diff --git a/fe/src/components/CV/point.vue b/fe/src/components/CV/point.vue
index f40bc8c..259159c 100644
--- a/fe/src/components/CV/point.vue
+++ b/fe/src/components/CV/point.vue
@@ -116,7 +116,7 @@ export default ({
   user-select: none;
 }
 .point-box {
-  width: 300px;
+  width: 400px;
   position: absolute;
   font-size: 0;
 }
diff --git a/fe/src/components/CV/seg.vue b/fe/src/components/CV/seg.vue
new file mode 100644
index 0000000..30991b2
--- /dev/null
+++ b/fe/src/components/CV/seg.vue
@@ -0,0 +1,339 @@
+<template>
+  <div class="anno-img-box">
+    <div class="seg-box">
+      <img class="anno-img" id="anno-img" @click="addSeg($event)" :src="`data:image/jpeg;base64,${fileContent}`" @dragstart="$event.preventDefault()" @contextmenu="$event.preventDefault()">
+      <div v-for="annoDetail in annoDetails" :seg="annoDetail" :key="`${annoDetail.segs[0][0]}_${annoDetail.segs[0][1]}`" :style="{
+        left: annoDetail.segs[0][0]*100 + '%',
+        top: annoDetail.segs[0][1]*100 + '%',
+        backgroundColor: types[annoDetail.type]?types[annoDetail.type].color:'#f00',
+      }" class="seg" @contextmenu="$event.preventDefault();delSeg(annoDetail, true)" @touchstart="$event.preventDefault();delSeg(annoDetail, false)" @mouseover="overSeg($event, annoDetail)"></div>
+    </div>
+  </div>
+</template>
+
+<script>
+// import npyjs from 'npyjs'
+import { InferenceSession, Tensor } from 'onnxruntime-web'
+
+const ort = require('onnxruntime-web')
+
+console.log(ort)
+// console.log(npyjs)
+
+// const IMAGE_PATH = '/assets/data/dogs.jpg'
+const IMAGE_EMBEDDING = '/assets/data/dogs_embedding.npy'
+// const MODEL_DIR = '/assets/data/sam_onnx_quantized_example.onnx'
+const MODEL_DIR = './sam_onnx_quantized_example.onnx'
+let model = null
+async function getModel () {
+  console.log(InferenceSession, MODEL_DIR)
+  model = await InferenceSession.create(MODEL_DIR)
+  console.log('model', model)
+  // go()
+}
+getModel()
+
+// Use a Canvas element to produce an image from ImageData
+function imageDataToImage (imageData) {
+  const canvas = imageDataToCanvas(imageData)
+  const image = new Image()
+  image.src = canvas.toDataURL()
+  return image
+}
+
+// Convert the onnx model mask prediction to ImageData
+function arrayToImageData (input, width, height) {
+  const [r, g, b, a] = [0, 114, 189, 255] // the masks's blue color
+  const arr = new Uint8ClampedArray(4 * width * height).fill(0)
+  for (let i = 0; i < input.length; i++) {
+    // Threshold the onnx model mask prediction at 0.0
+    // This is equivalent to thresholding the mask using predictor.model.mask_threshold
+    // in python
+    if (input[i] > 0.0) {
+      arr[4 * i + 0] = r
+      arr[4 * i + 1] = g
+      arr[4 * i + 2] = b
+      arr[4 * i + 3] = a
+    }
+  }
+  return new ImageData(arr, height, width)
+}
+
+// Canvas elements can be created from ImageData
+function imageDataToCanvas (imageData) {
+  const canvas = document.createElement('canvas')
+  const ctx = canvas.getContext('2d')
+  canvas.width = imageData.width
+  canvas.height = imageData.height
+  ctx.putImageData(imageData, 0, 0)
+  return canvas
+}
+
+// Convert the onnx model mask output to an HTMLImageElement
+export function onnxMaskToImage (input, width, height) {
+  return imageDataToImage(arrayToImageData(input, width, height))
+}
+// async function go () {
+//   let n = 0
+//   const pointCoords = new Float32Array(2 * (n + 1))
+//   const pointLabels = new Float32Array(n + 1)
+
+//   // Add in the extra point/label when only clicks and no box
+//   // The extra point is at (0, 0) with label -1
+//   pointCoords[2 * n] = 0.0
+//   pointCoords[2 * n + 1] = 0.0
+//   pointLabels[n] = -1.0
+
+//   const feeds = {
+//     image_embeddings: new Tensor('float32', [
+//       256,
+//       64,
+//       64
+//     ]),
+//     point_coords: new Tensor('float32', pointCoords, [1, n + 1, 2]),
+//     point_labels: new Tensor('float32', pointLabels, [1, n + 1]),
+//     orig_im_size: new Tensor('float32', [
+//       256,
+//       256
+//     ]),
+//     mask_input: new Tensor(
+//       'float32',
+//       new Float32Array(256 * 256),
+//       [1, 1, 256, 256]
+//     ),
+//     has_mask_input: new Tensor('float32', [0])
+//   }
+//   const results = await model.run(feeds)
+//   const output = results[model.outputNames[0]]
+//   // The predicted mask returned from the ONNX model is an array which is
+//   // rendered as an HTML image using onnxMaskToImage() from maskUtils.tsx.
+//   let img = onnxMaskToImage(output.data, output.dims[2], output.dims[3])
+//   console.log(img)
+// }
+
+// Decode a Numpy file into a tensor.
+const loadNpyTensor = async (tensorFile, dType) => {
+  // let npLoader = new npyjs()
+  // const npArray = await npLoader.load(tensorFile)
+  // const tensor = new ort.Tensor(dType, npArray.data, npArray.shape)
+  // return tensor
+}
+// Load the Segment Anything pre-computed embedding
+Promise.resolve(loadNpyTensor(IMAGE_EMBEDDING, 'float32')).then(
+  (embedding) => console.log(embedding)
+)
+
+// Run the ONNX model every time clicks has changed
+const modelData = ({ clicks, tensor, modelScale }) => {
+  const imageEmbedding = tensor
+  let pointCoords
+  let pointLabels
+  let pointCoordsTensor
+  let pointLabelsTensor
+
+  // Check there are input click prompts
+  if (clicks) {
+    let n = clicks.length
+
+    // If there is no box input, a single padding point with
+    // label -1 and coordinates (0.0, 0.0) should be concatenated
+    // so initialize the array to support (n + 1) points.
+    pointCoords = new Float32Array(2 * (n + 1))
+    pointLabels = new Float32Array(n + 1)
+
+    // Add clicks and scale to what SAM expects
+    for (let i = 0; i < n; i++) {
+      pointCoords[2 * i] = clicks[i].x * modelScale.samScale
+      pointCoords[2 * i + 1] = clicks[i].y * modelScale.samScale
+      pointLabels[i] = clicks[i].clickType
+    }
+
+    // Add in the extra point/label when only clicks and no box
+    // The extra point is at (0, 0) with label -1
+    pointCoords[2 * n] = 0.0
+    pointCoords[2 * n + 1] = 0.0
+    pointLabels[n] = -1.0
+
+    // Create the tensor
+    pointCoordsTensor = new Tensor('float32', pointCoords, [1, n + 1, 2])
+    pointLabelsTensor = new Tensor('float32', pointLabels, [1, n + 1])
+  }
+  const imageSizeTensor = new Tensor('float32', [
+    modelScale.height,
+    modelScale.width
+  ])
+
+  if (pointCoordsTensor === undefined || pointLabelsTensor === undefined) {
+    return
+  }
+
+  // There is no previous mask, so default to an empty tensor
+  const maskInput = new Tensor(
+    'float32',
+    new Float32Array(256 * 256),
+    [1, 1, 256, 256]
+  )
+  // There is no previous mask, so default to 0
+  const hasMaskInput = new Tensor('float32', [0])
+
+  return {
+    image_embeddings: imageEmbedding,
+    point_coords: pointCoordsTensor,
+    point_labels: pointLabelsTensor,
+    orig_im_size: imageSizeTensor,
+    mask_input: maskInput,
+    has_mask_input: hasMaskInput
+  }
+}
+console.log(modelData)
+
+// const runONNX = async () => {
+//   try {
+//     if (
+//       model === null ||
+//       clicks === null ||
+//       tensor === null ||
+//       modelScale === null
+//     ) {
+//       return
+//     } else {
+//       // Preapre the model input in the correct format for SAM.
+//       // The modelData function is from onnxModelAPI.tsx.
+//       const feeds = modelData({
+//         clicks,
+//         tensor,
+//         modelScale,
+//       })
+//       if (feeds === undefined) return
+//       // Run the SAM ONNX model with the feeds returned from modelData()
+//       const results = await model.run(feeds)
+//       const output = results[model.outputNames[0]]
+//       // The predicted mask returned from the ONNX model is an array which is
+//       // rendered as an HTML image using onnxMaskToImage() from maskUtils.tsx.
+//       setMaskImg(onnxMaskToImage(output.data, output.dims[2], output.dims[3]))
+//     }
+//   } catch (e) {
+//     console.log(e)
+//   }
+// }
+
+export default ({
+  name: 'CVSeg',
+  props: {
+    fileContent: {
+      type: String,
+      default: '',
+      required: true
+    },
+    annoDetails: {
+      type: Array,
+      default: () => [],
+      required: true
+    },
+    nowType: {
+      type: String,
+      default: '1234',
+      required: true
+    },
+    types: {
+      type: Object,
+      default: () => {},
+      required: true
+    },
+    save: {
+      type: Function,
+      required: true
+    }
+  },
+  data () {
+    return {
+      segs: [],
+      width: 0,
+      height: 0
+    }
+  },
+  methods: {
+    log (...args) {
+      console.log(args)
+    },
+    addSeg (ev) {
+      ev.preventDefault()
+      if (!this.nowType) {
+        alert('请先选择标注类型')
+        return
+      }
+      const tar = ev.target
+      const newSeg = [ev.offsetX / tar.offsetWidth, ev.offsetY / tar.offsetHeight]
+      // this.segs.push(newSeg)
+      // console.log(this.segs)
+      this.annoDetails.push({
+        segs: [newSeg],
+        type: this.nowType
+      })
+      this.save()
+      return false
+    },
+    delSeg (annoDetail, isForce) {
+      if (!isForce) {
+        // 非强制删除，就判定下当前类型是否一致，不一致就不删除
+        if (this.nowType !== annoDetail.type) {
+          return
+        }
+      }
+      const idx = this.annoDetails.indexOf(annoDetail)
+      console.log(annoDetail, this.segs)
+      this.annoDetails.splice(idx, 1)
+      this.save()
+    },
+    overSeg (ev, seg) {
+      if (ev.which === 3) {
+        this.delSeg(seg)
+      }
+    }
+  },
+  watch: {
+    annoDetails: {
+      handler (val) {
+        // const annoImg = document.getElementById('anno-img')
+        // annoImg.src = val
+        // console.log(annoImg.width)
+        // this.width = annoImg.width
+        // this.height = annoImg.height
+        console.log(val)
+      },
+      deep: true
+    }
+  }
+})
+</script>
+
+<style>
+.anno-img-box {
+  position: relative;
+  display: flex;
+  height: 100%;
+  align-content: center;
+  justify-content: center;
+}
+.anno-img {
+  width: 100%;
+  user-select: none;
+}
+.seg-box {
+  width: 400px;
+  position: absolute;
+  font-size: 0;
+}
+.seg {
+  position: absolute;
+  width: 8px;
+  height: 8px;
+  transform: translateX(-50%) translateY(-50%);
+  border-radius: 50%;
+  background-color: #f00;
+}
+.seg:hover {
+  width: 16px;
+  height: 16px;
+}
+</style>