Prinzip und Implementierung der PHP-Bilderkennungstechnologie

class gjPhone
{

  protected $imgPath; // 图片路径
  protected $imgSize; // 图片大小
  protected $hecData; // 分离后数组
  protected $horData; // 横向整理的数据
  protected $verData; // 纵向整理的数据
  function __construct ($path)
  {
    $this->imgPath = $path;
  }

  public function getHec ()
  {
    $size = getimagesize($this->imgPath);
    $res = imagecreatefrompng($this->imgPath);
    for ($i = 0; $i < $size[1]; ++ $i) {
      for ($j = 0; $j < $size[0]; ++ $j) {
        $rgb = imagecolorat($res, $j, $i);
        $rgbarray = imagecolorsforindex($res, $rgb);
        if ($rgbarray[&#39;red&#39;] < 125 || $rgbarray[&#39;green&#39;] < 125 ||
             $rgbarray[&#39;blue&#39;] < 125) {
          $data[$i][$j] = 1;
        } else {
          $data[$i][$j] = 0;
        }
      }
    }
    $this->imgSize = $size;
    $this->hecData = $data;
  }

  public function magHorData ()
  {
    $data = $this->hecData;
    $size = $this->imgSize;
    $z = 0;
    for ($i = 0; $i < $size[1]; ++ $i) {
      if (in_array(&#39;1&#39;, $data[$i])) {
        $z ++;
        for ($j = 0; $j < $size[0]; ++ $j) {
          if ($data[$i][$j] == &#39;1&#39;) {
            $newdata[$z][$j] = 1;
          } else {
            $newdata[$z][$j] = 0;
          }
        }
      }
    }
    return $this->horData = $newdata;
  }

  public function showPhone ($ndatas)
  {
    error_reporting(0);
    $phone = null;
    $d = 0;
    foreach ($ndatas as $key => $val) {
      if (in_array(1, $val)) {
        foreach ($val as $k => $v) {
          $ndArr[$d] .= $v;
        }
      }
      if (! in_array(1, $val)) {
        $d ++;
      }
    }
    foreach ($ndArr as $key01 => $val01) {
      $phone .= $this->initData($val01);
    }
    return $phone;
  }

  /**
   * 初始数据
   */
  public function initData ($numStr)
  {
    $result = null;
    $data = array(
        &#39;1&#39; => &#39;00000000111000000000000001110000000001001000100000000010100011000000000011000110000000000110000100000000010110011000000&#39;,
        &#39;5&#39; => &#39;00000000001000000000000000010000000000100100100000000000101001110000000000100000110000000011000000100000001101000010000&#39;,
        &#39;10&#39; => &#39;00000011100011100000000011001100100100100010010001000110000100100010001100001001000100011000010010001001001001100010100&#39;
    );
    foreach ($data as $key => $val) {
      similar_text($numStr, $val, $pre);
      if ($pre > 95) { // 相似度95%以上
        $result = $key;
        break;
      }
    }
    return $result;
  }
}

$imgurl = &#39;jd.png&#39;;
list ($width, $heght, $type, $attr) = getimagesize($imgurl);
$new_w = 17;
$new_h = 11;
$thisimage = imagecreatetruecolor($new_w, $new_h); // $new_w, $new_h 为裁剪后的图片宽高
$background = imagecolorallocate($thisimage, 255, 255, 255);
imagefilledrectangle($thisimage, 0, 0, $new_w, $new_h, $background);
$oldimg = imagecreatefrompng($imgurl); // 载入原始图片
                    
// 首先定位要取图的位置(这里可以通过前端js或者其他手段定位，由于我这是测试，所以就ps定位并写死了)
$weizhi = array(
    &#39;1&#39; => 165,
    &#39;5&#39; => 308,
    &#39;10&#39; => 456
);

foreach ($weizhi as $wwzz) {
  $src_y = 108;
  imagecopy($thisimage, $oldimg, 0, 0, $wwzz, $src_y, $new_w, $new_h); // $src_y,$new_w为原图中裁剪区域的左上角坐标拷贝图像的一部分将src_im图像中坐标从src_x，src_y开始，宽度为src_w，高度为src_h的一部分拷贝到dst_im图像中坐标为dst_x和dst_y的位置上。
  $tem_png = &#39;tem_1.png&#39;;
  imagepng($thisimage, __DIR__ . &#39;/&#39; . $tem_png); // 通过定位从原图中copy出想要识别的位置并生成新的缓存图，用以后面的图像识别类使用。
  
  $gjPhone = new gjPhone($tem_png); // 实例化类
  $gjPhone->getHec(); // 进行图像像素分离
  $horData = $gjPhone->magHorData(); // 将分离出是数据转成01表示的图像、这里可以根据自己喜好定
  $phone = $gjPhone->showPhone($horData); // 将转换好的01表示的数据与库中的数据进行匹配，匹配度95以上就算成功，库这里由于是做测试就直接写了数组
  echo &#39;| &#39; . $phone . &#39; | &#39;;
}