设置训练和验证集路径 train_vol_path = "data/train/trainvol" train_seg_path = "data/train/trainseg" val_vol_path = "data/val/valvol" val_seg_path = "data/val/valseg" # 定义数据生成器 train_datagen = ImageDataGenerator(rescale=1./255) val_datagen = ImageDataGenerator(rescale=1./255) # 加载训练和验证数据 train_vol = train_datagen.flow_from_directory(train_vol_path, target_size=(256, 256), batch_size=32, class_mode=None) train_seg = train_datagen.flow_from_directory(train_seg_path, target_size=(256, 256), batch_size=32, class_mode=None) val_vol = val_datagen.flow_from_directory(val_vol_path, target_size=(256, 256), batch_size=32, class_mode=None) val_seg = val_datagen.flow_from_directory(val_seg_path, target_size=(256, 256), batch_size=32, class_mode=None) # 合并训练和验证数据 train_generator = zip(train_vol, train_seg) val_generator = zip(val_vol, val_seg) # 定义UNet模型 model = unet() # 编译模型 model.compile(optimizer=Adam(learning_rate=1e-4), loss='binary_crossentropy', metrics=['accuracy'])报错'NoneType' object has no attribute 'compile'怎么改代码

seg7_controller.zip_VHDL/FPGA/Verilog_VHDL_

标题中的"seg7_controller.zip"表明这是一个与七段数码管控制器相关的项目，文件格式为ZIP，通常用于存储和传输多个文件。"VHDL/FPGA/Verilog"标签揭示了这个项目涉及到的编程语言和硬件描述语言，VHDL和Verilog是两...

05_seg_test.rar_VHDL/FPGA/Verilog_VHDL__VHDL/FPGA/Verilog_VHDL_

标题中的“05_seg_test.rar”可能是一个项目文件或教程的命名，暗示着这是一个关于数字电路设计的资源包，特别是与VHDL和FPGA相关的。"VHDL/FPGA/Verilog"在标签中重复出现，这表明内容涵盖了三种重要的硬件描述语言...

用IDLE写一段代码，其中训练集图像文件路径为data/train/trainvol，训练集标签文件路径为data/train/trainseg，验证集图像文件路径为data/val/valvol，验证集标签路径为data/val/valseg

以下是一个简单的示例代码，用于读取训练集和验证集的图像和标签： python import numpy as np import nibabel as nib # 读取训练集图像和标签 train_vol = nib.load('data/train/trainvol.nii.gz').get_fdata...

修改此代码，使其图片路径和保存路径改成LiTS2017数据集的路径为E:\LiTS2017。保存路径为E:\2D-LiTS2017，帮我创建保存图像与标签的文件夹，并且将转换好的数据保存到对应的文件夹。。import os import numpy as np import nibabel as nib import imageio import cv2 def read_niifile(niifilepath): # 读取niifile文件 img = nib.load(niifilepath) # 提取niifile文件 img_fdata = img.get_fdata(dtype='float32') return img_fdata def save_fig(niifilepath, savepath, num, name): # 保存为图片 name = name.split('-')[1] filepath_seg = niifilepath + "segmentation\\" + "segmentation-" + name filepath_vol = niifilepath + "volume\\" + "volume-" +name savepath_seg = savepath + "segmentation\\" savepath_vol = savepath + "volume\\" if not os.path.exists(savepath_seg): os.makedirs(savepath_seg) if not os.path.exists(savepath_vol): os.makedirs(savepath_vol) fdata_vol = read_niifile(filepath_vol) fdata_seg = read_niifile(filepath_seg) (x, y, z) = fdata_seg.shape total = x * y for k in range(z): silce_seg = fdata_seg[:, :, k] # 三个位置表示三个不同角度的切片 if silce_seg.max() == 0: continue else: silce_seg = (silce_seg-silce_seg.min())/(silce_seg.max() - silce_seg.min())255 silce_seg = cv2.threshold(silce_seg, 1, 255, cv2.THRESH_BINARY)[1] if (np.sum(silce_seg == 255) / total) > 0.015: silce_vol = fdata_vol[:, :, k] silce_vol = (silce_vol - silce_vol.min()) / (silce_vol.max() - silce_vol.min()) 255 imageio.imwrite(os.path.join(savepath_seg, '{}.png'.format(num)), silce_seg) imageio.imwrite(os.path.join(savepath_vol, '{}.png'.format(num)), silce_vol) num += 1 # 将切片信息保存为png格式 return num if name == 'main': path= 'E:\\dataset\\LiTS17\\' savepath = 'E:\\dataset\\LiTS17\\2d\\' filenames = os.listdir(path + "segmentation") num = 0 for filename in filenames: num = save_fig(path, savepath, num, filename)

在这段代码中，我们修改了path和savepath变量的值，分别指向LiTS2017数据集的路径和保存图像与标签的文件夹路径。并且在保存图像和标签之前，我们使用了os.makedirs函数来创建保存图像和标签的文件夹，确保...

用IDLE写一段UNET代码，其中训练集图像文件路径为data/train/trainvol，训练集标签文件路径为 data/train/trainseg，验证集图像文件路径为 data/val/valvol，验证集标签路径为 data/val/valseg，测试集为data/test,所有数据文件均为png格式

model.fit(train_data, train_labels, batch_size=16, epochs=50, validation_data=(val_data, val_labels)) model.save_weights('model_weights.h5') # 测试UNET模型 def test(): model = unet() model.load_...

model = IDLE_UNET() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(train_vol, train_seg, batch_size=40, epochs=1, validation_data=(val_vol, val_seg))报错怎么改

model.fit(train_vol, train_seg, batch_size=40, epochs=1, validation_data=(val_vol, val_seg)) 如果这样还是无法解决问题，你可以进一步检查代码是否存在语法错误、数据是否正确加载等方面的问题。如果问题...

dataset_type = 'PascalVOCDataset' data_root = './data/VOCdevkit/VOC2012' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (512, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict(type='Normalize', img_norm_cfg), dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', img_norm_cfg), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type=dataset_type, data_root=data_root, img_dir='JPEGImages', ann_dir='SegmentationClass', split='ImageSets/Segmentation/train.txt', pipeline=train_pipeline), val=dict( type=dataset_type, data_root=data_root, img_dir='JPEGImages', ann_dir='SegmentationClass', split='ImageSets/Segmentation/val.txt', pipeline=test_pipeline), test=dict( type=dataset_type, data_root=data_root, img_dir='JPEGImages', ann_dir='SegmentationClass', split='ImageSets/Segmentation/val.txt', pipeline=test_pipeline)) 这个我如果导入自己的数据集，我应该改哪些参数

如果你要导入自己的数据集，你需要根据你的数据集文件夹和文件名的结构，以及数据集的类别数量进行以下参数的修改： 1. data_root: 修改为你数据集的根目录路径。 2. img_dir: 修改为你存放图像的文件夹名称。 ...

dataset_type = 'PascalVOCDataset' data_root = 'data/VOCdevkit/VOC2012' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (512, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict(type='Normalize', img_norm_cfg), dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(2048, 512), # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', img_norm_cfg), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type=dataset_type, data_root=data_root, img_dir='JPEGImages', ann_dir='SegmentationClass', split='ImageSets/Segmentation/train.txt', pipeline=train_pipeline), val=dict( type=dataset_type, data_root=data_root, img_dir='JPEGImages', ann_dir='SegmentationClass', split='ImageSets/Segmentation/val.txt', pipeline=test_pipeline), test=dict( type=dataset_type, data_root=data_root, img_dir='JPEGImages', ann_dir='SegmentationClass', split='ImageSets/Segmentation/val.txt', pipeline=test_pipeline)) 解释一下这个代码

然后，定义了一个字典data，包含了训练集、验证集和测试集的配置信息： - samples_per_gpu：每个GPU的样本数量。 - workers_per_gpu：每个GPU的数据加载器工作进程数量。 - train：训练集的配置，包括数据集...

请在注释处填入代码完成对训练集和测试集的结巴分词from paddlenlp.datasets import load_dataset def read(data_path): data_set = [] with open(data_path, 'r', encoding='utf-8') as f: for line in f: l = line.strip('\n').split('\t') if len(l) != 2: print (len(l), line) words, labels = line.strip('\n').split('\t') data_set.append((words,labels)) return data_set train_ds = read(data_path='train.txt') dev_ds = read(data_path='dev.txt') test_ds = read(data_path='test.txt') for i in range(5): print("sentence %d" % (i), train_ds[i][0]) print("sentence %d" % (i), train_ds[i][1]) print(len(train_ds),len(dev_ds)) import jieba def data_preprocess(corpus): data_set = [] ####填结巴分词代码 for text in corpus: seg_list = jieba.cut(text) data_set.append(" ".join(seg_list)) return data_set train_corpus = data_preprocess(train_ds) test_corpus = data_preprocess(test_ds) print(train_corpus[:2]) print(test_corpus[:2])

train_ds = read(data_path='train.txt') dev_ds = read(data_path='dev.txt') test_ds = read(data_path='test.txt') for i in range(5): print("sentence %d" % (i), train_ds[i][0]) print("sentence %d" % (i...

给出相同功能的代码import os import numpy as np import nibabel as nib import imageio from PIL import Image def read_niifile(niifilepath): # 读取niifile文件 img = nib.load(niifilepath) # 提取niifile文件 img_fdata = img.get_fdata(dtype='float32') return img_fdata def save_fig(niifilepath, savepath, num, name): # 保存为图片 name = name.split('-')[1] filepath_seg = niifilepath + "segmentation\" + "segmentation-" + name filepath_vol = niifilepath + "volume\" + "volume-" + name savepath_seg = savepath + "segmentation\" savepath_vol = savepath + "volume\" if not os.path.exists(savepath_seg): os.makedirs(savepath_seg) if not os.path.exists(savepath_vol): os.makedirs(savepath_vol) fdata_vol = read_niifile(filepath_vol) fdata_seg = read_niifile(filepath_seg) (x, y, z) = fdata_seg.shape total = x * y for k in range(z): silce_seg = fdata_seg[:, :, k] if silce_seg.max() == 0: continue else: silce_seg = (silce_seg - silce_seg.min()) / (silce_seg.max() - silce_seg.min()) * 255 silce_seg = np.uint8(Image.fromarray(silce_seg).convert('L')) silce_seg = cv2.threshold(silce_seg, 1, 255, cv2.THRESH_BINARY)[1] if (np.sum(silce_seg == 255) / total) > 0.015: silce_vol = fdata_vol[:, :, k] silce_vol = (silce_vol - silce_vol.min()) / (silce_vol.max() - silce_vol.min()) * 255 silce_vol = np.uint8(Image.fromarray(silce_vol).convert('L')) imageio.imwrite(os.path.join(savepath_seg, '{}.png'.format(num)), silce_seg) imageio.imwrite(os.path.join(savepath_vol, '{}.png'.format(num)), silce_vol) num += 1 return num if name == 'main': path = r'C:\Users\Administrator\Desktop\LiTS2017' savepath = r'C:\Users\Administrator\Desktop\2D-LiTS2017' filenames = os.listdir(path + "segmentation") num = 0 for filename in filenames: num = save_fig(path, savepath, num, filename) 。用另一段代码实现相同功能

silce_seg = (silce_seg - silce_seg.min()) / (silce_seg.max() - silce_seg.min()) * 255 silce_seg = np.uint8(cv2.cvtColor(silce_seg, cv2.COLOR_GRAY2RGB)) silce_seg = cv2.threshold(silce_seg, 1, 255, ...

import os import cv2 # 定义原始和更改大小后的文件夹路径 original_folder_path = 'E:\shujuji\Intel Image Classification\seg_train\seg_train/buildings' resized_folder_path = 'E:\shujuji\Intel Image Classification\seg_train\change/buildings' # 如果更改大小后的文件夹不存在，则创建它 if not os.path.exists(resized_folder_path): os.makedirs(resized_folder_path) # 循环遍历原始文件夹中的所有图像文件 for filename in os.listdir(original_folder_path): if filename.endswith('.jpg') or filename.endswith('.png') or filename.endswith('.jpeg'): # 读取原始图像 img = cv2.imread(os.path.join(original_folder_path, filename)) # 将图像大小更改为（224，224，3） img_resized = cv2.resize(img, (224, 224)) # 保存更改大小后的图像到新文件夹中，使用原始文件名 cv2.imwrite(os.path.join(resized_folder_path, filename), img_resized)，对这段代码进行更改，使之满足要求

original_folder_path = r'E:\shujuji\Intel Image Classification\seg_train\seg_train\buildings' resized_folder_path = r'E:\shujuji\Intel Image Classification\seg_train\change\buildings' # 如果更改大小...

Traceback (most recent call last): File "train.py", line 294, in <module> train() File "train.py", line 177, in train pred = get_model(pointclouds_phs[-1], is_training_phs[-1], bn_decay=bn_decay) File "/data/hxl/TDGCNN6sagan/tensorflow/sem_seg/model.py", line 569, in get_model self_att_1 = SelfAttention(out111) File "/data/hxl/TDGCNN6sagan/tensorflow/sem_seg/model.py", line 505, in init self.q_map = tf.keras.layers.Conv1D(self.out_channel, 1, use_bias=False) File "/root/anaconda3/envs/py37tf241/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 512, in init **kwargs) File "/root/anaconda3/envs/py37tf241/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 164, in init self._validate_init() File "/root/anaconda3/envs/py37tf241/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 175, in _validate_init self.groups, self.filters)) ValueError: The number of filters must be evenly divisible by the number of groups. Received: groups=1, filters=Tensor("transpose_2:0", shape=(24, 64, 4096), dtype=float32, device=/device:GPU:0)

要解决这个问题，你可以检查一下模型的定义，特别是和该层相关的部分，确保输入 filters 的形状符合要求。还可以检查一下数据的预处理过程，确保数据输入到模型中时的形状是正确的。如果问题仍然存在，你可以尝试...

如何把#对微调数据进行分词处理 train_seg = [] for line in train: seg_list = seg.cut(line.strip()) train_seg.append(' '.join(seg_list)) #print(train_seg) #加载标签数据 with open("D:\用来微调的模型\分词后贵港市港南区：高质量推动农业机械化.txt", 'r', encoding='utf-8') as f: label = f.readlines() #将标签数据转换为数字标签 label_dict = {} num_labels = 0 for line in label: if line.strip() not in label_dict: label_dict[line.strip()] = num_labels num_labels += 1 y_train = [label_dict[line.strip()] for line in label] 训练数据和标签数据的数量变得一致

可以使用以下代码将训练数据和标签数据的数量变得一致： # 对训练数据进行分词处理 train_seg = [] for line in train: seg_list = seg.cut(line.strip()) train_seg.append(' '.join(seg_list)) # 加载标签...

main函数执行 this_dir = os.path.dirname(file)这一行代码的时候报错shutil.Error: [('tools/../lib/models/.~seg_hrnet.py', 'output/cityscapes/seg_hrnet_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484/models/.~seg_hrnet.py', "[Errno 2] No such file or directory: 'tools/../lib/models/.~seg_hrnet.py'")]该怎么修改

这个错误提示表明在执行this_dir = os.path.dirname(__file__)这一行代码时，文件或目录tools/../lib/models/.~seg_hrnet.py不存在。这可能是由于路径设置不正确导致的。要解决这个问题，你可以尝试以下几个...

import pkuseg from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import SVC import joblib #加载 pkuseg 预训练模型 seg = pkuseg.pkuseg() #加载用来微调数据 with open("D:\统计数据原始数据\贵港市港南区：高质量推动农业机械化.txt", 'r', encoding='utf-8') as f: train = f.readlines() #对微调数据进行分词处理 train_seg = [] for line in train: seg_list = seg.cut(line.strip()) train_seg.append(' '.join(seg_list)) #print(train_seg) #加载标签数据 with open("D:\用来微调的模型\分词后贵港市港南区：高质量推动农业机械化.txt", 'r', encoding='utf-8') as f: label = f.readlines() #将标签数据转换为数字标签 label_dict = {} num_labels = 0 for line in label: if line.strip() not in label_dict: label_dict[line.strip()] = num_labels num_labels += 1 y_train = [label_dict[line.strip()] for line in label] #使用 TfidfVectorizer 将文本数据转换为向量表示 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(train_seg) #使用 SVM 进行分类 svm = SVC() svm.fit(X_train, y_train) 产生Found input variables with inconsistent numbers of samples:错误

这个错误通常是由于训练数据和标签数据的数量不一致导致的。请检查一下你的训练数据和标签数据的数量是否一致。你可以使用以下代码检查一下： python print(len(train_seg)) print(len(y_train)) 如果两个...

[filename,pathname,flag] = uigetfile('.jpg','请导入图像文件'); pic = imread([pathname,filename]); figure; imshow(pic); %% 确定训练集 TrainData_background = zeros(20,3,'double'); TrainData_foreground = ones(20,3,'double'); % 背景采样 msgbox('请选择20个背景样本点','Background Samples','help'); pause; for run = 1:20 [x,y] = ginput(1); %ginput函数直接提取像素点,返回这个点的坐标 hold on; plot(x,y,'r'); x = uint8(x); y = uint8(y); TrainData_background(run,1) = pic(x,y,1); TrainData_background(run,2) = pic(x,y,2); TrainData_background(run,3) = pic(x,y,3); end % 待分割出来的前景采样 msgbox('请选择20个前景样本点','Foreground Samples','help'); pause; for run = 1:20 [x,y] = ginput(1); hold on; plot(x,y,'ro'); x = uint8(x); y = uint8(y); TrainData_foreground(run,1) = pic(x,y,1); TrainData_foreground(run,2) = pic(x,y,2); TrainData_foreground(run,3) = pic(x,y,3); end % let background be 0 & foreground 1 TrainLabel = [zeros(length(TrainData_background),1); ... ones(length(TrainData_foreground),1)]; %% 建立支持向量机基于libsvm TrainData = [TrainData_background;TrainData_foreground]; model = svmtrain(TrainLabel, TrainData, '-t 1 -d 3'); %% 进行预测 i.e.进行图像分割基于libsvm preTrainLabel = svmpredict(TrainLabel, TrainData, model); [m,n,k] = size(pic); TestData = double(reshape(pic,mn,k)); TestLabal = svmpredict(zeros(length(TestData),1), TestData, model); %% ind = reshape([TestLabal,TestLabal,TestLabal],m,n,k); ind = logical(ind); pic_seg = pic; pic_seg(~ind) = 255; figure; imshow(pic_seg); figure; subplot(1,2,1); imshow(pic); subplot(1,2,2); imshow(pic_seg); %% toc将这些代码转换成python语言

train_data = np.concatenate((train_data_background, train_data_foreground)) model = svm.SVC(kernel='poly', degree=3) model.fit(train_data, train_label) # 进行预测，即进行图像分割 (m, n, k) = pic....

对定义好的UNET模型model进行训练，其中训练集原始图像"data\train\trainvol""，标签"data\train\trainseg"，验证集原始图像"data\val\valvol"，标签"data\val\valseg"

接着，我们将训练和验证数据分别加载到train_vol、train_seg、val_vol和val_seg中，并使用zip函数将它们合并到train_generator和val_generator中。然后我们定义UNet模型，并使用compile函数进行编译。最后使用fit...

YOLO算法-城市电杆数据集-496张图像带标签-电杆.zip

YOLO系列算法目标检测数据集，包含标签，可以直接训练模型和验证测试，数据集已经划分好，包含数据集配置文件data.yaml，适用yolov5,yolov8,yolov9,yolov7,yolov10,yolo11算法；包含两种标签格:yolo格式（txt文件）和voc格式（xml文件），分别保存在两个文件夹中，文件名末尾是部分类别名称; yolo格式：<class> <x_center> <y_center> <width> <height>，其中： <class> 是目标的类别索引（从0开始）。 <x_center> 和 <y_center> 是目标框中心点的x和y坐标，这些坐标是相对于图像宽度和高度的比例值，范围在0到1之间。 <width> 和 <height> 是目标框的宽度和高度，也是相对于图像宽度和高度的比例值；【注】可以下拉页面，在资源详情处查看标签具体内容；

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用IDLE写一段代码，其中训练集图像文件路径为data/train/trainvol，训练集标签文件路径为data/train/trainseg，验证集图像文件路径为data/val/valvol，验证集标签路径为data/val/valseg

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用IDLE写一段UNET代码，其中训练集图像文件路径为data/train/trainvol，训练集标签文件路径为 data/train/trainseg，验证集图像文件路径为 data/val/valvol，验证集标签路径为 data/val/valseg，测试集为data/test,所有数据文件均为png格式