关于前端:Day-26100-前端JS实现图像像素临近色的Demo

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1、需要

要用 JS 来实现魔法棒的性能,首先须要实现找到邻近色彩的像素点,再标识进去。
刚实现了找到了邻近根本的像素点的局部。

2、代码

是图像宰割中的泛洪算法在前端中的利用。

<!--
 * @Author: ArdenZhao
 * @Date: 2022-01-18 14:09:54
 * @LastEditors: Do not edit
 * @LastEditTime: 2022-02-08 19:02:01
 * @FilePath: /magic_wand/demo/4、myFloodFillData.html
-->
<!DOCTYPE html>
<html>

<head>
  <meta charset="utf-8">
  <title>Hello OpenCV.js</title>
  <style>
    #imageSrc,
    #canvasOutput {
      width: 50%;
      height: auto;
    }
  </style>
</head>

<body>
  <h2>Hello OpenCV.js</h2>
  <p id="status">OpenCV.js is loading...</p>
  <div>
    <div class="inputoutput">
      <img id="imageSrc" alt="No Image" />
      <div class="caption">imageSrc</div>
    </div>
    <div class="inputoutput">
      <canvas id="canvasOutput"></canvas>
      <div class="caption">canvasOutput</div>
    </div>
  </div>

  <script type="text/javascript">
    // 获取图片标签
    let imgElement = document.getElementById('imageSrc');
    // 获取 canvas 标签
    let canvasElement = document.getElementById('canvasOutput');
    imgElement.src = 'https://t7.baidu.com/it/u=1595072465,3644073269&fm=193&f=GIF';
    imgElement.crossOrigin = "Anonymous";

    function init_image(self, image_file) {self.image = cv2.imdecode(np.fromfile(image_file, dtype = np.uint8), 1)
      self.image_raw = self.image.copy()
      // self.img_h, self.img_w = self.image.shape[0: 2]  // 原图宽高
    }
    let imageArr = [];
    // 2、获取图片的宽高
    let imageWidth = 0
    let imageHeight = 0
    imgElement.onload = function () {
      imageWidth = imgElement.width
      imageHeight = imgElement.height
    };
    // 异步获取后显示失常
    function onOpenCvReady() {console.log('[ cv] >', cv)
      document.getElementById('status').innerHTML = 'OpenCV.js is ready.';
      cv['onRuntimeInitialized'] = () => {
        // 1、获取图片的数据
        let mat = cv.imread(imgElement);
        let arr = []
        // 3、构建图片的二维数组
        for (let i = 0; i < mat.data.length; i = i + 4) {arr.push(mat.data.slice(i, i + 4));
        }
        for (let j = 0; j < arr.length; j = j + imageWidth) {imageArr.push(arr.slice(j, j + imageWidth));
        }
        cv.imshow('canvasOutput', mat);
        mat.delete(); // 避免内存透露};
    }
    // 4、获取到 Canvas 点击的坐标
    canvasElement.addEventListener('click', (e) => {let clickPoint = getMousePos(canvasElement, e)
      let seedMark = myFloodFill(imageArr, 10, clickPoint) // 默认 20-30 左右,动静输出
    }, false);

    function getMousePos(canvas, event) {var rect = canvas.getBoundingClientRect();// 办法返回元素的大小及其绝对于视口的地位
      var x = event.clientX - rect.left * (canvas.width / rect.width);
      var y = event.clientY - rect.top * (canvas.height / rect.height);
      return [Math.round(x), Math.round(y)];
    }
    // 5、改写泛洪办法,取得返回的区域
    function myFloodFill(image, thresh, seedpoint) {
      // 构建图片的二维数组
      let seedMark = new Array(imageHeight).fill(0).map(() => new Array(imageWidth).fill(0));
      // 四邻域
      let p = 4
      let connection = [[-1, 0], [0, 1], [1, 0], [0, -1]]
      let seeds = [[seedpoint[1], seedpoint[0]]] // 竖直方向在前,程度方向在后
      let interval = thresh
      while (seeds.length > 0) {
        // 栈顶元素出栈
        // pt=(y,x),opencv 中程度为 x 坐标,竖直为 y 坐标,seeds 输出坐标为先竖直坐标,后程度坐标
        let pt = seeds.shift(0)
        let Ra = image[pt[0]][pt[1]][0]
        let Ga = image[pt[0]][pt[1]][1]
        let Ba = image[pt[0]][pt[1]][2]
        for (let i = 0; i < p; i++) {let x = pt[1] + connection[i][0]
          let y = pt[0] + connection[i][1]
          // 检测边界点
          if (x < 0 || x >= imageWidth || y < 0 || y >= imageHeight) {continue}
          let Rb = image[y][x][0]
          let Gb = image[y][x][1]
          let Bb = image[y][x][2]
          // 满足魔法点的条件
          if (seedMark[y][x] == 0 && ((Ra - Rb) <= interval) && (Ga - Gb) <= interval && (Ba - Bb) <= interval) {
            // 将魔法点标记为已拜访
            seedMark[y][x] = 1
            // 将魔法点压入栈
            seeds.push([y, x])
          }
        }
      }
      return seedMark
    }
    // todo
    // 6、显示图像的遮层
  </script>

  <script async src="https://docs.opencv.org/4.5.0/opencv.js" onload="onOpenCvReady();" type="text/javascript"></script>
</body>

</html>

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