鸟类识别系统后端

一、Flask框架简介

      Flask框架诞生于2010年，是一个用python语言基于Werkzeug工具箱编写的轻量级web开发框架，它主要面向需求简单，项目周期短的小应用。Flask本身相当于一个内核，其他几乎所有的功能都要用到扩展，都需要用第三方的扩展来实现。用 extension 增加其他功能。相比Python最出名的后台框架Django，Flask应该算是最灵活的框架之一，这也是Flask受到广大开发者喜爱的原因。

二、功能分析

     在本次项目中，后端的主要任务就是支持用户将图片上传到网页上再由卷积神经网络模型进行识别

三、具体实现（尚在完善）

from flask import Flask, render_template, request, redirect, url_for, make_response, jsonify

from werkzeug.utils import secure_filename import os import time#模型所需库包import torch from model import AlexNet from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt import json # read class_indicttry: json_file = open('./class_indices.json', 'r') class_indict = json.load(json_file) except Exception as e: print(e) exit(-1) # 模型实例化 model = AlexNet(num_classes=5) model_weight_path = "./AlexNet.pth" model.load_state_dict(torch.load(model_weight_path, map_location='cpu')) model.eval()from datetime import timedelta # 设置允许的文件格式 ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp'])

 

# 文件函数

def allowed_file(filename):

return'.'in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS app = Flask(__name__) # 设置静态文件缓存过期时间 app.send_file_max_age_default = timedelta(seconds=1)def tran(img_path):# 预处理 data_transform = transforms.Compose( [transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # 加载图片 img = Image.open("xxx.jpg") img = data_transform(img) img = torch.unsqueeze(img, dim=0) return img @app.route('/upload', methods=['POST', 'GET']) def upload(): path=""if request.method == 'POST': f = request.files['file'] ifnot (f and allowed_file(f.filename)): return jsonify({"error": 1001, "msg": "请检查上传的图片类型，仅限于png、PNG、jpg、JPG、bmp"}) basepath = os.path.dirname(__file__) # 当前文件所在路径 path = secure_filename(f.filename) upload_path = os.path.join(basepath, 'static/images', secure_filename(f.filename)) # 注意：没有的文件夹一定要先创建，不然会提示没有该路径# upload_path = os.path.join(basepath, 'static/images','test.jpg') #注意：没有的文件夹一定要先创建，不然会提示没有该路径 print(path) img = tran('static/images'+path) ###########################预测图片with torch.no_grad(): # predict class output = torch.squeeze(model(img)) # 将输出压缩，即压缩掉 batch 这个维度 predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() res = class_indict[str(predict_cla)] pred = predict[predict_cla].item() #print(class_indict[str(predict_cla)], predict[predict_cla].item())

# 得到鸟类类型

res_chinese = ""

from flask import Flask, render_template, request, redirect, url_for, make_response, jsonify from werkzeug.utils import secure_filename import os import time ####################模型所需库包import torch from model import AlexNet from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt import json # read class_indicttry: json_file = open('./class_indices.json', 'r') class_indict = json.load(json_file) except Exception as e: print(e) exit(-1) # create model model = AlexNet(num_classes=5) # load model weights model_weight_path = "./AlexNet.pth"#, map_location='cpu' model.load_state_dict(torch.load(model_weight_path, map_location='cpu')) # 关闭 Dropout model.eval() ###################from datetime import timedelta # 设置允许的文件格式 ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp']) def allowed_file(filename):return'.'in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS app = Flask(__name__) # 设置静态文件缓存过期时间 app.send_file_max_age_default = timedelta(seconds=1) #图片装换操作def tran(img_path):# 预处理 data_transform = transforms.Compose( [transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # load image img = Image.open("pgy2.jpg") #plt.imshow(img)# [N, C, H, W] img = data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) return img @app.route('/upload', methods=['POST', 'GET']) # 添加路由def upload(): path=""if request.method == 'POST': f = request.files['file'] ifnot (f and allowed_file(f.filename)): return jsonify({"error": 1001, "msg": "请检查上传的图片类型，仅限于png、PNG、jpg、JPG、bmp"}) basepath = os.path.dirname(__file__) # 当前文件所在路径 path = secure_filename(f.filename) upload_path = os.path.join(basepath, 'static/images', secure_filename(f.filename)) # 注意：没有的文件夹一定要先创建，不然会提示没有该路径# upload_path = os.path.join(basepath, 'static/images','test.jpg') #注意：没有的文件夹一定要先创建，不然会提示没有该路径 print(path) img = tran('static/images'+path) ###########################预测图片with torch.no_grad(): # predict class output = torch.squeeze(model(img)) # 将输出压缩，即压缩掉 batch 这个维度 predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() res = class_indict[str(predict_cla)] pred = predict[predict_cla].item() #print(class_indict[str(predict_cla)], predict[predict_cla].item()) res_chinese = ""if res=="daisy": res_chinese="雏菊"if res=="dandelion": res_chinese="蒲公英"if res=="roses": res_chinese="玫瑰"if res=="sunflower": res_chinese="向日葵"if res=="tulips": res_chinese="郁金香"#print('result:', class_indict[str(predict_class)], 'accuracy:', prediction[predict_class])########################## f.save(upload_path) pred = pred*100return render_template('upload_ok.html', path=path, res_chinese=res_chinese,pred = pred, val1=time.time()) return render_template('upload.html') if __name__ == '__main__': # app.debug = True app.run(host='127.0.0.1', port=80,debug = True)