 - $h) * 6;
return $m1;
}
### Convert a hex color into an RGB triplet.
function _color_unpack($hex, $normalize = false) {
if (strlen($hex) == 4) {
$hex = $hex[1] . $hex[1] . $hex[2] . $hex[2] . $hex[3] . $hex[3];
} $c = hexdec($hex);
for ($i = 16; $i >= 0; $i -= 8) {
$out[] = (($c >> $i) & 0xFF) / ($normalize ? 255 : 1);
} return $out;
}
### Convert an RGB triplet to a hex color.
function _color_pack($rgb, $normalize = false) {
foreach ($rgb as $k => $v) {
$out |= (($v * ($normalize ? 255 : 1)) << (16 - $k * 8));
}return '#'. str_pad(dechex($out), 6, 0, STR_PAD_LEFT);
}
/* $testrgb = array(0.2,0.75,0.4); //RGB to start with
print_r($testrgb); */
print "Hex: ";
$testhex = "#b7b700";
print $testhex;
$testhex2rgb = _color_unpack($testhex,true);
print "<br />RGB: ";
var_dump($testhex2rgb);
print "<br />HSL color module: ";
$testrgb2hsl = _color_rgb2hsl($testhex2rgb); //Converteren naar HSL
var_dump($testrgb2hsl);
print "<br />RGB: ";
$testhsl2rgb = _color_hsl2rgb($testrgb2hsl); // En weer terug naar RGB
var_dump($testhsl2rgb);
print "<br />Hex: ";
$testrgb2hex = _color_pack($testhsl2rgb,true);
var_dump($testrgb2hex);
?>
PHP does have a couple image manipulation libraries. Either GD or Imagemagick
EDIT: I jumped the gun, these libraries do not have direct PHP color manipulation functions - I honestly assumed they did of a sort after seeing a lot of the things they can do with images via PHP. They do accomplish a lot of cool things. Here's one guy's example.