深度学习目标检测-交通标志检测web界面

深度学习目标检测-交通标志检测web界面

今天我们一起学习利用flask框架将深度学习检测封装为一个界面系统。该界面是基于之前写的一篇文章的训练模型进行检测：
自动驾驶目标检测项目实战(二)—基于Faster-RCNN的交通标志检测

效果

首先看看效果：

有识别历史记录。

算法流程

参考这些大佬的代码进行训练：
https://github.com/arindam93/Traffic-Sign-Detection-Faster-RCNN

使用Faster-RCNN进行训练，这是一个二分类的问题，也就是检测是否存在交通标志，而未进行分类。

训练完之后，可以得到pickle文件和hdf5文件：

这里选用fcnet，也可选择vgg进行训练。

网页设计

使用Flask框架进行网页的搭建。主要代码如下：

# -*- coding: utf-8 -*-
import os
import uuid
import cv2
from flask import (Blueprint, flash, g, redirect, render_template, request, url_for
)
from flask import send_from_directory
from flask_img.db import get_db
import shutil
from test_frcnn import detect_sign # 加载文件函数
import cv2 as cvtable = []  # 识别历史表格
hot = 0 # 编号bp = Blueprint('main', __name__)ALLOWED_EXTENSIONS = {'png', 'jpg', 'bmp'}
IMAGE_FOLDER = os.path.join(bp.root_path, '..', 'images')
TMP_FOLDER = os.path.join(bp.root_path, '..', 'tmps')
CARD_COLOR = {"blue": "蓝色","yello": "黄色","green": "绿色"
}try:shutil.rmtree(IMAGE_FOLDER)os.makedirs(IMAGE_FOLDER)
except OSError:passtry:shutil.rmtree(TMP_FOLDER)os.makedirs(TMP_FOLDER)
except OSError:passdef allowed_file(filename):return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS@bp.route('/', methods=('GET', 'POST'))
def index():if request.method == 'POST':if 'defect_image' not in request.files:flash('请选择识别文件')return redirect(request.url)image = request.files['defect_image']image_path = ""if image.filename == '':flash('请选择识别文件')return redirect(request.url)if not image or not allowed_file(image.filename):flash('文件不存在或后缀不合法')return redirect(request.url)# 原来的文件名写法# image_uuid = uuid.uuid4().hex# filename = ''.join([image_uuid, '.', image.filename.rsplit('.', 1)[1]])# 现在的文件名写法filename = image.filenameimage.save(os.path.join(IMAGE_FOLDER, filename))result = {}try:db = get_db()result = defect_pic(filename, image_path)db.execute('INSERT INTO img_info (img_color_contours, img_only_color, barcode_info)'' VALUES (?, ?, ?)',(result.get('img_color_contours', ''),result.get('img_only_color', ''),' '.join(result['text']) if result.get('text') else ''))db.commit()result.update({'result': '检测成功'})except Exception as e:flash(e)result.update({'result': '检测失败'})return render_template('index.html', result=result)return render_template('index.html')@bp.route('/image/<filename>')
def show_img(filename):return send_from_directory(IMAGE_FOLDER, filename)@bp.route('/tmp/<filename>')
def show_tmp_img(filename):return send_from_directory(TMP_FOLDER, filename)def defect_pic(filename, image_path):pic_path = os.path.join(IMAGE_FOLDER, filename)res, defect_img = detect_sign(pic_path, filename, "绝对路径")has_defect = '否'  # 初始化if res > 0:has_defect = '是'cv2.imwrite(TMP_FOLDER + "/" + "defect_" + filename, defect_img)global hot # 设为全局变量img_his = cv.imread(pic_path)x, y = img_his.shape[0:2]cv2.imwrite(IMAGE_FOLDER + "/" + "hot_" + filename, cv.resize(img_his, (int(y / 2), int(x / 2))))table.append([hot, '/image/' + "hot_" + filename, has_defect, res])hot += 1  # 编号加1return {'defect': True,'img_color_contours': '原始图像','img_color_contours_path': '/image/'+filename,'img_only_color': '检测图像','img_only_color_path': '/tmp/'+ "defect_" + filename,'has_defect': has_defect,'is_defect': res,'table': table,}

注意

1.该界面适合所有检测目标，只需要更改界面的后台检测算法即可。
2.目前算法可以同时支持检测分类，具体可以看：
深度学习目标检测ui界面-交通标志检测识别

需要代码的请私信我