14 changed files with 742 additions and 242 deletions
@ -1,75 +0,0 @@ |
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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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@ -0,0 +1,226 @@ |
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<template> |
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<div class="bgimg"> |
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<el-row class="inputClass"> |
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<el-col :span="8"> |
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<span>IoU阈值：</span> |
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<el-input-number v-model="iou" :min="0.01" :max="1" :step="0.01" :disabled="checkLoading"> |
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<template #decrease-icon> |
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<el-icon> |
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<ArrowDown /> |
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</el-icon> |
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</template> |
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<template #increase-icon> |
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<el-icon> |
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<ArrowUp /> |
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</el-icon> |
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</template> |
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</el-input-number> |
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</el-col> |
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<el-col :span="8"> |
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<span>置信度阈值：</span> |
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<el-input-number v-model="conf" :min="0.01" :max="1" :step="0.01" :disabled="checkLoading"> |
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<template #decrease-icon> |
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<el-icon> |
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<ArrowDown /> |
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</el-icon> |
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</template> |
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<template #increase-icon> |
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<el-icon> |
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<ArrowUp /> |
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</el-icon> |
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</template> |
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</el-input-number> |
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</el-col> |
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<el-col :span="4"> |
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<el-upload :show-file-list="false" :before-upload="beforeAvatarUpload" :http-request="successSubmit" |
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accept=".png,.jpg,.jpeg"> |
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<el-button type="success" :disabled="checkLoading"> |
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<span>上传图片</span> |
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</el-button> |
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</el-upload> |
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</el-col> |
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</el-row> |
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<el-row class="outClass"> |
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<el-col> |
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<el-descriptions direction="vertical" :column="3" border> |
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<template #title> |
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<span class="spanClass">检测结果：</span> |
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</template> |
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<el-descriptions-item label="毛囊群数" align="center"> |
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<template v-if="_.isEmpty(resResult)"><el-tag type="info">暂无数据</el-tag></template> |
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<template v-else> |
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<el-tag type="success">{{ resResult.num_follicle_groups }}</el-tag> |
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</template> |
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</el-descriptions-item> |
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<el-descriptions-item label="大毛囊群" align="center"> |
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<template v-if="_.isEmpty(resResult)"><el-tag type="info">暂无数据</el-tag></template> |
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<template v-else> |
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<el-tag type="success">{{ resResult.num_big_follicles }}</el-tag> |
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</template> |
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</el-descriptions-item> |
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<el-descriptions-item label="小毛囊群" align="center"> |
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<template v-if="_.isEmpty(resResult)"><el-tag type="info">暂无数据</el-tag></template> |
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<template v-else> |
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<el-tag type="success">{{ resResult.num_small_follicles }}</el-tag> |
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</template> |
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</el-descriptions-item> |
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</el-descriptions> |
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</el-col> |
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</el-row> |
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<el-row class="imgClass"> |
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<el-col :span="8"> |
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<div> |
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<template v-if="_.isEmpty(resResult)"><span class="noDataClass">暂无数据</span></template> |
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<template v-else> |
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<el-image style="width: 500px; height: 500px" |
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:src="`${baseUrl}/api/getPng/${_.split(resResult.originalImgPath, '/')[1]}`" :zoom-rate="1.2" |
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:max-scale="7" :min-scale="0.2" fit="cover" /> |
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</template> |
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</div> |
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</el-col> |
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<el-col :span="8"> |
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<div> |
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<template v-if="_.isEmpty(resResult)"><span class="noDataClass">暂无数据</span></template> |
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<template v-else> |
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<el-image style="width: 500px; height: 500px" |
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:src="`${baseUrl}/api/getPng/${_.split(resResult.result1Path, '/')[1]}`" :zoom-rate="1.2" :max-scale="7" |
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:min-scale="0.2" fit="cover" /> |
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</template> |
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</div> |
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</el-col> |
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<el-col :span="8"> |
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<div> |
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<template v-if="_.isEmpty(resResult)"><span class="noDataClass">暂无数据</span></template> |
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<template v-else> |
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<el-image style="width: 500px; height: 500px" |
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:src="`${baseUrl}/api/getPng/${_.split(resResult.result2Path, '/')[1]}`" :zoom-rate="1.2" :max-scale="7" |
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:min-scale="0.2" fit="cover" /> |
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</template> |
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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 _ from "lodash"; |
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import dayjs from "dayjs"; |
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import axios from "axios"; |
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import { ArrowDown, ArrowUp } from '@element-plus/icons-vue' |
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import NProgress from "nprogress"; |
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import "nprogress/nprogress.css"; |
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import { ElMessage } from 'element-plus' |
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export default { |
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name: "app", |
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components: { ArrowDown, ArrowUp }, |
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data() { |
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return { |
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_: _, |
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dayjs: dayjs, |
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iou: 0.1, |
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conf: 0.25, |
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checkLoading: false, |
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resResult: {}, |
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baseUrl: "http://127.0.0.1:8006", |
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}; |
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}, |
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methods: { |
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/** |
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* 上传检查图片 |
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*/ |
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beforeAvatarUpload(rawFile) { |
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let imgList = ["image/jpeg", "image/png"] |
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if (imgList.indexOf(rawFile.type) === -1) { |
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this.$msgbox.alert('请上传.png,.jpg,.jpeg格式的图片!') |
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return false |
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} else if (rawFile.size / 1024 / 1024 > 50) { |
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this.$msgbox.alert('图片文件的大小为小于50MB,过大时会处理过慢') |
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return true |
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} |
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return true |
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}, |
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//上传图片检测 |
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async successSubmit(opts) { |
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ElMessage({ |
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showClose: true, |
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duration: 0, |
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type: "success", |
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message: '正在检测，请耐心等待后，稍后查看结果', |
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}) |
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this.resResult = {} |
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NProgress.configure({ showSpinner: false }); |
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NProgress.configure({ parent: '.bgimg' }); |
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this.checkLoading = true |
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NProgress.start(); |
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NProgress.inc(); |
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let that = this |
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let file = opts.file |
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let fileReader = new FileReader() |
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fileReader.onload = async function (e) { |
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let formData = new FormData(); |
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formData.append('file', file); |
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formData.append('iou', that.iou); |
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formData.append('conf', that.conf); |
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let res = await axios.post('http://127.0.0.1:8006/api/checkPng', formData, { |
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headers: { 'content-type': 'multipart/form-data' }, |
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}) |
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that.resResult = res.data |
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that.checkLoading = false |
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ElMessage.closeAll() |
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NProgress.done(); |
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} |
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fileReader.onerror = function (error) { |
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console.error('Error reading file:', error) |
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} |
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fileReader.readAsArrayBuffer(file) |
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}, |
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//上传成功 |
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handlePictureCardPreview(file) { |
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console.log(7777, file) |
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}, |
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// 毛囊检测请求 |
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async checkPng() { |
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let res = await axios.get('http://127.0.0.1:8006/api/checkResult') |
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console.log(8977, res) |
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} |
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}, |
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async mounted() { |
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}, |
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watch: {}, |
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computed: {}, |
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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: calc(100vh - 20px); |
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width: calc(100vw - 16px); |
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padding: 12px 8px 8px 8px; |
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background-color: #ecf5ff; |
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} |
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.inputClass { |
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text-align: center; |
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margin: 60px 0 20px 0; |
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} |
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.outClass { |
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text-align: center; |
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margin: 20px 0; |
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} |
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.imgClass { |
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margin: 20px; |
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text-align: center; |
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} |
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.spanClass { |
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font-size: 3em; |
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} |
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.noDataClass { |
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font-size: 2em; |
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color: #909399; |
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} |
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</style> |
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@ -1,6 +0,0 @@ |
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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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Before Width: | Height: | Size: 19 KiB |
Binary file not shown.
@ -0,0 +1,161 @@ |
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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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from flask import Flask, request, send_file, make_response |
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from flask_cors import CORS |
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import time |
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from io import BytesIO |
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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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originalImgPath = "" |
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result1Path = "" |
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result2Path = "" |
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app = Flask(__name__) |
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# 解决跨域问题 |
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cors = CORS( |
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app, |
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resources={ |
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r"/api/*": { |
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"origins": ["http://localhost:5173"], |
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"methods": ["GET", "POST"], |
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} |
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}, |
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) |
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# 初始化模型 |
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resResult = "" |
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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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# 保存检测图片 |
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@app.route("/api/checkPng", methods=["POST"]) |
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def checkPng(): |
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global follicle_groups_detector |
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global follicles_detector |
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global originalImgPath |
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global result1Path |
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global result2Path |
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global resResult |
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pngFile = request.files["file"] |
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img = Image.open(pngFile.stream) |
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iou = request.form.get("iou") |
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iou = float(iou) |
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conf = request.form.get("conf") |
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conf = float(conf) |
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nowtime1 = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime(time.time())) |
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originalImgPath = "images/original" + str(nowtime1) + ".png" |
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result1Path = "images/result1" + str(nowtime1) + ".png" |
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result2Path = "images/result2" + str(nowtime1) + ".png" |
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img.save(originalImgPath) |
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results = follicle_groups_detector(originalImgPath, 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(result1Path) # 输出结果图1 |
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img_list, indexes, (height, width) = split_image(result1Path) |
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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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|
) |
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|
combined_image_pil = Image.fromarray(combined_img[..., ::-1]) |
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|
combined_image_pil.save(result2Path) # 输出结果图2 |
||||
|
resResult = { |
||||
|
"hasError": False, |
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|
"num_follicle_groups": num_follicle_groups, |
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|
"num_big_follicles": num_big_follicles, |
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|
"num_small_follicles": num_small_follicles, |
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|
"originalImgPath": originalImgPath, |
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|
"result1Path": result1Path, |
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|
"result2Path": result2Path, |
||||
|
} |
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|
return resResult |
||||
|
|
||||
|
|
||||
|
# 检测结果返回 |
||||
|
@app.route("/api/checkResult", methods=["GET"]) |
||||
|
def checkResult(): |
||||
|
global resResult |
||||
|
return resResult |
||||
|
|
||||
|
|
||||
|
# 图片查询 |
||||
|
@app.route("/api/getPng/<pngPath>", methods=["GET"]) |
||||
|
def getPng(pngPath): |
||||
|
# 打开或处理图像 |
||||
|
img = Image.open("images/" + pngPath) |
||||
|
# 对图像进行处理，例如调整大小 |
||||
|
img = img.resize((800, 600)) |
||||
|
# 创建一个 BytesIO 对象来保存图像数据 |
||||
|
img_byte_arr = BytesIO() |
||||
|
img.save(img_byte_arr, format="PNG") |
||||
|
img_byte_arr.seek(0) |
||||
|
|
||||
|
# 使用 send_file 返回图像数据 |
||||
|
# return send_file(img_byte_arr, mimetype='image/jpeg') |
||||
|
|
||||
|
# 或者使用 make_response 来创建一个响应对象 |
||||
|
response = make_response(img_byte_arr.getvalue()) |
||||
|
response.headers.set("Content-Type", "image/jpeg") |
||||
|
response.headers.set("Content-Disposition", "attachment", filename=pngPath) |
||||
|
|
||||
|
return response |
||||
|
|
||||
|
|
||||
|
if __name__ == "__main__": |
||||
|
app.run(host="127.0.0.1", port=8006, debug=True) |
||||
|
Before Width: | Height: | Size: 481 KiB |
|
Before Width: | Height: | Size: 723 KiB |
Loading…
Reference in new issue