目錄一.開源神經網絡(AlexNet)1.獲取數據集2.神經網絡模型3.訓練神經網絡4.對模型進行預測二、花卉識別系統搭建（flask）1.構建頁面：2.調用神經網絡模型3.系統識別結果4.啟動系統：三、總結一.開源神經網絡(AlexNet)1.獲取數據集

使用步驟如下：* （1）在data_set文件夾下創建新文件夾'flower_data'* （2）點擊鏈接下載花分類數據集download.tensorflow.org/example_im…* （3）解壓數據集到flower_data文件夾下* （4）執行'split_data.py'腳本自動將數據集劃分成訓練集train和驗證集val

split_data.py

import osfrom shutil import copy, rmtreeimport random def mk_file(file_path: str): if os.path.exists(file_path):# 如果文件夾存在，則先刪除原文件夾在重新創建rmtree(file_path) os.makedirs(file_path) def main(): # 保證隨機可復現 random.seed(0) # 將數據集中10%的數據劃分到驗證集中 split_rate = 0.1 # 指向你解壓后的flower_photos文件夾 cwd = os.getcwd() data_root = os.path.join(cwd, 'flower_data') origin_flower_path = os.path.join(data_root, 'flower_photos') assert os.path.exists(origin_flower_path) flower_class = [cla for cla in os.listdir(origin_flower_path) if os.path.isdir(os.path.join(origin_flower_path, cla))] # 建立保存訓練集的文件夾 train_root = os.path.join(data_root, 'train') mk_file(train_root) for cla in flower_class:# 建立每個類別對應的文件夾mk_file(os.path.join(train_root, cla)) # 建立保存驗證集的文件夾 val_root = os.path.join(data_root, 'val') mk_file(val_root) for cla in flower_class:# 建立每個類別對應的文件夾mk_file(os.path.join(val_root, cla)) for cla in flower_class:cla_path = os.path.join(origin_flower_path, cla)images = os.listdir(cla_path)num = len(images)# 隨機采樣驗證集的索引eval_index = random.sample(images, k=int(num*split_rate))for index, image in enumerate(images): if image in eval_index:# 將分配至驗證集中的文件復制到相應目錄image_path = os.path.join(cla_path, image)new_path = os.path.join(val_root, cla)copy(image_path, new_path) else:# 將分配至訓練集中的文件復制到相應目錄image_path = os.path.join(cla_path, image)new_path = os.path.join(train_root, cla)copy(image_path, new_path) print('r[{}] processing [{}/{}]'.format(cla, index+1, num), end='') # processing barprint() print('processing done!') if __name__ == ’__main__’: main()2.神經網絡模型

model.py

import torch.nn as nnimport torch class AlexNet(nn.Module): def __init__(self, num_classes=1000, init_weights=False):super(AlexNet, self).__init__()# 用nn.Sequential()將網絡打包成一個模塊，精簡代碼self.features = nn.Sequential( # 卷積層提取圖像特征 nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2), # input[3, 224, 224] output[48, 55, 55] nn.ReLU(inplace=True), # 直接修改覆蓋原值，節省運算內存 nn.MaxPool2d(kernel_size=3, stride=2), # output[48, 27, 27] nn.Conv2d(48, 128, kernel_size=5, padding=2), # output[128, 27, 27] nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), # output[128, 13, 13] nn.Conv2d(128, 192, kernel_size=3, padding=1), # output[192, 13, 13] nn.ReLU(inplace=True), nn.Conv2d(192, 192, kernel_size=3, padding=1), # output[192, 13, 13] nn.ReLU(inplace=True), nn.Conv2d(192, 128, kernel_size=3, padding=1), # output[128, 13, 13] nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), # output[128, 6, 6])self.classifier = nn.Sequential( # 全連接層對圖像分類 nn.Dropout(p=0.5), # Dropout 隨機失活神經元，默認比例為0.5 nn.Linear(128 * 6 * 6, 2048), nn.ReLU(inplace=True), nn.Dropout(p=0.5), nn.Linear(2048, 2048), nn.ReLU(inplace=True), nn.Linear(2048, num_classes),)if init_weights: self._initialize_weights() # 前向傳播過程 def forward(self, x):x = self.features(x)x = torch.flatten(x, start_dim=1)# 展平后再傳入全連接層x = self.classifier(x)return x# 網絡權重初始化，實際上 pytorch 在構建網絡時會自動初始化權重 def _initialize_weights(self):for m in self.modules(): if isinstance(m, nn.Conv2d): # 若是卷積層nn.init.kaiming_normal_(m.weight, mode=’fan_out’, # 用（何）kaiming_normal_法初始化權重nonlinearity=’relu’)if m.bias is not None: nn.init.constant_(m.bias, 0) # 初始化偏重為0 elif isinstance(m, nn.Linear): # 若是全連接層nn.init.normal_(m.weight, 0, 0.01) # 正態分布初始化nn.init.constant_(m.bias, 0) # 初始化偏重為03.訓練神經網絡

train.py

# 導入包import torchimport torch.nn as nnfrom torchvision import transforms, datasets, utilsimport matplotlib.pyplot as pltimport numpy as npimport torch.optim as optimfrom model import AlexNetimport osimport jsonimport time # 使用GPU訓練device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')with open(os.path.join('train.log'), 'a') as log: log.write(str(device)+'n') #數據預處理data_transform = { 'train': transforms.Compose([transforms.RandomResizedCrop(224), # 隨機裁剪，再縮放成 224×224 transforms.RandomHorizontalFlip(p=0.5), # 水平方向隨機翻轉，概率為 0.5, 即一半的概率翻轉, 一半的概率不翻轉 transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]), 'val': transforms.Compose([transforms.Resize((224, 224)), # cannot 224, must (224, 224) transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])} #導入、加載 訓練集# 導入訓練集#train_set = torchvision.datasets.CIFAR10(root=’./data’, # 數據集存放目錄# train=True, # 表示是數據集中的訓練集#download=True, # 第一次運行時為True，下載數據集，下載完成后改為False#transform=transform) # 預處理過程# 加載訓練集 #train_loader = torch.utils.data.DataLoader(train_set, # 導入的訓練集# batch_size=50, # 每批訓練的樣本數# shuffle=False, # 是否打亂訓練集# num_workers=0) # num_workers在windows下設置為0 # 獲取圖像數據集的路徑data_root = os.path.abspath(os.path.join(os.getcwd(), '../..')) # get data root path 返回上上層目錄image_path = data_root + '/jqsj/data_set/flower_data/' # flower data_set path # 導入訓練集并進行預處理train_dataset = datasets.ImageFolder(root=image_path + '/train', transform=data_transform['train'])train_num = len(train_dataset) # 按batch_size分批次加載訓練集train_loader = torch.utils.data.DataLoader(train_dataset,# 導入的訓練集 batch_size=32, # 每批訓練的樣本數 shuffle=True,# 是否打亂訓練集 num_workers=0)# 使用線程數，在windows下設置為0 #導入、加載 驗證集# 導入驗證集并進行預處理validate_dataset = datasets.ImageFolder(root=image_path + '/val',transform=data_transform['val'])val_num = len(validate_dataset) # 加載驗證集validate_loader = torch.utils.data.DataLoader(validate_dataset,# 導入的驗證集 batch_size=32, shuffle=True, num_workers=0) # 存儲 索引：標簽 的字典# 字典，類別：索引 {’daisy’:0, ’dandelion’:1, ’roses’:2, ’sunflower’:3, ’tulips’:4}flower_list = train_dataset.class_to_idx# 將 flower_list 中的 key 和 val 調換位置cla_dict = dict((val, key) for key, val in flower_list.items()) # 將 cla_dict 寫入 json 文件中json_str = json.dumps(cla_dict, indent=4)with open(’class_indices.json’, ’w’) as json_file: json_file.write(json_str) #訓練過程net = AlexNet(num_classes=5, init_weights=True) # 實例化網絡（輸出類型為5，初始化權重）net.to(device) # 分配網絡到指定的設備（GPU/CPU）訓練loss_function = nn.CrossEntropyLoss() # 交叉熵損失optimizer = optim.Adam(net.parameters(), lr=0.0002) # 優化器（訓練參數，學習率） save_path = ’./AlexNet.pth’best_acc = 0.0 for epoch in range(150): ########################################## train ############################################### net.train() # 訓練過程中開啟 Dropout running_loss = 0.0# 每個 epoch 都會對 running_loss 清零 time_start = time.perf_counter()# 對訓練一個 epoch 計時for step, data in enumerate(train_loader, start=0): # 遍歷訓練集，step從0開始計算images, labels = data # 獲取訓練集的圖像和標簽optimizer.zero_grad()# 清除歷史梯度outputs = net(images.to(device)) # 正向傳播loss = loss_function(outputs, labels.to(device)) # 計算損失loss.backward() # 反向傳播optimizer.step() # 優化器更新參數running_loss += loss.item()# 打印訓練進度（使訓練過程可視化）rate = (step + 1) / len(train_loader) # 當前進度 = 當前step / 訓練一輪epoch所需總stepa = '*' * int(rate * 50)b = '.' * int((1 - rate) * 50)with open(os.path.join('train.log'), 'a') as log: log.write(str('rtrain loss: {:^3.0f}%[{}->{}]{:.3f}'.format(int(rate * 100), a, b, loss))+'n')print('rtrain loss: {:^3.0f}%[{}->{}]{:.3f}'.format(int(rate * 100), a, b, loss), end='') print() with open(os.path.join('train.log'), 'a') as log: log.write(str(’%f s’ % (time.perf_counter()-time_start))+'n') print(’%f s’ % (time.perf_counter()-time_start)) ########################################### validate ########################################### net.eval() # 驗證過程中關閉 Dropout acc = 0.0 with torch.no_grad():for val_data in validate_loader: val_images, val_labels = val_data outputs = net(val_images.to(device)) predict_y = torch.max(outputs, dim=1)[1] # 以output中值最大位置對應的索引（標簽）作為預測輸出 acc += (predict_y == val_labels.to(device)).sum().item() val_accurate = acc / val_num# 保存準確率最高的那次網絡參數if val_accurate > best_acc: best_acc = val_accurate torch.save(net.state_dict(), save_path)with open(os.path.join('train.log'), 'a') as log: log.write(str(’[epoch %d] train_loss: %.3f test_accuracy: %.3f n’ % (epoch + 1, running_loss / step, val_accurate))+'n')print(’[epoch %d] train_loss: %.3f test_accuracy: %.3f n’ % (epoch + 1, running_loss / step, val_accurate))with open(os.path.join('train.log'), 'a') as log: log.write(str(’Finished Training’)+'n')print(’Finished Training’)

訓練結果后，準確率是94%

訓練日志如下：

4.對模型進行預測

predict.py

import torch

接著對其中一個花卉圖片進行識別，其結果如下：

可以看到只有一個識別結果（daisy雛菊）和準確率1.0是100%（范圍是0~1，所以1對應100%）

為了方便使用這個神經網絡，接著我們將其開發成一個可視化的界面操作

二、花卉識別系統搭建（flask）1.構建頁面：

2.調用神經網絡模型

main.py

# coding:utf-8 from flask import Flask, render_template, request, redirect, url_for, make_response, jsonifyfrom werkzeug.utils import secure_filenameimport osimport time ####################模型所需庫包import torchfrom model import AlexNetfrom PIL import Imagefrom torchvision import transformsimport matplotlib.pyplot as pltimport 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 modelmodel = AlexNet(num_classes=5)# load model weightsmodel_weight_path = './AlexNet.pth'#, map_location=’cpu’model.load_state_dict(torch.load(model_weight_path, map_location=’cpu’)) # 關閉 Dropoutmodel.eval() ###################from datetime import timedelta# 設置允許的文件格式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__)# 設置靜態文件緩存過期時間app.send_file_max_age_default = timedelta(seconds=1) #圖片裝換操作def tran(img_path): # 預處理 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’]if not (f and allowed_file(f.filename)): return jsonify({'error': 1001, 'msg': '請檢查上傳的圖片類型，僅限于png、PNG、jpg、JPG、bmp'}) basepath = os.path.dirname(__file__) # 當前文件所在路徑path = secure_filename(f.filename)upload_path = os.path.join(basepath, ’static/images’, secure_filename(f.filename)) # 注意：沒有的文件夾一定要先創建，不然會提示沒有該路徑# upload_path = os.path.join(basepath, ’static/images’,’test.jpg’) #注意：沒有的文件夾一定要先創建，不然會提示沒有該路徑print(path) img = tran(’static/images’+path)###########################預測圖片with torch.no_grad(): # predict class output = torch.squeeze(model(img)) # 將輸出壓縮，即壓縮掉 batch 這個維度 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='雛菊'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)3.系統識別結果

<!DOCTYPE html><html lang='en'><head> <meta charset='UTF-8'> <title>李運辰-花卉識別系統v1.0</title> <script src='https://www.xxx.com.cn/static/js/locales/zh.js'></script> </head><body> <h1 align='center'>李運辰-花卉識別系統v1.0</h1><div align='center'> <form action='' enctype=’multipart/form-data’ method=’POST’><input type='file' name='file' data-show-preview='false' /><br><input type='submit' value='上傳' /> </form><p style='size:15px;color:blue;'>識別結果:{{res_chinese}}</p></br><p style='size:15px;color:red;'>準確率:{{pred}}%</p> <img src='https://www.xxx.com.cn/bcjs/{{ ’./static/images/’+path }}' alt=''/></div></body></html>4.啟動系統：

python main.py

接著在瀏覽器在瀏覽器里面訪問

http://127.0.0.1/upload

出現如下界面：

最后來一個識別過程的動圖

三、總結

ok，這個花卉系統就已經搭建完成了，是不是超級簡單，我也是趁著修了這個機器視覺這么課，才弄這么一個系統，回顧一下之前的知識，哈哈哈。

以上就是用python搭建一個花卉識別系統的詳細內容，更多關于python 花卉識別系統的資料請關注好吧啦網其它相關文章！