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FME+YOLOV7写DNF自动刷图脚本


前言

这是一篇不务正业的研究,首先说明,这不是外挂!这不是外挂!这不是外挂!这只是用ai做图像识别、目标检测然后通过模拟键鼠实现的一个外部自动化脚本。求生欲极强!哈哈哈哈


一、难点分析

    在不读取内存又想拿到信息的情况下,只有走图像识别一条路了。一个完整的刷图应该包括打怪,拾取物品,找门过图。那么YOLOV7的轻量级框架能支持140fps的图像实时解析,必定非常符合我们的要求。

    剩下的难点就是怎么让人物移动的固定坐标点,怎么设计打怪逻辑,怎么读取技能cd时间让人物合理释放技能。

二、实现流程

1.DNF窗口位置获取

这里当然是使用过pywin32是快捷的,下载一个spy++,拿到dnf窗口句柄,然后用过win32gui来获取窗口坐标。


def get_window_rect(hwnd):
    try:
        f = ctypes.windll.dwmapi.DwmGetWindowAttribute
    except WindowsError:
        f = None
    if f:
        rect = ctypes.wintypes.RECT()
        DWMWA_EXTENDED_FRAME_BOUNDS = 9
        f(ctypes.wintypes.HWND(hwnd),
          ctypes.wintypes.DWORD(DWMWA_EXTENDED_FRAME_BOUNDS),
          ctypes.byref(rect),
          ctypes.sizeof(rect)
          )
        return rect.left, rect.top, rect.right, rect.bottom

hid = win32gui.FindWindow("地下城与勇士", "地下城与勇士:创新世纪")
left, top, right, bottom = get_window_rect(hid)

2.获取训练数据

拿到DNF窗口位置后,我们需要截屏具体位置来获取训练的图像,截屏我们使用pyautogui这个库来完成,因为这个库非常强大,能实现0.004秒一张图截屏速度,只需要手动刷一遍图,就能截取大量素材。

im = pyautogui.screenshot(region=[left, top, abs(right - left), abs(top - bottom)])

然后我们拿到了大量的图片

3.数据标注

这就到了整个环节最痛苦的流程了,使用labme工具标注数据,标注门、物品、角色、怪物

4.数据格式转换

labme标注完成后,会导出一个json文件,为了将json文件转换成标准训练集数据格式,我们用fme写了一个模板来完成数据转换。

转换前数据:

{
  "version": "4.5.6",
  "flags": {},
  "shapes": [
    {
      "label": "i",
      "points": [
        [
          541.21768707483,
          298.85034013605446
        ],
        [
          642.578231292517,
          428.78231292517006
        ]
      ],
      "group_id": null,
      "shape_type": "rectangle",
      "flags": {}
    },
    {
      "label": "guai",
      "points": [
        [
          267.7482993197279,
          228.10204081632654
        ],
        [
          380.6734693877551,
          379.12244897959187
        ]
      ],
      "group_id": null,
      "shape_type": "rectangle",
      "flags": {}
    }
  ],
  "imagePath": "dnf-16773414691978383.jpg",
  "imageData": "",
  "imageHeight": 600,
  "imageWidth": 1067
}

转换后数据:(将数据用路径+标注类别+坐标表示)

942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-73bc62b1-f305-4a76-809e-72f7564a9633.jpg 639,413,771,677,0 746,625,817,653,2 977,598,1174,632,2 942,619,996,635,2 786,609,961,630,2 824,563,879,589,2 880,531,1004,555,2 1017,544,1091,572,2
C:\Users\Administrator\Desktop\dnfimg\dnf-e2bbbe68-605b-4021-960b-e75c9f01dcd1.jpg 1207,452,1312,690,0 1080,446,1174,658,1 299,628,363,663,2 879,397,1071,474,1
C:\Users\Administrator\Desktop\dnfimg\dnf-e2bbbe68-605b-4021-960b-e75c9f01dcd1.jpg 1207,452,1312,690,0 1080,446,1174,658,1 299,628,363,663,2 879,397,1071,474,1
C:\Users\Administrator\Desktop\dnfimg\dnf-e2bbbe68-605b-4021-960b-e75c9f01dcd1.jpg 1207,452,1312,690,0 1080,446,1174,658,1 299,628,363,663,2 879,397,1071,474,1
C:\Users\Administrator\Desktop\dnfimg\dnf-e2bbbe68-605b-4021-960b-e75c9f01dcd1.jpg 1207,452,1312,690,0 1080,446,1174,658,1 299,628,363,663,2 

5.数据训练

将yolov7代码封装到fme的pythoncaller中。

import fme
import fmeobjects
import datetime
import os
from functools import partial

import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.callbacks import (EarlyStopping, LearningRateScheduler,
                                        TensorBoard)
from tensorflow.keras.optimizers import SGD, Adam

from nets.yolo import get_train_model, yolo_body
from nets.yolo_training import get_lr_scheduler
from utils.callbacks import LossHistory, ModelCheckpoint, EvalCallback
from utils.dataloader import YoloDatasets
from utils.utils import get_anchors, get_classes, show_config
from utils.utils_fit import fit_one_epoch

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
eager           = False
#---------------------------------------------------------------------#
#   train_gpu   训练用到的GPU
#               默认为第一张卡、双卡为[0, 1]、三卡为[0, 1, 2]
#               在使用多GPU时,每个卡上的batch为总batch除以卡的数量。
#---------------------------------------------------------------------#
train_gpu       = [0,]
#---------------------------------------------------------------------#
#   classes_path    指向model_data下的txt,与自己训练的数据集相关 
#                   训练前一定要修改classes_path,使其对应自己的数据集
#---------------------------------------------------------------------#
classes_path    = 'model_data/voc_classes.txt'
#---------------------------------------------------------------------#
#   anchors_path    代表先验框对应的txt文件,一般不修改。
#   anchors_mask    用于帮助代码找到对应的先验框,一般不修改。
#---------------------------------------------------------------------#
anchors_path    = 'model_data/yolo_anchors.txt'
anchors_mask    = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
#----------------------------------------------------------------------------------------------------------------------------#
#   权值文件的下载请看README,可以通过网盘下载。模型的 预训练权重 对不同数据集是通用的,因为特征是通用的。
#   模型的 预训练权重 比较重要的部分是 主干特征提取网络的权值部分,用于进行特征提取。
#   预训练权重对于99%的情况都必须要用,不用的话主干部分的权值太过随机,特征提取效果不明显,网络训练的结果也不会好
#
#   如果训练过程中存在中断训练的操作,可以将model_path设置成logs文件夹下的权值文件,将已经训练了一部分的权值再次载入。
#   同时修改下方的 冻结阶段 或者 解冻阶段 的参数,来保证模型epoch的连续性。
#   
#   当model_path = ''的时候不加载整个模型的权值。
#
#   此处使用的是整个模型的权重,因此是在train.py进行加载的。
#   如果想要让模型从0开始训练,则设置model_path = '',下面的Freeze_Train = Fasle,此时从0开始训练,且没有冻结主干的过程。
#   
#   一般来讲,网络从0开始的训练效果会很差,因为权值太过随机,特征提取效果不明显,因此非常、非常、非常不建议大家从0开始训练!
#   从0开始训练有两个方案:
#   1、得益于Mosaic数据增强方法强大的数据增强能力,将UnFreeze_Epoch设置的较大(300及以上)、batch较大(16及以上)、数据较多(万以上)的情况下,
#      可以设置mosaic=True,直接随机初始化参数开始训练,但得到的效果仍然不如有预训练的情况。(像COCO这样的大数据集可以这样做)
#   2、了解imagenet数据集,首先训练分类模型,获得网络的主干部分权值,分类模型的 主干部分 和该模型通用,基于此进行训练。
#----------------------------------------------------------------------------------------------------------------------------#
model_path      = 'model_data/best_epoch_weights.h5'
#------------------------------------------------------#
#   input_shape     输入的shape大小,一定要是32的倍数
#------------------------------------------------------#
input_shape     = [640, 640]
#------------------------------------------------------#
#   phi             所使用的YoloV7的版本。l、x
#------------------------------------------------------#
phi             = 'l'
#------------------------------------------------------------------#
#   mosaic              马赛克数据增强。
#   mosaic_prob         每个step有多少概率使用mosaic数据增强,默认50%。
#
#   mixup               是否使用mixup数据增强,仅在mosaic=True时有效。
#                       只会对mosaic增强后的图片进行mixup的处理。
#   mixup_prob          有多少概率在mosaic后使用mixup数据增强,默认50%。
#                       总的mixup概率为mosaic_prob * mixup_prob。
#
#   special_aug_ratio   参考YoloX,由于Mosaic生成的训练图片,远远脱离自然图片的真实分布。
#                       当mosaic=True时,本代码会在special_aug_ratio范围内开启mosaic。
#                       默认为前70%个epoch,100个世代会开启70个世代。
#------------------------------------------------------------------#
mosaic              = True
mosaic_prob         = 0.5
mixup               = True
mixup_prob          = 0.5
special_aug_ratio   = 0.7
#------------------------------------------------------------------#
#   label_smoothing     标签平滑。一般0.01以下。如0.01、0.005。
#------------------------------------------------------------------#
label_smoothing     = 0

#----------------------------------------------------------------------------------------------------------------------------#
#   训练分为两个阶段,分别是冻结阶段和解冻阶段。设置冻结阶段是为了满足机器性能不足的同学的训练需求。
#   冻结训练需要的显存较小,显卡非常差的情况下,可设置Freeze_Epoch等于UnFreeze_Epoch,Freeze_Train = True,此时仅仅进行冻结训练。
#      
#   在此提供若干参数设置建议,各位训练者根据自己的需求进行灵活调整:
#   (一)从整个模型的预训练权重开始训练: 
#       Adam:
#           Init_Epoch = 0,Freeze_Epoch = 50,UnFreeze_Epoch = 100,Freeze_Train = True,optimizer_type = 'adam',Init_lr = 1e-3,weight_decay = 0。(冻结)
#           Init_Epoch = 0,UnFreeze_Epoch = 100,Freeze_Train = False,optimizer_type = 'adam',Init_lr = 1e-3,weight_decay = 0。(不冻结)
#       SGD:
#           Init_Epoch = 0,Freeze_Epoch = 50,UnFreeze_Epoch = 300,Freeze_Train = True,optimizer_type = 'sgd',Init_lr = 1e-2,weight_decay = 5e-4。(冻结)
#           Init_Epoch = 0,UnFreeze_Epoch = 300,Freeze_Train = False,optimizer_type = 'sgd',Init_lr = 1e-2,weight_decay = 5e-4。(不冻结)
#       其中:UnFreeze_Epoch可以在100-300之间调整。
#   (二)从0开始训练:
#       Init_Epoch = 0,UnFreeze_Epoch >= 300,Unfreeze_batch_size >= 16,Freeze_Train = False(不冻结训练)
#       其中:UnFreeze_Epoch尽量不小于300。optimizer_type = 'sgd',Init_lr = 1e-2,mosaic = True。
#   (三)batch_size的设置:
#       在显卡能够接受的范围内,以大为好。显存不足与数据集大小无关,提示显存不足(OOM或者CUDA out of memory)请调小batch_size。
#       受到BatchNorm层影响,batch_size最小为2,不能为1。
#       正常情况下Freeze_batch_size建议为Unfreeze_batch_size的1-2倍。不建议设置的差距过大,因为关系到学习率的自动调整。
#----------------------------------------------------------------------------------------------------------------------------#
#------------------------------------------------------------------#
#   冻结阶段训练参数
#   此时模型的主干被冻结了,特征提取网络不发生改变
#   占用的显存较小,仅对网络进行微调
#   Init_Epoch          模型当前开始的训练世代,其值可以大于Freeze_Epoch,如设置:
#                       Init_Epoch = 60、Freeze_Epoch = 50、UnFreeze_Epoch = 100
#                       会跳过冻结阶段,直接从60代开始,并调整对应的学习率。
#                       (断点续练时使用)
#   Freeze_Epoch        模型冻结训练的Freeze_Epoch
#                       (当Freeze_Train=False时失效)
#   Freeze_batch_size   模型冻结训练的batch_size
#                       (当Freeze_Train=False时失效)
#------------------------------------------------------------------#
Init_Epoch          = 0
Freeze_Epoch        = 50
Freeze_batch_size   = 14
#------------------------------------------------------------------#
#   解冻阶段训练参数
#   此时模型的主干不被冻结了,特征提取网络会发生改变
#   占用的显存较大,网络所有的参数都会发生改变
#   UnFreeze_Epoch          模型总共训练的epoch
#                           SGD需要更长的时间收敛,因此设置较大的UnFreeze_Epoch
#                           Adam可以使用相对较小的UnFreeze_Epoch
#   Unfreeze_batch_size     模型在解冻后的batch_size
#------------------------------------------------------------------#
UnFreeze_Epoch      = 50
Unfreeze_batch_size = 4
#------------------------------------------------------------------#
#   Freeze_Train    是否进行冻结训练
#                   默认先冻结主干训练后解冻训练。
#------------------------------------------------------------------#
Freeze_Train        = True

#------------------------------------------------------------------#
#   其它训练参数:学习率、优化器、学习率下降有关
#------------------------------------------------------------------#
#------------------------------------------------------------------#
#   Init_lr         模型的最大学习率
#                   当使用Adam优化器时建议设置  Init_lr=1e-3
#                   当使用SGD优化器时建议设置   Init_lr=1e-2
#   Min_lr          模型的最小学习率,默认为最大学习率的0.01
#------------------------------------------------------------------#
Init_lr             = 1e-2
Min_lr              = Init_lr * 0.01
#------------------------------------------------------------------#
#   optimizer_type  使用到的优化器种类,可选的有adam、sgd
#                   当使用Adam优化器时建议设置  Init_lr=1e-3
#                   当使用SGD优化器时建议设置   Init_lr=1e-2
#   momentum        优化器内部使用到的momentum参数
#   weight_decay    权值衰减,可防止过拟合
#                   adam会导致weight_decay错误,使用adam时建议设置为0。
#------------------------------------------------------------------#
optimizer_type      = "sgd"
momentum            = 0.937
weight_decay        = 5e-4
#------------------------------------------------------------------#
#   lr_decay_type   使用到的学习率下降方式,可选的有'step'、'cos'
#------------------------------------------------------------------#
lr_decay_type       = 'cos'
#------------------------------------------------------------------#
#   save_period     多少个epoch保存一次权值
#------------------------------------------------------------------#
save_period         = 10
#------------------------------------------------------------------#
#   save_dir        权值与日志文件保存的文件夹
#------------------------------------------------------------------#
save_dir            = 'logs'
#------------------------------------------------------------------#
#   eval_flag       是否在训练时进行评估,评估对象为验证集
#                   安装pycocotools库后,评估体验更佳。
#   eval_period     代表多少个epoch评估一次,不建议频繁的评估
#                   评估需要消耗较多的时间,频繁评估会导致训练非常慢
#   此处获得的mAP会与get_map.py获得的会有所不同,原因有二:
#   (一)此处获得的mAP为验证集的mAP。
#   (二)此处设置评估参数较为保守,目的是加快评估速度。
#------------------------------------------------------------------#
eval_flag           = True
eval_period         = 10
#------------------------------------------------------------------#
#   num_workers     用于设置是否使用多线程读取数据,1代表关闭多线程
#                   开启后会加快数据读取速度,但是会占用更多内存
#                   keras里开启多线程有些时候速度反而慢了许多
#                   在IO为瓶颈的时候再开启多线程,即GPU运算速度远大于读取图片的速度。
#------------------------------------------------------------------#
num_workers         = 1

#------------------------------------------------------#
#   train_annotation_path   训练图片路径和标签
#   val_annotation_path     验证图片路径和标签
#------------------------------------------------------#
train_annotation_path   = '2007_train.txt'
val_annotation_path     = '2007_val.txt'

#------------------------------------------------------#
#   设置用到的显卡
#------------------------------------------------------#
os.environ["CUDA_VISIBLE_DEVICES"]  = ','.join(str(x) for x in train_gpu)
ngpus_per_node                      = len(train_gpu)

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

#------------------------------------------------------#
#   判断当前使用的GPU数量与机器上实际的GPU数量
#------------------------------------------------------#
if ngpus_per_node > 1 and ngpus_per_node > len(gpus):
    raise ValueError("The number of GPUs specified for training is more than the GPUs on the machine")
    
if ngpus_per_node > 1:
    strategy = tf.distribute.MirroredStrategy()
else:
    strategy = None
print('Number of devices: {}'.format(ngpus_per_node))
class FeatureProcessor(object):
    """Template Class Interface:
    When using this class, make sure its name is set as the value of the 'Class
    to Process Features' transformer parameter.
    """

    def __init__(self):
        """Base constructor for class members."""
        pass

    def input(self, feature):
        
        class_names, num_classes = get_classes(classes_path)
        print("类名{},类数量{}".format(class_names, num_classes))
        anchors, num_anchors     = get_anchors(anchors_path)

        #----------------------------------------------------#
        #   判断是否多GPU载入模型和预训练权重
        #----------------------------------------------------#

        if True:
            #------------------------------------------------------#
            #   创建yolo模型
            #------------------------------------------------------#
            model_body  = yolo_body((None, None, 3), anchors_mask, num_classes, phi, weight_decay)
            if model_path != '':
                pass
                #------------------------------------------------------#
                #   载入预训练权重
                #------------------------------------------------------#
              #  print('Load weights {}.'.format(model_path))
               # model_body.load_weights(model_path, by_name=True, skip_mismatch=True)
            if not eager:
                model = get_train_model(model_body, input_shape, num_classes, anchors, anchors_mask, label_smoothing)
        model.summary()
        #---------------------------#
        #   读取数据集对应的txt
        #---------------------------#
        with open(train_annotation_path, encoding='utf-8') as f:
            train_lines = f.readlines()
        with open(val_annotation_path, encoding='utf-8') as f:
            val_lines   = f.readlines()
        num_train   = len(train_lines)
        num_val     = len(val_lines)

        show_config(
            classes_path = classes_path, anchors_path = anchors_path, anchors_mask = anchors_mask, model_path = model_path, input_shape = input_shape, \
            Init_Epoch = Init_Epoch, Freeze_Epoch = Freeze_Epoch, UnFreeze_Epoch = UnFreeze_Epoch, Freeze_batch_size = Freeze_batch_size, Unfreeze_batch_size = Unfreeze_batch_size, Freeze_Train = Freeze_Train, \
            Init_lr = Init_lr, Min_lr = Min_lr, optimizer_type = optimizer_type, momentum = momentum, lr_decay_type = lr_decay_type, \
            save_period = save_period, save_dir = save_dir, num_workers = num_workers, num_train = num_train, num_val = num_val
        )
        #---------------------------------------------------------#
        #   总训练世代指的是遍历全部数据的总次数
        #   总训练步长指的是梯度下降的总次数 
        #   每个训练世代包含若干训练步长,每个训练步长进行一次梯度下降。
        #   此处仅建议最低训练世代,上不封顶,计算时只考虑了解冻部分
        #----------------------------------------------------------#
        wanted_step = 5e4 if optimizer_type == "sgd" else 1.5e4
        total_step  = num_train // Unfreeze_batch_size * UnFreeze_Epoch
        if total_step <= wanted_step:
            if num_train // Unfreeze_batch_size == 0:
                raise ValueError('数据集过小,无法进行训练,请扩充数据集。')
            wanted_epoch = wanted_step // (num_train // Unfreeze_batch_size) + 1
            print("\n\033[1;33;44m[Warning] 使用%s优化器时,建议将训练总步长设置到%d以上。\033[0m"%(optimizer_type, wanted_step))
            print("\033[1;33;44m[Warning] 本次运行的总训练数据量为%d,Unfreeze_batch_size为%d,共训练%d个Epoch,计算出总训练步长为%d。\033[0m"%(num_train, Unfreeze_batch_size, UnFreeze_Epoch, total_step))
            print("\033[1;33;44m[Warning] 由于总训练步长为%d,小于建议总步长%d,建议设置总世代为%d。\033[0m"%(total_step, wanted_step, wanted_epoch))

        #------------------------------------------------------#
        #   主干特征提取网络特征通用,冻结训练可以加快训练速度
        #   也可以在训练初期防止权值被破坏。
        #   Init_Epoch为起始世代
        #   Freeze_Epoch为冻结训练的世代
        #   UnFreeze_Epoch总训练世代
        #   提示OOM或者显存不足请调小Batch_size
        #------------------------------------------------------#
        if True:
            if Freeze_Train:
                freeze_layers = {'n':118, 's': 118, 'm': 167, 'l': 216, 'x': 265}[phi]
                #print(freeze_layers)
                for i in range(50): model_body.layers[i].trainable = False
               # print('Freeze the first {} layers of total {} layers.'.format(freeze_layers, len(model_body.layers)))
                
            #-------------------------------------------------------------------#
            #   如果不冻结训练的话,直接设置batch_size为Unfreeze_batch_size
            #-------------------------------------------------------------------#
            batch_size  = Freeze_batch_size if Freeze_Train else Unfreeze_batch_size
            
            #-------------------------------------------------------------------#
            #   判断当前batch_size,自适应调整学习率
            #-------------------------------------------------------------------#
            nbs             = 64
            lr_limit_max    = 1e-3 if optimizer_type == 'adam' else 5e-2
            lr_limit_min    = 3e-4 if optimizer_type == 'adam' else 5e-4
            Init_lr_fit     = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max)
            Min_lr_fit      = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)

            #---------------------------------------#
            #   获得学习率下降的公式
            #---------------------------------------#
            lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)

            epoch_step      = num_train // batch_size
            epoch_step_val  = num_val // batch_size

            if epoch_step == 0 or epoch_step_val == 0:
                raise ValueError('数据集过小,无法进行训练,请扩充数据集。')

            train_dataloader    = YoloDatasets(train_lines, input_shape, anchors, batch_size, num_classes, anchors_mask, Init_Epoch, UnFreeze_Epoch, \
                                                mosaic=mosaic, mixup=mixup, mosaic_prob=mosaic_prob, mixup_prob=mixup_prob, train=True, special_aug_ratio=special_aug_ratio)
            val_dataloader      = YoloDatasets(val_lines, input_shape, anchors, batch_size, num_classes, anchors_mask, Init_Epoch, UnFreeze_Epoch, \
                                                mosaic=False, mixup=False, mosaic_prob=0, mixup_prob=0, train=False, special_aug_ratio=0)

            optimizer = {
                'adam'  : Adam(lr = Init_lr, beta_1 = momentum),
                'sgd'   : SGD(lr = Init_lr, momentum = momentum, nesterov=True)
            }[optimizer_type]
            
            if eager:
                start_epoch     = Init_Epoch
                end_epoch       = UnFreeze_Epoch
                UnFreeze_flag   = False

                gen     = tf.data.Dataset.from_generator(partial(train_dataloader.generate), (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32))
                gen_val = tf.data.Dataset.from_generator(partial(val_dataloader.generate), (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32))

                gen     = gen.shuffle(buffer_size = batch_size).prefetch(buffer_size = batch_size)
                gen_val = gen_val.shuffle(buffer_size = batch_size).prefetch(buffer_size = batch_size)
                
                if ngpus_per_node > 1:
                    gen     = strategy.experimental_distribute_dataset(gen)
                    gen_val = strategy.experimental_distribute_dataset(gen_val)

                time_str        = datetime.datetime.strftime(datetime.datetime.now(),'%Y_%m_%d_%H_%M_%S')
                log_dir         = os.path.join(save_dir, "loss_" + str(time_str))
                loss_history    = LossHistory(log_dir)
                eval_callback   = EvalCallback(model_body, input_shape, anchors, anchors_mask, class_names, num_classes, val_lines, log_dir, \
                                                eval_flag=eval_flag, period=eval_period)
                #---------------------------------------#
                #   开始模型训练
                #---------------------------------------#
                for epoch in range(start_epoch, end_epoch):
                    #---------------------------------------#
                    #   如果模型有冻结学习部分
                    #   则解冻,并设置参数
                    #---------------------------------------#
                    if epoch >= Freeze_Epoch and not UnFreeze_flag and Freeze_Train:
                        batch_size      = Unfreeze_batch_size

                        #-------------------------------------------------------------------#
                        #   判断当前batch_size,自适应调整学习率
                        #-------------------------------------------------------------------#
                        nbs             = 64
                        lr_limit_max    = 1e-3 if optimizer_type == 'adam' else 5e-2
                        lr_limit_min    = 3e-4 if optimizer_type == 'adam' else 5e-4
                        Init_lr_fit     = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max)
                        Min_lr_fit      = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)
                        #---------------------------------------#
                        #   获得学习率下降的公式
                        #---------------------------------------#
                        lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)

                        for i in range(len(model_body.layers)): 
                            model_body.layers[i].trainable = True

                        epoch_step      = num_train // batch_size
                        epoch_step_val  = num_val // batch_size

                        if epoch_step == 0 or epoch_step_val == 0:
                            raise ValueError("数据集过小,无法继续进行训练,请扩充数据集。")

                        train_dataloader.batch_size    = batch_size
                        val_dataloader.batch_size      = batch_size

                        gen     = tf.data.Dataset.from_generator(partial(train_dataloader.generate), (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32))
                        gen_val = tf.data.Dataset.from_generator(partial(val_dataloader.generate), (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32))

                        gen     = gen.shuffle(buffer_size = batch_size).prefetch(buffer_size = batch_size)
                        gen_val = gen_val.shuffle(buffer_size = batch_size).prefetch(buffer_size = batch_size)
                
                        if ngpus_per_node > 1:
                            gen     = strategy.experimental_distribute_dataset(gen)
                            gen_val = strategy.experimental_distribute_dataset(gen_val)
                        
                        UnFreeze_flag = True

                    lr = lr_scheduler_func(epoch)
                    K.set_value(optimizer.lr, lr)

                    fit_one_epoch(model_body, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, 
                                end_epoch, input_shape, anchors, anchors_mask, num_classes, label_smoothing, save_period, save_dir, strategy)
                                
                    train_dataloader.on_epoch_end()
                    val_dataloader.on_epoch_end()
            else:
                start_epoch = Init_Epoch
                end_epoch   = Freeze_Epoch if Freeze_Train else UnFreeze_Epoch

                if ngpus_per_node > 1:
                    with strategy.scope():
                        model.compile(optimizer = optimizer, loss={'yolo_loss': lambda y_true, y_pred: y_pred})
                else:
                    model.compile(optimizer = optimizer, loss={'yolo_loss': lambda y_true, y_pred: y_pred})
                #-------------------------------------------------------------------------------#
                #   训练参数的设置
                #   logging         用于设置tensorboard的保存地址
                #   checkpoint      用于设置权值保存的细节,period用于修改多少epoch保存一次
                #   lr_scheduler       用于设置学习率下降的方式
                #   early_stopping  用于设定早停,val_loss多次不下降自动结束训练,表示模型基本收敛
                #-------------------------------------------------------------------------------#
                model.load_weights(model_path)
                time_str        = datetime.datetime.strftime(datetime.datetime.now(),'%Y_%m_%d_%H_%M_%S')
                log_dir         = os.path.join(save_dir, "loss_" + str(time_str))
                logging         = TensorBoard(log_dir)
                loss_history    = LossHistory(log_dir)
                checkpoint      = ModelCheckpoint(os.path.join(save_dir, "ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5"), 
                                        monitor = 'val_loss', save_weights_only = True, save_best_only = False, period = save_period)
                checkpoint_last = ModelCheckpoint(os.path.join(save_dir, "last_epoch_weights.h5"), 
                                        monitor = 'val_loss', save_weights_only = True, save_best_only = False, period = 1)
                checkpoint_best = ModelCheckpoint(os.path.join(save_dir, "best_epoch_weights.h5"), 
                                        monitor = 'val_loss', save_weights_only = True, save_best_only = True, period = 1)
                early_stopping  = EarlyStopping(monitor='val_loss', min_delta = 0, patience = 10, verbose = 1)
                lr_scheduler    = LearningRateScheduler(lr_scheduler_func, verbose = 1)
                eval_callback   = EvalCallback(model_body, input_shape, anchors, anchors_mask, class_names, num_classes, val_lines, log_dir, \
                                                eval_flag=eval_flag, period=eval_period)
                callbacks       = [logging, loss_history, checkpoint, checkpoint_last, checkpoint_best, lr_scheduler, eval_callback]

                if start_epoch < end_epoch:
                    print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
                    model.fit(
                        x                   = train_dataloader,
                        steps_per_epoch     = epoch_step,
                        validation_data     = val_dataloader,
                        validation_steps    = epoch_step_val,
                        epochs              = end_epoch,
                        initial_epoch       = start_epoch,
                        use_multiprocessing = True if num_workers > 1 else False,
                        workers             = num_workers,
                        callbacks           = callbacks
                    )
            

        self.pyoutput(feature)

    def close(self):
        """This method is called once all the FME Features have been processed
        from input().
        """
        pass

    def process_group(self):
        """When 'Group By' attribute(s) are specified, this method is called 
        once all the FME Features in a current group have been sent to input().

        FME Features sent to input() should generally be cached for group-by 
        processing in this method when knowledge of all Features is required. 
        The resulting Feature(s) from the group-by processing should be emitted 
        through self.pyoutput().

        FME will continue calling input() a number of times followed
        by process_group() for each 'Group By' attribute, so this 
        implementation should reset any class members for the next group.
        """
        pass

开始训练

训练需要注意几个事项,首先是需要加载主干网络预训练权重,然后是训练50个epoch后,冻结模型部分层继续训练,使得模型能更加匹配数据。

5.刷图逻辑编写

这里我们需要自己做两个类,一个键鼠控制类,一个是人物行为类。以下是部分类代码

def get_thing(yolo_list):
    door_list = []
    guai_list = []
    wuping_list = []
    person_xy = []
    if len(yolo_list) != 0:
        # 解析当前状态
        for i in yolo_list:
            if i["label"] == str("i"):
                # 获取人物所在屏幕真实坐标点
                person_x = (i["right"] + i["left"]) / 2
                person_y = i["bottom"]
                person_xy.append(person_x)
                person_xy.append(person_y)
            if "door" in str(i["label"]):
                # 获取门所在真实坐标点
                door_x = (i["right"] + i["left"]) / 2
                door_y = i["bottom"] - 10
                door_list.append([door_x, door_y])
            if "guai" in str(i["label"]):
                # 获取门所在真实坐标点

                guai_x = (i["right"] + i["left"]) / 2
                guai_y = i["bottom"] - 30
                guai_list.append([guai_x, guai_y])

            if "wuping" in str(i["label"]):
                # 获取物品所在真实坐标点
                wuping_x = (i["right"] + i["left"]) / 2
                wuping_y = i["bottom"] + 33
                wuping_list.append([wuping_x, wuping_y])

    return person_xy,door_list,guai_list,wuping_list

def recognize(img):
    ocr = ddddocr.DdddOcr()
    res = ocr.classification(img)
    return res
class Action(object):
    """
    -------------------------------------------------------------------------
    该类为dnf人物角色动作类,目前适配大部分职业
    -------------------------------------------------------------------------
    """
    def __init__(self, dnf_win_box,speed):
        self.dnf_win_box = dnf_win_box
        self.speed = speed
        self.skill_button = ["q", "w", "e", "r", "t", "y", "a", "s", "d", "f", "h", "ctrl","alt"]

        pass
    def buff(self):
        """添加角色buff,默认右右空格,上上空格,上下空格,左右空格都按一遍"""
        pydirectinput.press(['right','right','space'])
        pydirectinput.press(['up', 'up', 'space'])
        #pydirectinput.press(['right', 'right', 'space'])
        pydirectinput.press(['down', 'down', 'space'])
        pydirectinput.press(['left', 'right', 'z'])

        pass
    def move_to_wuping(self,target_xy,person_xy):
        """输入目标坐标,人物会移动到该坐标"""
        speed=self.speed

        target_x=target_xy[0]
        target_y = target_xy[1]
        person_x = person_xy[0]
        person_y = person_xy[1]
        if target_x - person_x > 30:
            x_button_name = "right"
            time1 = abs(target_x - person_x) / (400*speed)
            pydirectinput.keyDown(x_button_name)
            time.sleep(time1)
            pydirectinput.keyUp(x_button_name)
        elif target_x - person_x < -30:
            x_button_name = "le

然后就是角色cd判定机制,为了能匹配所有职业,我选择再做一个轻量级的ai神经网络来干这个事情。

截取各种角色的技能图标作为训练集

搭建轻量级网络进行模型训练

然后写一个技能判断的类,完成技能自动识别

def getcdpic(img,model):
    h=47
    next_img = img.crop((649, 796, 977, 895))
    buttonlist1=["q","w","e","r","t","y","ctrl"]
    buttonlist2=["a","s","d","f","g","h","alt"]
    new_buttonlist=[]
    for i in range(14):
        if i <=6:
            aa=next_img.crop((0+(i*h),0,h+(i*h),h))
            b= havecd(aa,model)
            if b == 1:
                new_buttonlist.append(buttonlist1[i])
        else:
            i=i-7
            aa=next_img.crop((0+(i*h),h,h+(i*h),h*2))
            b= havecd(aa,model)
            if b == 1:
                new_buttonlist.append(buttonlist2[i])
    return new_buttonlist

最后就是整体的刷图逻辑,包括过图,角色切换等

通过FME多层循环,来保证整体的流程控制,最终实现只需要输入,刷图的角色数量,即可完成搬砖自动化


总结

    该研究仅为个人学习研究使用,主要为展示FME在深度学习领域的作用。代码只展示部分,不接受任何形式的购买行为。前前后后用空闲时间折腾了几个月,算是完成了一个有趣的课题研究。yolo真的是一个非常牛逼的算法,最近才推出了yolov8,性能和精度都获得了较大提升。
标签: YOLO 自动化 python

本文转载自: https://blog.csdn.net/weixin_57664381/article/details/128825062
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