鱼类识别，pytorch，flask，opencv"/>
在web实现鱼类识别，pytorch，flask，opencv
基于pytorch的预训练模型，结合opencv处理图像，用flask搭建本地服务器，在web给用户提供鱼类识别的项目。
目录
pytorch代码
flask本地服务器代码
以下是我的html文件的代码
index.html
2.html
1.html
pytorch代码
import os
import cv2
import torch
from random import uniform
from torch import optim, nn, device, cuda, save
import torchvision.transforms as T
import torchvision
from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F# opencv图像处理
def pre_handing_train(img_path):# 读取图片，灰度化img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)# 除背景噪声img = cv2.medianBlur(img, 3)img = cv2.GaussianBlur(img, (3, 3), 0)# 锐化边缘img = cv2.equalizeHist(img)return imgdef pre_handing_test(img_path):img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)return img# 读取图像处理
class PicDataset(Dataset):def __init__(self, path, transform=None):if path == "pic/":pre_handing = pre_handing_trainelse:pre_handing = pre_handing_testjoin = os.path.joinlistdir = os.listdirself.transform = transformself.images = [] # 存储图像数据self.labels = [] # 存储标签appendI = self.images.appendappendL = self.labels.append# 遍历每个分类的文件夹for folder in listdir(path):folder_path = join(path, folder)# 遍历每张图片for filename in listdir(folder_path):# 每一张图片所在的文件路径img_path = join(folder_path, filename)# opencv处理img = pre_handing(img_path)# 存储图像信息appendI(img)appendL(int(folder))# 每一张图片的存储长度def __len__(self):return len(self.images)def __getitem__(self, idx):image = self.images[idx]label = self.labels[idx]# 转存储格式为tensor格式if self.transform is not None:image = self.transform(image)return image, labeltrain_dir = "fishP/train"
# 构建训练集的各种操作集合,创建训练集
transform_train = T.Compose([T.ToPILImage(), # 将图片存储格式转为PIL类型T.Resize((32, 32)), # 将图片按比例重塑成64*64像素大小T.Grayscale(num_output_channels=3), # 灰度化图片，输出三个通道T.ToTensor(), # 将图片存储格式转为tensor类型# 数据标准化，即均值为0，标准差为1。# 简单来说就是将数据按通道进行计算，将每一个通道的数据先计算出其方差与均值，然后再将其每一个通道内的每一个数据减去均值，再除以方差，得到归一化后的结果。# 在深度学习图像处理中，标准化处理之后，可以使数据更好的响应激活函数，提高数据的表现力，减少梯度爆炸和梯度消失的出现。T.Normalize((0.5,), (0.5,)),T.RandomHorizontalFlip(p=0.3), # 随机水平翻转T.RandomRotation(10) # 随机将图片旋转10°
])
trainset = PicDataset(train_dir, transform=transform_train)
trainloader = DataLoader(trainset, batch_size=2, shuffle=True)# 加载预训练模型vgg16并更改分类器的输出
model =torchvision.models.vgg16(weights="VGG16_Weights.DEFAULT")
for p in model.features.parameters():p.requires_grad=False
model.classifier[-1].out_features=23# 优先选择显卡训练
device = device("cuda:0" if cuda.is_available() else "cpu")
model.to(device)# 损失函数计算
criterion = nn.CrossEntropyLoss()
# 优化器
optimizer = optim.SGD(model.parameters(), lr=uniform(0.001, 0.01), momentum=0.9)print('start train')
for epoch in range(10):running_loss = 0.0for i, data in enumerate(trainloader, 0):inputs, labels = dataoptimizer.zero_grad()# 对于分类任务，output是一个概率分布，每个元素表示相应的类别的置信度得分，可使用softmax将其转化为概率值outputs = model(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item()# 取模运算，每200个小批量打印一次if i % 200 == 199:print('[%d, %5d] loss: %.3f' %(epoch + 1, i + 1, running_loss / 200))running_loss = 0.0
print('Finished Train')# 保存模型的参数
torch.save(model.state_dict(), 'model.pth')# 测试集路径
test_dir = "fishP/valid/"
# 构建测试集的各种操作集合，创建测试级
transform_test = T.Compose([T.ToPILImage(), # 将图片存储格式转为PIL类型T.Resize((64, 64)), # 将图片按比例重塑成64*64像素大小T.Grayscale(num_output_channels=3), # 灰度化图片，输出三个通道T.Normalize((0.5,), (0.5,)),T.ToTensor(), # 将图片存储格式转为tensor类型
])
testset = PicDataset(test_dir, transform=transform_test)
testloader = DataLoader(testset, batch_size=64, shuffle=True)# 测试模型
with torch.no_grad():correct = 0total = 0for data in testloader:images, labels = dataimages, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))
flask本地服务器代码
import os
from flask import Flask, request, render_template
from werkzeug.utils import secure_filename
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
import torchvision.models as modelsapp = Flask(__name__)
# 用户上传的文件的保存路径
UPLOAD_FOLDER = 'static'
ALLOWED_EXTENSIONS = {'jpg', 'jpeg', 'png', 'gif'}def allowed_file(filename):return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS@app.route('/')
def index():return render_template('index.html')@app.route('/1.html', methods=["GET"])
def loop1():return render_template('1.html')@app.route('/2.html', methods=["GET"])
def loop2():return render_template('2.html')@app.route('/3.html', methods=["GET"])
def loop3():return render_template('3.html')@app.route('/4.html', methods=["GET"])
def loop4():return render_template('4.html')@app.route('/5.html', methods=["GET"])
def loop5():return render_template('5.html')@app.route('/upload', methods=["POST"])
def upload():file = request.files["image"]if file and allowed_file(file.filename):filename = secure_filename(file.filename)file.save(os.path.join(UPLOAD_FOLDER, filename))# 参数=路径+文件名result = classify_fish(os.path.join(UPLOAD_FOLDER, filename))return render_template('2.html', result=result, statement="当前文件：" + filename)else:return render_template('2.html', statement="未选择文件或文件有误")def classify_fish(image_file):# 加载模型model = models.vgg16()model.load_state_dict(torch.load('model.pth'))model.eval()# 图像预处理和归一化preprocess = transforms.Compose([transforms.Resize((64, 64)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])# 将图像转换为 PyTorch 张量image = Image.open(image_file).convert('RGB')image = preprocess(image)image = image.unsqueeze(0)with torch.no_grad():output = model(image)# torch.max()表示在张量中取最大值，参数：操作对象，操作维度# 操作维度是1，表示类别维度上取最大值# torch.max(output.data, 1)返回的是一个元组，(最大值,最大值对应的索引)# predict是一个张一维量，表示分类物品的编号_, predict = torch.max(output.data, 1)return predict.item()if __name__ == '__main__':app.secret_key = 'supersecretkey'app.config['SESSION_TYPE'] = 'filesystem'# 只允许用户上传图片文件app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDERapp.run(debug=True)
以下是我的文件目录，fishR文件夹是我的项目文件目录
fishP文件夹存放训练集和测试集，static文件夹存放一些用户上传的文件和js文件，templates文件夹存放html文件，test文件夹是存放一些无关紧要的测试文件，不需要建立，app.py存放基于flask建立的本地服务器，p3.py存放着pytorch训练代码。文件夹（static，templates）的命名一定要和我的一样，不然会出问题。
我的templates文件夹下的目录
fishP下的文件目录
train （训练集）文件夹下放着每一种鱼的文件夹
valid （测试集或者叫验证集）文件夹下放着每一种鱼的文件夹
这里用数字表示鱼的种类
以下是我的html文件的代码
有部分html文件我没贴出来，是因为那些文件暂时是空的，作为我以后的功能拓展。可以根据个人需求建立相应的html文件
index.html
<!DOCTYPE html>
<html lang="en">
<head><meta charset="UTF-8"><title>鱼型识别系统</title><style>h1 {text-align: center;padding: 30px;color: #ff4800}body {background-image: url("=1029016406,1880474304&fm=253&fmt=auto&app=138&f=JPEG?w=800&h=500");background-size: cover;background-attachment: fixed;opacity: 0.9;background-color: cornflowerblue;}input[type="submit"] {background-color: #ff4800;border: none;padding: 10px 20px;text-decoration: none;margin: 20px 0;border-radius: 5px;color: white;}#d0 {text-align: center;display: flex;justify-content: center;align-items: center;}#d1 {width: 30%;height: 75%;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;margin: auto;}#d1:hover {background-color:rgba(255,255,255,0.6);}#d2 {width: 30%;height: 75%;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;margin: auto;}#d2:hover {background-color:rgba(255,255,255,0.6);}#d2 > p {padding: 70px;color: red;font-size: 15px;}#d3 {width: 30%;height: 75%;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;margin: auto;}#d3:hover {background-color:rgba(255,255,255,0.6);}#d4 {text-align: center;display: flex;justify-content: center;align-items: center;}#d5 {width: 30%;height: 75%;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;margin: auto;}#d5:hover {background-color:rgba(255,255,255,0.6);}#d6 {width: 30%;height: 75%;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;margin: auto;}#d6:hover {background-color:rgba(255,255,255,0.6);}</style>
</head>
<body><h1>辅助鱼类研究系统</h1>
<div id="d0"><div id="d1" onclick="window.location.href='1.html'"><h1>3维追踪</h1></div><div id="d2"onclick="window.location.href='2.html'"><h1>快速识别</h1></div><div id="d3"onclick="window.location.href='3.html'"><h1>使用指南</h1></div>
</div><br><div id="d4"><div id="d5"onclick="window.location.href='4.html'"><h1>鱼类种质库</h1></div><div id="d6"onclick="window.location.href='5.html'"><h1>信息记录</h1></div>
</div>
</body>
</html>2.html
<!DOCTYPE html>
<html lang="en">
<head><meta charset="UTF-8"><title>Title</title><style>body {/*渐变色*/background: radial-gradient(circle at center,cornflowerblue,cadetblue);}h1 {text-align: center}form{margin: auto;width: 50%;padding: 10px;text-align: center;}.result p{font-weight: bold;}button {background-color: #ff4800;border: none;padding: 10px 20px;text-decoration: none;margin: 20px 0;border-radius: 5px;color: white;}button:hover {box-shadow: 0 0 25px orangered;box-reflect: below 1px linear-gradient(transparent,rgba(0,0,0,0.3));}input[type="button"] {background-color: #ff4800;border: none;padding: 10px 20px;text-decoration: none;margin: 20px 0;border-radius: 5px;color: white;}input[type="button"]:hover {box-shadow: 0 0 25px orangered;box-reflect: below 1px linear-gradient(transparent,rgba(0,0,0,0.3));}main {width: 50%;height: 50%;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;margin: auto;padding: 1%;}div {display: flex;}#s1 {width: 33%;height: 30%;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;margin: auto;padding: 10%;}#uploadFile {display: none;}</style><script>function clickFile(){const input=document.querySelector('#uploadFile')input.click()// let f = document.getElementById('uploadFile').files;// let [fileName, fileSize, fileType] = [f[0].name,f[0].size, f[0].type];// /*分别获取 文件名 文件大小 文件类型*/// document.getElementById("stmend").innerHTML="当前选择的文件是"+fileName}</script></head>
<body><header><h1>鱼种属快速识别</h1></header><div><main><form enctype="multipart/form-data" method="POST" action="/upload"><input type="hidden" name="MAX_FILE_SIZE" value="100000"><input id="uploadFile" type="file" name="image"><section id="s2">{% if statement %}<div class="statement"><p>{{ statement }}</p></div>{% endif %}</section><input type="button" value="上传图片" class="btn" onclick="clickFile()"><button type="submit">查询</button></form></main>
</div>
<br>
<div><section id="s1">{% if result %}<div class="result"><p>这张图片上的鱼是 {{ result }}</p></div>{% endif %}</section>
</div></body>
</html>1.html
<!DOCTYPE html>
<html lang="en">
<head><meta name="viewport" content="initial-scale=1.0, user-scalable=no" /><meta http-equiv="Content-Type" content="text/html" charset="UTF-8"><title>3d建模</title><style>html {height: 100%}body {height: 100%;margin: 0;padding: 0}#container {height: 100%;}#d1 {height: 100%;width: 100%;display: grid;grid-template-columns: 3fr 1fr;}main {background-color: rgba(195,150,255,0.5);border: 1px solid rgba(195,191,255,0.5);text-align: center;background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;}button {background-color: orange;}#d2:hover {background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;}p:hover {background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(255,255,255,0.3);text-align: center;}p {border: 2px solid rgba(195,150,255,1);border-radius: 20px;}#d2 {background-color: rgba(195,150,255,0.5);font-size: 20px;border: 2px solid rgba(195,150,255,1);border-radius: 20px;}#d3 {background-color: rgba(195,150,255,0.5);font-size: 20px;border: 2px solid rgba(195,150,255,1);border-radius: 20px;}#d3:hover {background-blend-mode: normal,soft-light;-webkit-backdrop-filter: blur(50px);border-radius: 20px;background-color:rgba(195,150,255,0.9);text-align: center;}</style><script type="text/javascript" src="=3.0&ak=你的百度地图密钥"></script>
</head>
<body>
<div id="d1"><div id="container"></div><div><main><h1 style="color: #ff4800">西湖鱼群信息</h1><p>时间：<script>var d=new Date()document.write(d)</script></p><p>气温：</p><p>空气湿度：</p><p>水温：</p><p>溶氧量：</p><p>水pH：</p><p>气压：</p><p>绿藻含量</p><p>鱼群数量：</p><p>检测到：</p><p>罗非鱼</p><p>草鱼</p><div id="d2" onclick="window.location.href='2.html'">历史记录</div><br><div id="d3" onclick="window.location.href='index.html'">回到主页</div></main></div>
</div><script type="text/javascript">// 定义一个自定义控件function resZoomControl(){//设置默认停靠位置和偏移量this.defaultAnchor=BMAP_ANCHOR_TOP_LEFTthis.defaultOffset=new BMap.Size(10,10)}// 通过JavaScript的prototype属性继承于BMap.ControlresZoomControl.prototype=new BMap.Control()resZoomControl.prototype.initialize=function(map){// 创建一个DOM元素var div=document.createElement("div")// 添加文字说明div.appendChild(document.createTextNode("回中"))// 设置样式div.style.cursor="pointer"div.style.border="1px solid gray"div.style.backgroundColor="red"// 绑定事件，点击回到中心处div.onclick=function(e){map.panTo(new BMap.Point(113.370,23.170))}}
</script>
<script type="text/javascript">// 创建地图实例var map =new BMap.Map("container");// 创建点坐标，华南农业大学西湖var point=new BMap.Point(113.370,23.170);// 初始化地图设置中心点坐标和地图级别map.centerAndZoom(point,15);// 或者，你可以设置城市名为中心点// map.centerAndZoom("广州",15);//创建自定义控件实例var myZoomCtrl = new resZoomControl();// 添加到地图当中map.addControl(myZoomCtrl);// 开启鼠标滚轮缩放map.enableScrollWheelZoom(true);// 添加控件map.addControl(new BMap.NavigationControl());// 平移缩放功能map.addControl(new BMap.ScaleControl());// 比例尺map.addControl(new BMap.OverviewMapControl());// 可折叠的缩略地图map.addControl(new BMap.GeolocationControl());// 定位map.addControl(new BMap.MapTypeControl());// 设置地图类型map.setCurrentCity("广州"); // 仅当设置城市信息时，MapTypeControl的切换功能才能可用</script></body>
</html>html效果
图片数据自取
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