Python 官方文档：入门教程 => 点击学习
目录 前言 一、难点分析 二、实现流程 1.DNF窗口位置获取 2.获取训练数据 3.数据标注 4.数据格式转换 5.数据训练 5.刷图逻辑编写 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
目录
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
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。
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.bottomhid = 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)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
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
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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\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 1017,544,1091,572,2C:\Users\Administrator\Desktop\dnfimg\dnf-e2bbbe68-605b-4021-960b-e75c9f01dcd1.jpg 1207,452,1312,690,0 1080,446,1174,658,1 299,628,363,663,2 879,397,1071,474,1C:\Users\Administrator\Desktop\dnfimg\dnf-e2bbbe68-605b-4021-960b-e75c9f01dcd1.jpg 1207,452,1312,690,0 1080,446,1174,658,1 299,628,363,663,2 879,397,1071,474,1C:\Users\Administrator\Desktop\dnfimg\dnf-e2bbbe68-605b-4021-960b-e75c9f01dcd1.jpg 1207,452,1312,690,0 1080,446,1174,658,1 299,628,363,663,2 879,397,1071,474,1C:\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 将yolov7代码封装到fme的pythoncaller中。
import fmeimport fmeobjectsimport datetimeimport osfrom functools import partialimport tensorflow as tfimport tensorflow.keras.backend as Kfrom tensorflow.keras.callbacks import (EarlyStopping, LearningRateScheduler, TensorBoard)from tensorflow.keras.optimizers import SGD, Adamfrom nets.yolo import get_train_model, yolo_bodyfrom nets.yolo_training import get_lr_schedulerfrom utils.callbacks import LossHistory, ModelCheckpoint, EvalCallbackfrom utils.dataloader import YoloDatasetsfrom utils.utils import get_anchors, get_classes, show_configfrom utils.utils_fit import fit_one_epochos.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 = Truemosaic_prob = 0.5mixup = Truemixup_prob = 0.5special_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 = 0Freeze_Epoch = 50Freeze_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 = 50Unfreeze_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-2Min_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 = 0.937weight_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 = Trueeval_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 = Noneprint('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。
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_listdef recognize(img): ocr = ddddocr.DdddOcr() res = ocr.classification(img) return resclass 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。
来源地址：https://blog.csdn.net/weixin_57664381/article/details/128825062
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本文标题: FME+YOLOV7写DNF自动刷图脚本
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