飞桨首款韩女团小卡扭蛋机

!pip install paddlex==1.3.10 -i https://pypi.tuna.tsinghua.edu.cn/simple
!pip install paddlelite -i https://pypi.tuna.tsinghua.edu.cn/simple
!pip install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple
!pip install chardet -i https://pypi.tuna.tsinghua.edu.cn/simple
!pip install mpimg -i https://pypi.tuna.tsinghua.edu.cn/simple
!pip install imgaug

import os
path='nvtuan'if not os.path.exists(path):
    os.makedirs(path)else:    pass

!unzip -oq /home/aistudio/data/data120007/韩国女团小姐姐.zip -d nvtuan/

import warnings
warnings.filterwarnings("ignore")

import osimport random
dirpath='nvtuan'def get_all_txt():
        all_list = []
        i = 0
        for root, dirs, files in os.walk(dirpath): 
            for file in files:
                i = i + 1
                if ("JOY" in root):
                    all_list.append(os.path.join(root, file) + " 0
")                if ("meiyan" in root):
                    all_list.append(os.path.join(root, file) + " 1
")                if ("Rose" in root):
                    all_list.append(os.path.join(root, file) + " 2
")                if("xuexuan" in root):
                    all_list.append(os.path.join(root, file) + " 3
")

        allstr = ''.join(all_list)
        f = open('nvtuan/all_list.txt', 'w', encoding='utf-8')
        f.write(allstr)        return all_list, i

all_list, all_lenth = get_all_txt()
random.shuffle(all_list)
random.shuffle(all_list)#数据集划分train_size = int(all_lenth * 0.8)
train_list = all_list[:train_size]
val_list = all_list[train_size:]

train_txt = ''.join(train_list)
f_train = open('nvtuan/train_list.txt', 'w', encoding='utf-8')
f_train.write(train_txt)
f_train.close()

val_txt = ''.join(val_list)
f_val = open('nvtuan/val_list.txt', 'w', encoding='utf-8')
f_val.write(val_txt)
f_val.close()

import paddleimport warningsimport osimport randomimport matplotlib.pyplot as plt #plt 用于显示图片import matplotlib.image as mpimg #mpimg 用于读取图片from paddle.onnx import exportfrom paddlex.cls import transformsimport cv2import numpy as npimport paddlex as pdximport imgaug.augmenters as iaafrom tqdm import tqdmimport chardet

train_transforms = transforms.Compose([        # transforms.resize(256),
        transforms.RandomCrop(crop_size=224),
        transforms.RandomHorizontalFlip(),
        transforms.RandomVerticalFlip(),
        transforms.Normalize()
    ])

eval_transforms = transforms.Compose([
    transforms.ResizeByShort(short_size=256),
    transforms.CenterCrop(crop_size=224),
    transforms.Normalize()
])

train_dataset = pdx.datasets.ImageNet(
        data_dir='',
        file_list='nvtuan/train_list.txt',
        label_list='nvtuan/labels.txt',
        transforms=train_transforms,
        shuffle=True)

eval_dataset = pdx.datasets.ImageNet(
    data_dir='',
    file_list='nvtuan/val_list.txt',
    label_list='nvtuan/labels.txt',
    transforms=eval_transforms)

num_classes = len(train_dataset.labels)
model = pdx.cls.ResNet50_vd_ssld(num_classes=num_classes)

model.train(num_epochs=50,
            train_dataset=train_dataset,
            train_batch_size=16,
            eval_dataset=eval_dataset,
            lr_decay_epochs=[5, 15, 30],
            save_dir='output/resnet50_vd_ssld',
            save_interval_epochs=5,
            learning_rate=0.025,
            use_vdl=True)

#若有报错“testdata/.ipynb_checkpoints”相关的错 或者 'NoneType' object has no attribute 'dtype' ，请重新执行即可（出蛋通道卡住了）#若第一次抽取没有显示图片，请再次点击抽取import paddleimport warningsimport osimport randomimport matplotlib.pyplot as plt #plt 用于显示图片import matplotlib.image as mpimg #mpimg 用于读取图片import paddlex as pdximport cv2import numpy as npimport timeprint("正在进行扭蛋中，请稍后...... ")
time.sleep(5)#清除出蛋通道（解决“testdata/.ipynb_checkpoints”相关的错）import osimport shutil
delList = []
delDir = "testdata/.ipynb_checkpoints"delList = os.listdir(delDir )for f in delList:
    filePath = os.path.join( delDir, f )    if os.path.isfile(filePath):
        os.remove(filePath)    elif os.path.isdir(filePath):
        shutil.rmtree(filePath,True)

fileDir='testdata'pathDir=os.listdir(fileDir)    #取图片的原始路径picknumber=int(1) #从文件夹中取一张数量图片sample = random.sample(pathDir, picknumber)  #随机选取picknumber数量的样本图片model = pdx.load_model('output/resnet50_vd_ssld/best_model')
result = model.predict(os.path.join(fileDir,sample[0]))  

a=mpimg.imread(os.path.join(fileDir,sample[0]))   
plt.figure(figsize=(10,10))
plt.imshow(a) 
plt.axis('off') 
plt.show()# print(result)print("恭喜你获得: ", result[0]['category'].split(" ")[1],"小卡一张")

!paddlex --export_inference --model_dir=/home/aistudio/output/resnet50_vd_ssld/best_model --save_dir=./inference_model

from tkinter import *from PIL import Image, ImageTk, ImageSequenceimport timeimport  tkinterimport requestsimport osimport randomimport matplotlib.pyplot as plt  # plt 用于显示图片import matplotlib.image as mpimg  # mpimg 用于读取图片root = Tk()
root.geometry("1000x1000")
root.title('韩女团小卡扭蛋机')

gif_label = Label(root,image="")
gif_label.pack()


img = PhotoImage(file=".\nvtuan\1.gif")#按钮设置B = tkinter.Button(root, width=1000, height=1000, text='抽取扭蛋', relief=RIDGE, font=('Helvetica', 100),
                   bg='black', fg='white', image=img, borderwidth=50, compound="center",command=lambda :animation(count))

B.pack()#画布设置canvas = Canvas(root, width=1000, height=1000, bg='white')
canvas.pack()

im = Image.open('.\nvtuan\1.gif')# GIF图片流的迭代器frames = im.n_frames  # gives total number of frames that gif contains# creating list of PhotoImage objects for each framesim = [PhotoImage(file='.\nvtuan\1.gif', format=f"gif -index {i}") for i in range(frames)]
count = 0anim = Nonedef animation(count):
    global anim
    im2 = im[count]
    gif_label.configure(image=im2)
    count += 1
    if count == frames:          #动画结束后进行的操作
        headers = {'content-type': 'charset=gb2312'}        # 标题显示中文
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.rcParams['axes.unicode_minus'] = False

        fileDir = 'nvtuan'
        pathDir = os.listdir(fileDir)  # 取图片的原始路径
        picknumber = int(1)  # 从文件夹中取一张数量图片
        sample = random.sample(pathDir, picknumber)  # 随机选取picknumber数量的样本图片

        with open(os.path.join(fileDir, sample[0]), 'rb') as f:
            img = f.read()
        result = requests.post('http://127.0.0.1:24401/', params={'threshold': 0.1}, data=img, headers=headers).json()

        c_dict = {'JOY': '朴秀荣', 'MEIYAN': '赵美延', 'ROSE': '朴彩英', 'XUEXUAN': '金雪炫'} #英文标签映射中文

        a = mpimg.imread(os.path.join(fileDir, sample[0]))
        plt.figure(figsize=(10, 10))
        plt.title("恭喜你获得:" + c_dict.get(result['results'][0]['label'].split(" ")[1]) + "小卡一张", fontsize='xx-large',
                  fontweight='heavy', bbox=dict(facecolor='y', edgecolor='black', alpha=0.65))
        plt.imshow(a)
        plt.axis('off')
        plt.show()

        quit()

    anim = root.after(800, lambda: animation(count))



root.mainloop()