微信小程序调用yolo目标检测模型

目录

后端

前端微信小程序

完整代码

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后端

利用Flask，调用目标检测模型，后端代码如下。

# flask_yolo.py 
from flask import Flask, request, jsonify
from ultralytics import YOLO
from PIL import Imageapp = Flask(__name__)
model_path = 'best.pt'
model = YOLO(model_path)  # 加载模型@app.route("/predict", methods=["POST"])
def predict():file = request.files["image"]image = Image.open(file.stream)results = model(image)boxes = []for box in results[0].boxes:xyxy = [int(x) for x in box.xyxy[0].tolist()]conf = round(float(box.conf[0]), 3)cls = int(box.cls[0])name = results[0].names[cls]boxes.append({'coor': xyxy,'conf': conf,'class': name})# print(boxes)return jsonify(boxes)if __name__ == "__main__":app.run(host="0.0.0.0", port=5005)

启动后端服务：

$ python flask_yolo.py * Serving Flask app 'flask_yolo'* Debug mode: off* Running on all addresses (0.0.0.0)* Running on http://127.0.0.1:5005* Running on http://x.x.x.x:5005
Press CTRL+C to quit

前端微信小程序

调用服务的js关键代码：

  // 上传图片到后端调用服务uploadImage(filePath) {this.setData({ loading: true });wx.uploadFile({url: 'http://****/predict',  //your flask apifilePath: filePath,name: 'image',success: (res) => {const detectionResult = JSON.parse(res.data);this.setData({detectionResult,loading: false});// 计算检测框位置if (this.data.imageInfo && this.data.originalImageSize) {this.updateDetectionBoxes();}},fail: (error) => {console.error('上传失败：', error);wx.showToast({title: '上传失败',icon: 'error'});this.setData({ loading: false });}});}

效果

       

完整代码

https://github.com/crystal0913/miniprogram_yolo

如需在手机上预览，需在小程序项目根目录下的project.config.json中填上你的appid。