research: Add analysis
This commit is contained in:
Fam Zheng
parent
35dfd23069
commit
8cf2a8a4a0
23
research/fetch
Executable file
23
research/fetch
Executable file
@ -0,0 +1,23 @@
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#!/usr/bin/env python3
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import argparse
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import subprocess
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--date", "-d", type=str)
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return parser.parse_args()
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def main():
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args = parse_args()
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remote_dir = f'/data/emblem-research/camera-frame'
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if args.date:
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remote_dir += f'/{args.date}'
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# we cannot rsync to data/json because we will update there after sync, and
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# the next sync will overwrite the updated files
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local_dir = f'data/raw/camera-frame'
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cmd = ['rsync', '-avz', f'zy:{remote_dir}', local_dir]
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subprocess.run(cmd)
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if __name__ == "__main__":
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main()
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@ -6,6 +6,13 @@
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<button @click="selectNone">Select None</button>
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<button @click="selectInverse">Inverse Selection</button>
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<button @click="deleteSelected" class="delete-button">Delete Selected</button>
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<button
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@click="toggleSelectionMode"
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:class="{ active: selectionMode }"
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class="mode-button">
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{{ selectionMode ? 'Selection Mode: ON' : 'Selection Mode: OFF' }}
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</button>
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<button @click="analyzeImages()">Analyze All</button>
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<input v-model="targetFolder" placeholder="Target folder name">
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<button @click="copyToFolder">Copy to Folder</button>
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<div class="size-control">
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@ -20,12 +27,19 @@
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</div>
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</div>
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<div class="datapoints">
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<div class="datapoint" v-for="datapoint in datapoints" :key="datapoint.path">
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<div class="datapoint" v-for="dp in datapoints" :key="dp.path">
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<div class="datapoint-container">
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<input type="checkbox" v-model="selectedDatapoints[datapoint.path]">
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<router-link target="_blank" :to="`/datapoint/${folder}/${datapoint.basename}`" @click.prevent="showModal(datapoint)">
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<img :title="datapoint_title(datapoint)" class="datapoint-image" :src="datapoint.image_data_url">
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<span class="datapoint-datetime">{{ datapoint.datetime }}</span>
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<input type="checkbox" v-model="selectedDatapoints[dp.path]">
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<router-link
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target="_blank"
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:to="selectionMode ? null : `/datapoint/${folder}/${dp.basename}`"
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@click.prevent="handleImageClick(dp)">
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<img :title="datapoint_title(dp)" class="datapoint-image" :src="dp.image_data_url">
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<img v-if="dp.dot_area" class="datapoint-dot-area" :src="dp.dot_area">
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<div v-if="dp.analyze_result" class="analyze-result">
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{{ dp.analyze_result }}
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</div>
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<span class="datapoint-datetime">{{ dp.datetime }}</span>
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</router-link>
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</div>
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</div>
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@ -34,6 +48,7 @@
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<!-- Add modal component -->
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<div v-if="selectedImage" class="modal" @click="closeModal">
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<img :src="selectedImage.image_data_url" class="modal-image">
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<div class="analyze-result" v-if="selectedImage.analyze_result"> {{ selectedImage.analyze_result }}</div>
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</div>
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</div>
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</template>
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@ -50,6 +65,9 @@ export default {
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targetFolder: '',
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imageSize: parseInt(localStorage.getItem('imageSize')) || 200,
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selectedImage: null,
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selectionMode: false,
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isAnalyzing: false,
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showDropdown: false,
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}
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},
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watch: {
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@ -140,6 +158,44 @@ export default {
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closeModal() {
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this.selectedImage = null;
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},
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toggleSelectionMode() {
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this.selectionMode = !this.selectionMode;
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},
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handleImageClick(datapoint) {
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if (this.selectionMode) {
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this.selectedDatapoints[datapoint.path] = !this.selectedDatapoints[datapoint.path];
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} else {
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this.showModal(datapoint);
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}
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},
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toggleDropdown() {
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if (!this.isAnalyzing) {
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this.showDropdown = !this.showDropdown;
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}
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},
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async analyzeImages() {
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this.showDropdown = false;
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this.isAnalyzing = true;
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try {
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for (var i in this.datapoints) {
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const datapoint = this.datapoints[i];
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const response = await fetch('/api/analyze', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify(datapoint),
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});
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const result = await response.json();
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this.datapoints[i] = result;
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}
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} catch (error) {
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alert(`Error analyzing image: ${error}`);
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} finally {
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this.isAnalyzing = false;
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}
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},
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},
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}
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</script>
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@ -151,6 +207,8 @@ export default {
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}
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.datapoint-container {
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border: 1px solid green;
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padding: 5px;
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position: relative;
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}
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@ -227,4 +285,64 @@ div.datapoints img {
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max-height: 90vh;
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object-fit: contain;
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}
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.mode-button {
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background-color: #666;
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color: white;
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}
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.mode-button.active {
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background-color: #4CAF50;
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}
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.dropdown {
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position: relative;
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display: inline-block;
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}
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.dropdown-button {
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background-color: #2196F3;
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color: white;
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}
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.dropdown-button:disabled {
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background-color: #90CAF9;
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cursor: not-allowed;
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}
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.dropdown-content {
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position: absolute;
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background-color: #f9f9f9;
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min-width: 160px;
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box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
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z-index: 1;
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border-radius: 4px;
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}
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.dropdown-content a {
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color: black;
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padding: 12px 16px;
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text-decoration: none;
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display: block;
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}
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.dropdown-content a:hover {
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background-color: #f1f1f1;
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}
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.qr-info {
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bottom: 5px;
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right: 5px;
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background-color: rgba(0, 0, 0, 0.7);
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color: white;
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padding: 1rem;
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border-radius: 3px;
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font-size: 14px;
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width: 200px;
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text-wrap: wrap;
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}
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.qr-info > div {
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margin: 2px 0;
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}
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</style>
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@ -3,7 +3,7 @@
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<h1>Folders</h1>
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<ul>
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<li v-for="folder in folders" :key="folder.folder">
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<router-link :to="`/folder/${folder.folder}`">{{ folder.folder }}</router-link>
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<router-link :to="`/folder/${folder.folder}`">{{ folder.folder }} ({{ folder.count }})</router-link>
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</li>
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</ul>
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</div>
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@ -1,4 +1,5 @@
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#!/usr/bin/env python3
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import subprocess
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from flask import Flask, request, jsonify, send_file
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from datetime import datetime
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import os
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@ -7,6 +8,9 @@ import uuid
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import shutil
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import base64
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from io import BytesIO
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import tempfile
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import cv2
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import numpy as np
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app = Flask(__name__)
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@ -17,7 +21,7 @@ def image_data_url_to_binary(image_data_url):
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if image_data_url.startswith('data:image/jpeg;base64,'):
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return base64.b64decode(image_data_url.split(',')[1]), 'image/jpeg'
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elif image_data_url.startswith('data:image/png;base64,'):
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return base64.b64decode(image_data_url.split(',')[1]), 'png'
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return base64.b64decode(image_data_url.split(',')[1]), 'image/png'
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else:
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raise ValueError(f"Unsupported image data URL: {image_data_url}")
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@ -58,6 +62,42 @@ def folders():
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"folders": ret,
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}
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@app.route("/api/qrtool", methods=["POST"])
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def qrtool():
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data = request.get_json()
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image_data_url = data.get("image_data_url")
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binary, mimetype = image_data_url_to_binary(image_data_url)
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if mimetype == 'image/png':
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suffix = ".png"
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elif mimetype == 'image/jpeg':
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suffix = ".jpg"
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else:
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raise ValueError(f"Unsupported image type: {mimetype}")
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:
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f.write(binary)
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qrtool = os.path.abspath("../alg/qrtool")
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if not os.path.exists(qrtool):
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return {
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"ok": False,
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"error": f"qrtool not found: {qrtool}",
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}
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cmd = ["./qrtool", data['cmd'], f.name]
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print(cmd)
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try:
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sp = subprocess.Popen(cmd, cwd=os.path.dirname(qrtool), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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stdout, stderr = sp.communicate()
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return {
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"exit_code": sp.returncode,
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"stdout": stdout.decode('utf-8'),
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"stderr": stderr.decode('utf-8'),
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}
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except Exception as e:
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return {
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"ok": False,
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"error": str(e),
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}
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@app.route('/api/datapoints')
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def datapoints():
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folder = request.args.get('folder')
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@ -111,7 +151,7 @@ def do_copy_to_folder(paths, target_folder):
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dst = os.path.join(DATA_DIR, "json", target_folder)
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os.makedirs(dst, exist_ok=True)
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print(f"Copying {src} to {dst}")
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shutil.copy(src, dst)
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shutil.copy2(src, dst)
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@app.route('/api/copy-to-folder', methods=['POST'])
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def copy_to_folder():
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@ -124,5 +164,70 @@ def copy_to_folder():
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"message": "Datapoints copied to folder",
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}
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def do_qrtool(image, cmd):
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with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as f:
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image.save(f.name)
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qrtool = os.path.abspath("../alg/qrtool")
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cmd = ["./qrtool", cmd, f.name]
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print(cmd)
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sp = subprocess.Popen(cmd, cwd=os.path.dirname(qrtool), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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stdout, stderr = sp.communicate()
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return stdout.decode('utf-8')
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def calc_sharpness(image_data_url):
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image = image_data_url_to_image(image_data_url)
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# 转换为灰度图像
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 高斯滤波
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# 计算Sobel梯度
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sobelx = cv2.Sobel(blurred, cv2.CV_64F, 1, 0, ksize=3)
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sobely = cv2.Sobel(blurred, cv2.CV_64F, 0, 1, ksize=3)
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# 计算梯度幅值
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magnitude = np.sqrt(sobelx**2 + sobely**2)
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# 计算清晰度
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sharpness = np.mean(magnitude) / (image.shape[0] * image.shape[1]) * 1000
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return sharpness
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def image_data_url_to_image(image_data_url):
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binary, mimetype = image_data_url_to_binary(image_data_url)
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return cv2.imdecode(np.frombuffer(binary, np.uint8), cv2.IMREAD_COLOR)
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def get_image_data_url(path):
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mimetype = "image/jpeg"
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if path.endswith(".png"):
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mimetype = "image/png"
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with open(path, "rb") as f:
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binary = f.read()
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return "data:" + mimetype + ";base64," + base64.b64encode(binary).decode('utf-8')
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def get_image_dot_area(image):
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with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as f:
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cv2.imwrite(f.name, image)
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qrtool = os.path.abspath("../alg/qrtool")
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cmd = ["./qrtool", "dot", f.name]
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if 0 == subprocess.call(cmd, cwd=os.path.dirname(qrtool)):
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return get_image_data_url(f.name + ".dot.jpg")
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def analyze_datapoint(data):
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image = image_data_url_to_image(data['image_data_url'])
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# data["clarity"] = do_qrtool(image, "clarity").split()[-1]
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# data["analyze_result"] = data
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data["analyze_result"] = {}
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data["dot_area"] = get_image_dot_area(image)
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if data["dot_area"]:
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data["analyze_result"]["dot_area_sharpness"] = calc_sharpness(data["dot_area"])
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return data
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@app.route('/api/analyze', methods=['POST'])
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def analyze():
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data = request.get_json()
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analyze_datapoint(data)
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return data
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=26966, debug=True)
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