基于Flask开发后端在WEB端部署YOLOv5目标检测模型.zip

# 2. YOLOv5模型训练： 训练自己的数据集可以看我这篇博客： [【小白CV】手把手教你用YOLOv5训练自己的数据集（从Windows环境配置到模型部署）](https://blog.csdn.net/weixin_44936889/article/details/110661862) 这里演示的话我就用官方训练好的 yolov5m.pt 模型。 # 3. YOLOv5模型预测： 预测接口： ```python import torch import numpy as np from models.experimental import attempt_load from utils.general import non_max_suppression, scale_coords, letterbox from utils.torch_utils import select_device import cv2 from random import randint class Detector(object): def __init__(self): self.img_size = 640 self.threshold = 0.4 self.max_frame = 160 self.init_model() def init_model(self): self.weights = 'weights/yolov5m.pt' self.device = '0' if torch.cuda.is_available() else 'cpu' self.device = select_device(self.device) model = attempt_load(self.weights, map_location=self.device) model.to(self.device).eval() model.half() # torch.save(model, 'test.pt') self.m = model self.names = model.module.names if hasattr( model, 'module') else model.names self.colors = [ (randint(0, 255), randint(0, 255), randint(0, 255)) for _ in self.names ] def preprocess(self, img): img0 = img.copy() img = letterbox(img, new_shape=self.img_size)[0] img = img[:, :, ::-1].transpose(2, 0, 1) img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() # 半精度 img /= 255.0 # 图像归一化 if img.ndimension() == 3: img = img.unsqueeze(0) return img0, img def plot_bboxes(self, image, bboxes, line_thickness=None): tl = line_thickness or round( 0.002 * (image.shape[0] + image.shape[1]) / 2) + 1 # line/font thickness for (x1, y1, x2, y2, cls_id, conf) in bboxes: color = self.colors[self.names.index(cls_id)] c1, c2 = (x1, y1), (x2, y2) cv2.rectangle(image, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize( cls_id, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(image, '{} ID-{:.2f}'.format(cls_id, conf), (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) return image def detect(self, im): im0, img = self.preprocess(im) pred = self.m(img, augment=False)[0] pred = pred.float() pred = non_max_suppression(pred, self.threshold, 0.3) pred_boxes = [] image_info = {} count = 0 for det in pred: if det is not None and len(det): det[:, :4] = scale_coords( img.shape[2:], det[:, :4], im0.shape).round() for *x, conf, cls_id in det: lbl = self.names[int(cls_id)] x1, y1 = int(x[0]), int(x[1]) x2, y2 = int(x[2]), int(x[3]) pred_boxes.append( (x1, y1, x2, y2, lbl, conf)) count += 1 key = '{}-{:02}'.format(lbl, count) image_info[key] = ['{}×{}'.format( x2-x1, y2-y1), np.round(float(conf), 3)] im = self.plot_bboxes(im, pred_boxes) return im, image_info ``` 处理完保存到服务器本地临时的目录下： ```python import os def pre_process(data_path): file_name = os.path.split(data_path)[1].split('.')[0] return data_path, file_name ``` ```python import cv2 def predict(dataset, model, ext): global img_y x = dataset[0].replace('\\', '/') file_name = dataset[1] print(x) print(file_name) x = cv2.imread(x) img_y, image_info = model.detect(x) cv2.imwrite('./tmp/draw/{}.{}'.format(file_name, ext), img_y) return image_info ``` ```python from core import process, predict def c_main(path, model, ext): image_data = process.pre_process(path) image_info = predict.predict(image_data, model, ext) return image_data[1] + '.' + ext, image_info if __name__ == '__main__': pass ``` # 4. Flask 部署： 然后通过Flask框架写相应函数： ```python @app.route('/upload', methods=['GET', 'POST']) def upload_file(): file = request.files['file'] print(datetime.datetime.now(), file.filename) if file and allowed_file(file.filename): src_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename) file.save(src_path) shutil.copy(src_path, './tmp/ct') image_path = os.path.join('./tmp/ct', file.filename) pid, image_info = core.main.c_main( image_path, current_app.model, file.filename.rsplit('.', 1)[1]) return jsonify({'status': 1, 'image_url': 'http://127.0.0.1:5003/tmp/ct/' + pid, 'draw_url': 'http://127.0.0.1:5003/tmp/draw/' + pid, 'image_info': image_info}) return jsonify({'status': 0}) ``` 这样前端发出POST请求时，会对上传的图像进行处理。 # 5. VUE前端： 主要是通过VUE编写前端WEB框架。 核心前后端交互代码： ```html // 上传文件 update(e) { this.percentage = 0; this.dialogTableVisible = true; this.url_1 = ""; this.url_2 = ""; this.srcList = []; this.srcList1 = []; this.wait_return = ""; this.wait_upload = ""; this.feature_list = []; this.feat_list = []; this.fullscreenLoading = true; this.loading = true; this.showbutton = false; let file = e.target.files[0]; this.url_1 = this.$options.methods.getObjectURL(file); let param = new FormData(); //创建form对象 param.append("file", file, file.name); //通过append向form对象添加数据 var timer = setInterval(() => { this.myFunc(); }, 30); let config = { headers: { "Content-Type": "multipart/form-data" }, }; //添加请求头 axios .post(this.server_url + "/upload", param, config) .then((response) => { this.percentage = 100; clearInterval(timer); this.url_1 = response.data.image_url; this.srcList.push(this.url_1); this.url_2 = response.data.draw_url; this.srcList1.push(this.url_2); this.fullscreenLoading = false; this.loading = false; this.feat_list = Object.keys(response.data.image_info); for (var i = 0; i < this.feat_list.length; i++) { response.data.image_info[this.feat_list[i]][2] = this.feat_list[i]; this.feature_list.push(response.data.image_info[this.feat_list[i]]); } this.feature_list.push(response.data.image_info); this.feature_list_1 = this.feature_list[0]; this.dialogTableVisible = false; this.percentage = 0; this.notice1(); }); }, ``` 这段代码在点击提交图片时响应： ```html <div slot="header" class="clearfix"> <span>检测目标</span> <el-button style="margin-left: 35px" v-show="!showbutton" type="primary" icon="el-icon-upload" class="download_bt" v-on:click="true_upload2" > 重新选择图像 <input ref="upload2" style="display: none" name="file" type="file" @change="update" />