已移至GitHub

Question

我想比较下面图片之间的相似性。根据我的要求，我想将所有这些图像识别为相似，因为它使用相同的颜色，相同的剪贴画。这些图像的唯一区别是旋转，缩放和剪贴画的放置。由于所有3件T恤都使用了相同的颜色和剪贴画，我想将所有3张图片识别为相似。我尝试了hackerfactor.com中描述的方法。但根据我的要求，它并没有给我正确的结果。如何识别所有这些图像？你有什么建议吗？请帮帮我。

enter image description here

下面的图像应该被识别为与上面的图像不同。（即使T恤颜色相同，剪贴画也不同。最后的T恤与上面不同，因为它使用相同的剪贴画，但是两次。）

Image A Image B Image C

Answer 1

已移至GitHub

因为这个问题非常有趣，所以我将整个事情移到了GitHub，在那里你可以找到当前的实现： ImageCompare

原始答案

我做了一个非常简单的方法，使用img-resize并比较调整大小的图像的平均颜色。

$binEqual = [
    file_get_contents('http://i.stack.imgur.com/D8ct1.png'),
    file_get_contents('http://i.stack.imgur.com/xNZt1.png'),
    file_get_contents('http://i.stack.imgur.com/kjGjm.png')
];

$binDiff = [
    file_get_contents('http://i.stack.imgur.com/WIOHs.png'),
    file_get_contents('http://i.stack.imgur.com/ljoBT.png'),
    file_get_contents('http://i.stack.imgur.com/qEKSK.png')
];


function getAvgColor($bin, $size = 10) {

    $target = imagecreatetruecolor($size, $size);
    $source = imagecreatefromstring($bin);

    imagecopyresized($target, $source, 0, 0, 0, 0, $size, $size, imagesx($source), imagesy($source));

    $r = $g = $b = 0;

    foreach(range(0, $size - 1) as $x) {
        foreach(range(0, $size - 1) as $y) {
            $rgb = imagecolorat($target, $x, $y);
            $r += $rgb >> 16;
            $g += $rgb >> 8 & 255;
            $b += $rgb & 255;
        }
    }   

    unset($source, $target);

    return (floor($r / $size ** 2) << 16) +  (floor($g / $size ** 2) << 8)  + floor($b / $size ** 2);
}

function compAvgColor($c1, $c2, $tolerance = 4) {

    return abs(($c1 >> 16) - ($c2 >> 16)) <= $tolerance && 
           abs(($c1 >> 8 & 255) - ($c2 >> 8 & 255)) <= $tolerance &&
           abs(($c1 & 255) - ($c2 & 255)) <= $tolerance;
}

$perms = [[0,1],[0,2],[1,2]];

foreach($perms as $perm) {
    var_dump(compAvgColor(getAvgColor($binEqual[$perm[0]]), getAvgColor($binEqual[$perm[1]])));
}

foreach($perms as $perm) {
    var_dump(compAvgColor(getAvgColor($binDiff[$perm[0]]), getAvgColor($binDiff[$perm[1]])));
}

对于使用的尺寸和颜色容差，我得到了预期的结果：

bool(true)
bool(true)
bool(true)
bool(false)
bool(false)
bool(false)

更高级的实施

空T恤比较： Plain T-Shirt

$binEqual = [
    file_get_contents('http://i.stack.imgur.com/D8ct1.png'),
    file_get_contents('http://i.stack.imgur.com/xNZt1.png'),
    file_get_contents('http://i.stack.imgur.com/kjGjm.png')
];

$binDiff = [
    file_get_contents('http://i.stack.imgur.com/WIOHs.png'),
    file_get_contents('http://i.stack.imgur.com/ljoBT.png'),
    file_get_contents('http://i.stack.imgur.com/qEKSK.png')
];

class Color {
    private $r = 0;
    private $g = 0;
    private $b = 0;

    public function __construct($r = 0, $g = 0, $b = 0)
    {
        $this->r = $r;
        $this->g = $g;
        $this->b = $b;
    }

    public function r()
    {
        return $this->r;
    }

    public function g()
    {
        return $this->g;
    }

    public function b()
    {
        return $this->b;
    }

    public function toInt()
    {
        return $this->r << 16 + $this->g << 8 + $this->b;
    }

    public function toRgb()
    {
        return [$this->r, $this->g, $this->b];  
    }

    public function mix(Color $color)
    {
        $this->r = round($this->r + $color->r() / 2);
        $this->g = round($this->g + $color->g() / 2);
        $this->b = round($this->b + $color->b() / 2);
    }

    public function compare(Color $color, $tolerance = 500)
    {
        list($r1, $g1, $b1) = $this->toRgb();
        list($r2, $g2, $b2) = $color->toRgb();

        $diff = round(sqrt(pow($r1 - $r2, 2) + pow($g1 - $g2, 2) + pow($b1 - $b2, 2)));

        printf("Comp r(%s : %s), g(%s : %s), b(%s : %s) Diff %s \n", $r1, $r2, $g1, $g2, $b1, $b2, $diff);

        return  $diff <= $tolerance;
    }

    public static function fromInt($int) {
        return new self($int >> 16, $int >> 8 & 255, $int & 255);
    }
}


function getAvgColor($bin, $size = 5) {

    $target    = imagecreatetruecolor($size, $size);
    $targetTmp = imagecreatetruecolor($size, $size);

    $sourceTmp = imagecreatefrompng('http://i.stack.imgur.com/gfn5A.png');
    $source    = imagecreatefromstring($bin);

    imagecopyresized($target, $source, 0, 0, 0, 0, $size, $size, imagesx($source), imagesy($source));
    imagecopyresized($targetTmp, $sourceTmp, 0, 0, 0, 0, $size, $size, imagesx($source), imagesy($source));

    $r = $g = $b = $relPx = 0;

    $baseColor = new Color();

    foreach(range(0, $size - 1) as $x) {
        foreach(range(0, $size - 1) as $y) {
            if (imagecolorat($target, $x, $y) != imagecolorat($targetTmp, $x, $y))
                $baseColor->mix(Color::fromInt(imagecolorat($target, $x, $y)));
        }
    }

    unset($source, $target, $sourceTmp, $targetTmp);

    return $baseColor;

}

$perms = [[0,0], [1,0], [2,0], [1,0], [1,1], [1,2], [2,0], [2,1], [2,2]];

echo "Equal\n";
foreach($perms as $perm) {
    var_dump(getAvgColor($binEqual[$perm[0]])->compare(getAvgColor($binEqual[$perm[1]])));
}

echo "Different\n";
foreach($perms as $perm) {
    var_dump(getAvgColor($binEqual[$perm[0]])->compare(getAvgColor($binDiff[$perm[1]])));
}

结果：

Equal
Comp r(101 : 101), g(46 : 46), b(106 : 106) Diff 0 
bool(true)
Comp r(121 : 101), g(173 : 46), b(249 : 106) Diff 192 
bool(true)
Comp r(219 : 101), g(179 : 46), b(268 : 106) Diff 241 
bool(true)
Comp r(121 : 101), g(173 : 46), b(249 : 106) Diff 192 
bool(true)
Comp r(121 : 121), g(173 : 173), b(249 : 249) Diff 0 
bool(true)
Comp r(121 : 219), g(173 : 179), b(249 : 268) Diff 100 
bool(true)
Comp r(219 : 101), g(179 : 46), b(268 : 106) Diff 241 
bool(true)
Comp r(219 : 121), g(179 : 173), b(268 : 249) Diff 100 
bool(true)
Comp r(219 : 219), g(179 : 179), b(268 : 268) Diff 0 
bool(true)
Different
Comp r(101 : 446), g(46 : 865), b(106 : 1242) Diff 1442 
bool(false)
Comp r(121 : 446), g(173 : 865), b(249 : 1242) Diff 1253 
bool(false)
Comp r(219 : 446), g(179 : 865), b(268 : 1242) Diff 1213 
bool(false)
Comp r(121 : 446), g(173 : 865), b(249 : 1242) Diff 1253 
bool(false)
Comp r(121 : 654), g(173 : 768), b(249 : 1180) Diff 1227 
bool(false)
Comp r(121 : 708), g(173 : 748), b(249 : 1059) Diff 1154 
bool(false)
Comp r(219 : 446), g(179 : 865), b(268 : 1242) Diff 1213 
bool(false)
Comp r(219 : 654), g(179 : 768), b(268 : 1180) Diff 1170 
bool(false)
Comp r(219 : 708), g(179 : 748), b(268 : 1059) Diff 1090 
bool(false)

在此计算中忽略背景，导致平均颜色的差异更大。

最终实施（OOP）

相当有趣的主题。所以我试着把它调整起来。现在这是一个完整的OOP实现。您现在可以创建一个新图像并减去它的一些遮罩以消除背景。然后您可以使用compare方法将一个图像与另一个图像进行比较。为了保持计算的限制，最好先调整图像大小（蒙版总是适合当前图像）

比较algorythme它自动将两个图像分成多个图块，然后消除几乎等于白色平均颜色的图块，然后比较所有剩余图块排列的平均颜色。

Class Image {

    const HASH_SIZE = 8;
    const AVG_SIZE = 10;

    private $img = null;

    public function __construct($resource)
    {
        $this->img = $resource;;
    }

    private function permute(array $a1, array $a2) {
        $perms = array();
        for($i = 0; $i < sizeof($a1); $i++) {
            for($j = $i; $j < sizeof($a2); $j++) {
                if ($i != $j) {
                    $perms[] = [$a1[$i], 
                    $a2[$j]];
                }
            }
        }

        return $perms;
    }

    public function compare(Image $comp) {
        $avgComp = array();

        foreach($comp->chunk(25) as $chunk) {
            $avgComp[] = $chunk->avg();
        }

        $avgOrg = array();

        foreach($this->chunk(25) as $chunk) {
            $avgOrg[] = $chunk->avg();
        }

        $white = Color::fromInt(0xFFFFFF);

        $avgComp = array_values(array_filter($avgComp, function(Color $color) use ($white){
            return $white->compare($color, 1000);
        }));

        $avgOrg = array_values(array_filter($avgOrg, function(Color $color) use ($white){
            return $white->compare($color, 1000);
        }));

        $equal = 0;
        $pairs = $this->permute($avgOrg, $avgComp);

        foreach($pairs as $pair) {
            $equal += $pair[0]->compare($pair[1], 100) ? 1 : 0;
        }

        return ($equal / sizeof($pairs));
    }

    public function substract(Image $mask, $tolerance = 50)
    {
        $size = $this->size();

        if ($mask->size() != $size) {
            $mask = $mask->resize($size);
        }

        for ($x = 0; $x < $size[0]; $x++) {
            for ($y = 0; $y < $size[1]; $y++) {
                if ($this->colorat($x, $y)->compare($mask->colorat($x, $y), $tolerance))
                    imagesetpixel($this->img, $x, $y, 0xFFFFFF);
            }
        }

        return $this;
    }

    public function avg($size = 10)
    {
        $target = $this->resize([self::AVG_SIZE, self::AVG_SIZE]);

        $avg   = Color::fromInt(0x000000);
        $white = Color::fromInt(0xFFFFFF);  

        for ($x = 0; $x < self::AVG_SIZE; $x++) {
            for ($y = 0; $y < self::AVG_SIZE; $y++) {
                $color = $target->colorat($x, $y);
                if (!$color->compare($white, 10))
                    $avg->mix($color);
            }
        }

        return $avg;
    }

    public function colorat($x, $y)
    {
        return Color::fromInt(imagecolorat($this->img, $x, $y));
    }

    public function chunk($chunkSize = 10)
    {
        $collection = new ImageCollection();
        $size = $this->size();

        for($x = 0; $x < $size[0]; $x += $chunkSize) {
            for($y = 0; $y < $size[1]; $y += $chunkSize) {
                switch (true) {
                    case ($x + $chunkSize > $size[0] && $y + $chunkSize > $size[1]):
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $size[0] - $x, 'width' => $size[1] - $y]));
                        break;
                    case ($x + $chunkSize > $size[0]):
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $size[0] - $x, 'width' => $chunkSize]));
                        break;
                    case ($y + $chunkSize > $size[1]):
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $chunkSize, 'width' => $size[1] - $y]));
                        break;
                    default:
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $chunkSize, 'width' => $chunkSize]));
                        break;
                }
            }
        }

        return $collection;
    }

    public function slice(array $rect)
    {
        return Image::fromResource(imagecrop($this->img, $rect));
    }

    public function size()
    {
        return [imagesx($this->img), imagesy($this->img)];
    }

    public function resize(array $size = array(100, 100))
    {
        $target = imagecreatetruecolor($size[0], $size[1]);
        imagecopyresized($target, $this->img, 0, 0, 0, 0, $size[0], $size[1], imagesx($this->img), imagesy($this->img));

        return Image::fromResource($target);
    }

    public function show()
    {
        header("Content-type: image/png");
        imagepng($this->img);
        die();
    }

    public function save($name = null, $path = '') {
        if ($name === null) {
            $name = $this->hash();
        }

        imagepng($this->img, $path . $name . '.png');

        return $this;
    }

    public function hash()
    {
                // Resize the image.
        $resized = imagecreatetruecolor(self::HASH_SIZE, self::HASH_SIZE);
        imagecopyresampled($resized, $this->img, 0, 0, 0, 0, self::HASH_SIZE, self::HASH_SIZE, imagesx($this->img), imagesy($this->img));
        // Create an array of greyscale pixel values.
        $pixels = [];
        for ($y = 0; $y < self::HASH_SIZE; $y++)
        {
            for ($x = 0; $x < self::HASH_SIZE; $x++)
            {
                $rgb = imagecolorsforindex($resized, imagecolorat($resized, $x, $y));
                $pixels[] = floor(($rgb['red'] + $rgb['green'] + $rgb['blue']) / 3);
            }
        }
        // Free up memory.
        imagedestroy($resized);
        // Get the average pixel value.
        $average = floor(array_sum($pixels) / count($pixels));
        // Each hash bit is set based on whether the current pixels value is above or below the average.
        $hash = 0; $one = 1;
        foreach ($pixels as $pixel)
        {
            if ($pixel > $average) $hash |= $one;
            $one = $one << 1;
        }
        return $hash;
    }

    public static function fromResource($resource)
    {
        return new self($resource);
    }

    public static function fromBin($binf)
    {
        return new self(imagecreatefromstring($bin));
    }

    public static function fromFile($path)
    {
        return new self(imagecreatefromstring(file_get_contents($path)));
    }
}

class ImageCollection implements IteratorAggregate
{
    private $images = array();

    public function __construct(array $images = array())
    {
        $this->images = $images;
    }

    public function push(Image $image) {
        $this->images[] = $image;
        return $this;
    }

    public function pop()
    {
        return array_pop($this->images);
    }

    public function save()
    {
        foreach($this->images as $image)
        {
            $image->save();
        }

        return $this;
    }

    public function getIterator() {
        return new ArrayIterator($this->images);
    }
}

class Color {
    private $r = 0;
    private $g = 0;
    private $b = 0;

    public function __construct($r = 0, $g = 0, $b = 0)
    {
        $this->r = $r;
        $this->g = $g;
        $this->b = $b;
    }

    public function r()
    {
        return $this->r;
    }

    public function g()
    {
        return $this->g;
    }

    public function b()
    {
        return $this->b;
    }

    public function toInt()
    {
        return $this->r << 16 + $this->g << 8 + $this->b;
    }

    public function toRgb()
    {
        return [$this->r, $this->g, $this->b];  
    }

    public function mix(Color $color)
    {
        $this->r = round($this->r + $color->r() / 2);
        $this->g = round($this->g + $color->g() / 2);
        $this->b = round($this->b + $color->b() / 2);
    }

    public function compare(Color $color, $tolerance = 500)
    {
        list($r1, $g1, $b1) = $this->toRgb();
        list($r2, $g2, $b2) = $color->toRgb();

        $diff = round(sqrt(pow($r1 - $r2, 2) + pow($g1 - $g2, 2) + pow($b1 - $b2, 2)));

        //printf("Comp r(%s : %s), g(%s : %s), b(%s : %s) Diff %s \n", $r1, $r2, $g1, $g2, $b1, $b2, $diff);

        return  $diff <= $tolerance;
    }

    public static function fromInt($int) {
        return new self($int >> 16, $int >> 8 & 255, $int & 255);
    }
}

$mask = Image::fromFile('http://i.stack.imgur.com/gfn5A.png');

$image1 = Image::fromFile('http://i.stack.imgur.com/D8ct1.png')->resize([50, 100])->substract($mask, 100);
$image2 = Image::fromFile('http://i.stack.imgur.com/xNZt1.png')->resize([50, 100])->substract($mask, 100);
$image3 = Image::fromFile('http://i.stack.imgur.com/kjGjm.png')->resize([50, 100])->substract($mask, 100);

$other1 = Image::fromFile('http://i.stack.imgur.com/WIOHs.png')->resize([50, 100])->substract($mask, 100);
$other2 = Image::fromFile('http://i.stack.imgur.com/ljoBT.png')->resize([50, 100])->substract($mask, 100);
$other3 = Image::fromFile('http://i.stack.imgur.com/qEKSK.png')->resize([50, 100])->substract($mask, 100);


echo "Equal\n";
var_dump(
    $image1->compare($image2),
    $image1->compare($image3),
    $image2->compare($image3)
);

echo "Image 1 to Other\n";
var_dump(
    $image1->compare($other1),
    $image1->compare($other2),
    $image1->compare($other3)
);

echo "Image 2 to Other\n";
var_dump(
    $image2->compare($other1),
    $image2->compare($other2),
    $image2->compare($other3)
);

echo "Image 3 to Other\n";
var_dump(
    $image3->compare($other1),
    $image3->compare($other2),
    $image3->compare($other3)
);

结果：

Equal
float(0.47619047619048)
float(0.53333333333333)
float(0.4)
Image 1 to Other
int(0)
int(0)
int(0)
Image 2 to Other
int(0)
int(0)
int(0)
Image 3 to Other
int(0)
int(0)
int(0)

Answer 2

我并未声称对此主题一无所知，我认为通常称之为“愿景”。

然而，我会做的是：

流速：

Posterise ，以最少的颜色/阴影（猜测）。
删除两种最大的颜色（白色+衬衫）。
比较剩余的调色板，如果方案差异太大，则会失败。
计算围绕任何剩余的＆＃39;颜色blob＆＃39; （见https://en.wikipedia.org/wiki/Convex_hull）
比较多边形的数量和每个图像中最大多边形的角度数和角度值（不是大小），然后失败或通过。

在这样的设置中的主要问题是圆形...就像在一个颜色的海报上那样，恰好在两种颜色之间的中点处...有时它会变成颜色A，有时会变成颜色B. 我想这与多边形相同。

Answer 3

SIMILAR计算两个相等尺寸图像之间的归一化互相关相似性度量。归一化的互相关度量度量衡量两个图像的相似程度，而不是它们的差异.ncc度量值的范围在0（不相似）和1（相似）之间。如果mode = g，则两个图像将转换为灰度。如果mode = rgb，则首先将两个图像转换为colorspace = rgb。接下来，将为每个信道计算ncc相似性度量。最后，它们将合并为rms值。注意：此度量标准不适用于常量颜色通道，因为它为该通道生成ncc度量标准= 0/0。因此，不建议使用具有完全不透明或完全透明的alpha通道的图像来运行脚本。

试试这个api，

http://www.phpclasses.org/package/8255-PHP-Compare-two-images-to-find-if-they-are-similar.html

Answer 4

正如有人提到的，除了计算图像的直方图并进行比较之外的其他任何事情都不容易实现。这是一个示例，为所提供的图像提供正确的结果。这里的关键点是如何在峰值颜色数量和可接受量之间取得适当的平衡（similarity( $histograms, $levels = 30, $enough = 28 )）。

function histograms( $images ) {
    foreach( $images as $img ) {
        $image = imagecreatefrompng( $img );
        $width = imagesx( $image );
        $height = imagesy( $image );
        $num_pixels = $width * $height; 

        $histogram = [];
        for ( $x = 0; $x < $width; $x++ ) {
            for ( $y = 0; $y < $height; $y++ ) {
                $rgb = imagecolorat( $image, $y, $x );
                $rgb = [ $rgb >> 16, ( $rgb >> 8 ) & 0xFF, $rgb & 0xFF ];

                $histo_v = (int) round( ( $rgb[0] + $rgb[1] + $rgb[02] ) / 3 );
                $histogram[ $histo_v ] = array_key_exists( $histo_v, $histogram ) ? $histogram[ $histo_v ] + $histo_v/$num_pixels : $histo_v/$num_pixels;
            }
        }
        $histograms[$img] = $histogram;
        arsort( $histograms[$img] );
    }

    return $histograms;
}


function similarity( $histograms, $levels = 30, $enough = 28 ) {
    $keys = array_keys( $histograms );
    $output = [];
    for ( $x = 0; $x < count( $histograms ) - 1; $x++ ) {
        for ( $y = $x + 1; $y < count( $histograms ); $y++ ) {      
            $similarity = count( array_intersect_key( array_slice( $histograms[ $keys[$x] ], 0, $levels, true ), array_slice( $histograms[ $keys[$y] ], 0, $levels, true ) ) );

            if ( $similarity > $enough ) $output[] = [ $keys[$x], $keys[$y], $similarity ];                 
        }
    }
    return $output;
}


$histograms = histograms( [ 'http://i.stack.imgur.com/D8ct1.png', 'http://i.stack.imgur.com/xNZt1.png', 'http://i.stack.imgur.com/kjGjm.png', 'http://i.stack.imgur.com/WIOHs.png', 'http://i.stack.imgur.com/ljoBT.png', 'http://i.stack.imgur.com/qEKSK.png' ] );
$similarity = similarity( $histograms );

print_r( $similarity );

/*
Array
(
    [0] => Array
        (
            [0] => http://i.stack.imgur.com/D8ct1.png
            [1] => http://i.stack.imgur.com/xNZt1.png
            [2] => 30
        )

    [1] => Array
        (
            [0] => http://i.stack.imgur.com/D8ct1.png
            [1] => http://i.stack.imgur.com/kjGjm.png
            [2] => 30
        )

    [2] => Array
        (
            [0] => http://i.stack.imgur.com/D8ct1.png
            [1] => http://i.stack.imgur.com/qEKSK.png
            [2] => 29
        )

    [3] => Array
        (
            [0] => http://i.stack.imgur.com/xNZt1.png
            [1] => http://i.stack.imgur.com/kjGjm.png
            [2] => 30
        )

    [4] => Array
        (
            [0] => http://i.stack.imgur.com/xNZt1.png
            [1] => http://i.stack.imgur.com/qEKSK.png
            [2] => 29
        )

    [5] => Array
        (
            [0] => http://i.stack.imgur.com/kjGjm.png
            [1] => http://i.stack.imgur.com/qEKSK.png
            [2] => 29
        )

)
*/

This article也帮我创建了直方图。

如何使用PHP比较图像相似性而不管比例，旋转？

4 个答案:

已移至GitHub

原始答案

更高级的实施

最终实施（OOP）