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import torch |
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from flask import Flask, request |
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import torch.nn as nn |
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import pickle |
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import jieba |
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app = Flask(__name__) |
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# 初始化模型 |
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result = "" |
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class Model(nn.Module): |
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def __init__(self): |
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super(Model, self).__init__() |
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self.embedding = nn.Embedding(10002, 300, padding_idx=10001) |
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self.lstm = nn.LSTM(300, 128, 2, bidirectional=True, batch_first=True, dropout=0.5) |
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self.fc = nn.Linear(128 * 2, 2) |
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def forward(self, x): |
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x, _ = x |
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out = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300] |
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out, _ = self.lstm(out) |
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out = self.fc(out[:, -1, :]) # 句子最后时刻的 hidden state |
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return out |
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model = Model() |
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model.load_state_dict(torch.load('./THUCNews/saved_dict/TextRNN.ckpt', map_location=torch.device("cpu"))) |
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model.eval() |
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stopwords = open('./THUCNews/data/hit_stopwords.txt', encoding='utf8').read().split('\n')[:-1] |
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vocab = pickle.load(open('./THUCNews/data/vocab.pkl', 'rb')) |
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classes = ['negative', 'positive'] |
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s = '空调吵，住在电梯旁，电梯门口放垃圾箱，极臭，布草间没关门，也臭，臭到房间里，门下塞毛巾也挡不住臭味，开窗外面吵，关窗空调吵，楼下早餐桌子上摆满垃圾没人整理，不能再差的体验了' |
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# s = '东东还算不错。重装系统时，网上查不到怎么修改BIOS，才能安装？问题请帮忙解决！' |
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@app.route('/api/content', methods=["POST"]) |
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def content(): |
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get_json = request.get_json() |
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global model |
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global result |
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global classes |
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result = "" |
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s = get_json.get("content")[0] |
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print(6777, s) |
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try: |
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s = list(jieba.lcut(s)) |
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s = [i for i in s if i not in stopwords] |
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s = [vocab.get(i, 10000) for i in s] |
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if len(s) > 64: |
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s = s[:64] |
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else: |
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for i in range(64 - len(s)): |
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s.append(vocab['<PAD>']) |
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outputs = model((torch.LongTensor(s).unsqueeze(0), None)) |
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print(torch.argmax(outputs)) |
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result = classes[torch.argmax(outputs)] |
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except Exception as e: # 未捕获到异常，程序直接报错 |
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result = e |
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return "pridicting" |
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@app.route('/api/model_res', methods=['GET']) |
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def model_res(): |
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global result |
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return str(result) |
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if __name__ == '__main__': |
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app.run(host="127.0.0.1", port=8006) |
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# Logs |
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logs |
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*.log |
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npm-debug.log* |
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yarn-debug.log* |
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yarn-error.log* |
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pnpm-debug.log* |
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lerna-debug.log* |
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node_modules |
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dist |
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dist-ssr |
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*.local |
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# Editor directories and files |
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.vscode/* |
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!.vscode/extensions.json |
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.idea |
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.DS_Store |
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*.suo |
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*.ntvs* |
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*.njsproj |
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*.sln |
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*.sw? |
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# Vue 3 + Vite |
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|
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This template should help get you started developing with Vue 3 in Vite. The template uses Vue 3 `<script setup>` SFCs, check out the [script setup docs](https://v3.vuejs.org/api/sfc-script-setup.html#sfc-script-setup) to learn more. |
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## Recommended IDE Setup |
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- [VS Code](https://code.visualstudio.com/) + [Vue - Official](https://marketplace.visualstudio.com/items?itemName=Vue.volar) (previously Volar) and disable Vetur |
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<!DOCTYPE html> |
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<html lang="zh-CN"> |
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<head> |
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<meta charset="UTF-8" /> |
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<link rel="icon" type="image/svg+xml" href="/vite.svg" /> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /> |
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<title>毛囊结果检测</title> |
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</head> |
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<body> |
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<div id="app"></div> |
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<script type="module" src="/src/main.js"></script> |
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</body> |
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</html> |
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{ |
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"name": "vitebase", |
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"private": true, |
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"version": "0.0.0", |
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"type": "module", |
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"scripts": { |
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"dev": "vite", |
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"build": "vite build", |
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"preview": "vite preview" |
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}, |
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"dependencies": { |
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"axios": "^1.6.8", |
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"element-plus": "^2.7.2", |
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"lodash": "^4.17.21", |
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"vue": "^3.4.21" |
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}, |
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"devDependencies": { |
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"@vitejs/plugin-vue": "^5.0.4", |
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"unplugin-auto-import": "^0.17.5", |
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"unplugin-vue-components": "^0.26.0", |
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"vite": "^5.2.0" |
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} |
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} |
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After Width: | Height: | Size: 87 KiB |
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<template> |
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<div class="bgimg"> |
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<el-row class="pa-2"> |
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<el-col :span="8" class="text-align"> |
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<div> |
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<el-input class="opacitybg" v-model="content" :rows="15" type="textarea" placeholder="请输入文本进行预测" /> |
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</div> |
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<div class="pa-2"> |
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<el-button type="success" @click="sendData" :disabled="loading" :loading="loading"> |
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开始预测 |
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</el-button> |
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</div> |
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</el-col> |
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<el-col :span="16" class="sendButton text-align"> |
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<div v-if="result"> |
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<el-tag type="success" v-if="result === 'positive'" class="tagClass"> |
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预测结果：{{ classZh[result] }} |
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<el-icon style="width: 48px;font-size: 1.6em;position: relative;top: 20px;"> |
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"> |
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<path |
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d="M12,2C6.47,2 2,6.47 2,12C2,17.53 6.47,22 12,22A10,10 0 0,0 22,12C22,6.47 17.5,2 12,2M12,20A8,8 0 0,1 4,12A8,8 0 0,1 12,4A8,8 0 0,1 20,12A8,8 0 0,1 12,20M13,9.94L14.06,11L15.12,9.94L16.18,11L17.24,9.94L15.12,7.82L13,9.94M8.88,9.94L9.94,11L11,9.94L8.88,7.82L6.76,9.94L7.82,11L8.88,9.94M12,17.5C14.33,17.5 16.31,16.04 17.11,14H6.89C7.69,16.04 9.67,17.5 12,17.5Z" /> |
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</svg> |
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</el-icon> |
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</el-tag> |
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<el-tag type="danger" v-else-if="result === 'negative'" class="tagClass"> |
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预测结果：{{ classZh[result] }} |
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<el-icon style="width: 48px;font-size: 1.6em;position: relative;top: 20px;"> |
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"> |
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<path |
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d="M20,12A8,8 0 0,0 12,4A8,8 0 0,0 4,12A8,8 0 0,0 12,20A8,8 0 0,0 20,12M22,12A10,10 0 0,1 12,22A10,10 0 0,1 2,12A10,10 0 0,1 12,2A10,10 0 0,1 22,12M15.5,8C16.3,8 17,8.7 17,9.5C17,10.3 16.3,11 15.5,11C14.7,11 14,10.3 14,9.5C14,8.7 14.7,8 15.5,8M10,9.5C10,10.3 9.3,11 8.5,11C7.7,11 7,10.3 7,9.5C7,8.7 7.7,8 8.5,8C9.3,8 10,8.7 10,9.5M12,14C13.75,14 15.29,14.72 16.19,15.81L14.77,17.23C14.32,16.5 13.25,16 12,16C10.75,16 9.68,16.5 9.23,17.23L7.81,15.81C8.71,14.72 10.25,14 12,14Z" /> |
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</svg> |
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</el-icon> |
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</el-tag> |
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</div> |
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<div v-else> |
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<el-progress class="opacitybg" :percentage="percentage" :stroke-width="15" :striped="percentage !== 100" |
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:striped-flow="percentage !== 100" :duration="duration" :status="percentage === 100 ? 'success' : ''" |
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type="circle" /> |
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</div> |
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</el-col> |
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</el-row> |
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<el-row> |
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<el-col class="pa-2 text-align" :span="8" v-for="(item, index) in imgList" :index="index"> |
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<div class="pa-2">{{ item.label }}</div> |
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<div> |
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<el-image style="height: calc(100vh - 420px)" :src="item.imgPath" :zoom-rate="1.2" :max-scale="7" |
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:min-scale="0.2" :preview-src-list="[item.imgPath]" :initial-index="4" fit="cover" close-on-press-escape /> |
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</div> |
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</el-col> |
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</el-row> |
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</div> |
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</template> |
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<script> |
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import { result } from "lodash"; |
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import imgPath from "./assets/config/imgconf.json"; |
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export default { |
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name: "app", |
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data() { |
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return { |
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result: "", |
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content: "", |
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percentage: 0, |
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isSend: false, |
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loading: false, |
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imgList: [], |
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sendmessage: null, |
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classZh: { |
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negative: "消极", |
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positive: "积极", |
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}, |
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}; |
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}, |
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methods: { |
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async sendData() { |
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this.percentage = 0; |
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this.isSend = true; |
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this.loading = true; |
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this.result = ""; |
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if (this.sendmessage) { |
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this.sendmessage.close() |
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} |
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this.sendmessage = this.$message({ |
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showClose: true, |
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repeatNum: 1, |
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duration: 0, |
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message: "王一鸣202207100038", |
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type: 'success', |
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}); |
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let data = { |
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content: [this.content], |
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}; |
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let res = await this.$axios.post("/api/content", data); |
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if (res.status === 200) { |
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this.isSend = false; |
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} |
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}, |
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}, |
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async mounted() { |
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this.imgList = imgPath; |
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for (let index = 0; index < this.imgList.length; index++) { |
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let element = this.imgList[index]; |
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element.imgPath = new URL(element.path, import.meta.url).href; |
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} |
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}, |
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watch: { |
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async isSend(nev, olv) { |
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let total = 96; |
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if (nev) { |
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while (this.percentage < total) { |
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if (this.percentage < total) { |
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let random = Math.floor(Math.random() * 2); |
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this.percentage += random; |
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} |
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await new Promise((resolve) => setTimeout(resolve, 50)); |
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} |
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if (this.percentage > total - 1) { |
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while (!this.result) { |
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let res = await this.$axios.get("/api/model_res"); |
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this.percentage = 100; |
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this.result = res.data |
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await new Promise((resolve) => setTimeout(resolve, 1000)); |
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} |
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} |
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} else { |
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} |
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}, |
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result(nev, olv) { |
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this.loading = !nev; |
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}, |
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}, |
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computed: { |
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duration() { |
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return Math.floor(this.percentage / 10); |
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}, |
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}, |
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}; |
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</script> |
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<style scoped> |
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.bgimg { |
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height: 100vh; |
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width: 100vw; |
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color: #fff; |
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background-image: url("@/assets/imgs/otherimgs/bg.jpg"); |
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} |
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.sendButton { |
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align-content: center; |
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} |
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.text-align { |
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text-align: center; |
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} |
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.pa-2 { |
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padding: 4px; |
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} |
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.tagClass { |
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height: 1.6em; |
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font-size: 3em; |
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} |
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.opacitybg { |
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opacity: 0.6; |
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} |
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</style> |
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[ |
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{ |
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"label": "f1-score", |
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"path": "assets/imgs/bilstm/1.png" |
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} |
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] |
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After Width: | Height: | Size: 19 KiB |
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After Width: | Height: | Size: 383 KiB |
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After Width: | Height: | Size: 87 KiB |
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// main.ts
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import { createApp } from 'vue' |
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import ElementPlus from 'element-plus' |
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import 'element-plus/dist/index.css' |
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import App from './App.vue' |
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import './style.css' |
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import _ from 'lodash' |
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import axios from "axios" |
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const app = createApp(App) |
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app.config.globalProperties.$_ = _ |
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app.config.globalProperties.$axios = axios |
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app.use(ElementPlus) |
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app.mount('#app') |
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html, |
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body { |
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margin: 0; |
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padding: 0; |
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} |
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// vite.config.ts
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import { defineConfig } from 'vite' |
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import vue from '@vitejs/plugin-vue' |
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import { resolve } from 'path'; |
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|
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// https://vitejs.dev/config/
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export default defineConfig({ |
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plugins: [vue()], |
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server: { |
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proxy: { |
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'/api': { |
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target: 'http://127.0.0.1:8006/api', |
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changeOrigin: true, |
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rewrite: (path) => path.replace(/^\/api/, '') |
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} |
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} |
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}, |
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resolve: { |
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alias: { |
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// @ 替代为 src
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'@': resolve(__dirname, 'src'), |
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}, |
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}, |
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}) |
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项目启用： |
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后端启用： |
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使用pycharm打开项目文件夹，打开app.py(需要将缺失的包安装：例如flask、numpy等)，在app.py中运行即可 |
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前端启用： |
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使用vscode打开前端项目文件夹，在确保有node环境(百度搜索node，下载并安装18.20.0版本)后在终端（ctrl+shift+`）输入npm run dev，即可启用前端项目，如果缺失运行包的情况下，需要先npm install安装项目运行包。 |
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项目前端开发流程： |
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1.安装node18.20.0版本，安装VScode最新版本， |
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2.打算使用vite(打包工具)+vue(前端框架)进行开发 |
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3.在vscode终端中输入npm init vite@latest my-vue-app -- --template vue 搭建vue项目框架， |
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4.cd my-vue-app进入项目中,npm install安装项目运行包。 |
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5.在终端输入npm run dev，即可启动项目， |
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6.使用axios进行请求，与后端进行数据交互。 |
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7.使用element-plus进行前端页面开发。 |
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8.使用lodash进行数据处理。加快开发。 |
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9.开发的页面均在app.vue文件中。 |
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前端页面开发的思想： |
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1.需要一个输入框和按钮发送请求（在发送后未出结果前，按钮不能再次点击，防止请求次数过多，造成资源浪费），以及后端数据处理过程中，页面上需要给出提示，及时反馈给用户 |
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2.需要需要把训练的bilstm模型的参数放到页面上，便于用户查看，便于用户理解 |
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3.在后端数据处理完以后，请求处理后的数据，在页面上显示出来 |
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import cv2 |
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import torch |
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import numpy as np |
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from PIL import Image |
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from ultralytics import YOLO |
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# 3个输入参数 |
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img_path = 'img0002.jpg' |
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iou = 0.1 |
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conf = 0.25 |
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def split_image(img_path, size=(800, 800)): |
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img = cv2.imread(img_path) |
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height, width = img.shape[:2] |
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rows = (height + size[1] - 1) // size[1] |
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cols = (width + size[0] - 1) // size[0] |
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img_list = [] |
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indexes = [] |
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for r in range(rows): |
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for c in range(cols): |
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y1 = r * size[1] |
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y2 = min((r + 1) * size[1], height) |
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x1 = c * size[0] |
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x2 = min((c + 1) * size[0], width) |
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split = img[y1:y2, x1:x2] |
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img_list.append(split) |
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indexes.append((r, c)) |
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return img_list, indexes, (height, width) |
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def combine_images(pred_imgs, indexes, size=(800, 800), img_shape=(3000, 4000)): |
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combined_img = np.zeros((img_shape[0], img_shape[1], 3), dtype=np.uint8) |
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for idx, (r, c) in enumerate(indexes): |
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y1 = r * size[1] |
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y2 = min((r + 1) * size[1], img_shape[0]) |
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x1 = c * size[0] |
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x2 = min((c + 1) * size[0], img_shape[1]) |
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combined_img[y1:y2, x1:x2] = pred_imgs[idx][:y2 - y1, :x2 - x1] |
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return combined_img |
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follicle_groups_detector = YOLO('follicle_groups.pt') |
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follicles_detector = YOLO('follicles.pt') |
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results = follicle_groups_detector(img_path, iou=iou, conf=conf) |
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for r in results: |
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num_follicle_groups = len(r.boxes) |
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im_array = r.plot() |
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im = Image.fromarray(im_array[..., ::-1]) |
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im.save('results_1.jpg') #输出结果图1 |
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img_list, indexes, (height, width) = split_image(img_path) |
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print(f"Number of image blocks: {len(img_list)}") |
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num_small_follicles = 0 |
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num_big_follicles = 0 |
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pred_imgs = [] |
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for img in img_list: |
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results = follicles_detector(img, iou=iou, conf=conf) |
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for r in results: |
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num_small_follicles += torch.sum(r.boxes.cls == 0).item() |
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num_big_follicles += torch.sum(r.boxes.cls == 1).item() |
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im_array = r.plot() |
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pred_imgs.append(im_array) |
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# 输出的3个结果文本 |
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print('毛囊群数量：', num_follicle_groups) |
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print('大毛囊数量：', num_big_follicles) |
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print('小毛囊数量：', num_small_follicles) |
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combined_img = combine_images(pred_imgs, indexes, size=(800, 800), img_shape=(height, width)) |
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combined_image_pil = Image.fromarray(combined_img[..., ::-1]) |
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combined_image_pil.save('results_2.jpg') #输出结果图2 |
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After Width: | Height: | Size: 481 KiB |
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After Width: | Height: | Size: 723 KiB |
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Reference in new issue