def handwritingClassTest(): hwLabels = [] trainingFileList = listdir('trainingDigits') # load the training set m = len(trainingFileList) trainingMat = zeros((m, 1024)) for i in range(m): fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] # take off .txt classNumStr = int(fileStr.split('_')[0]) hwLabels.append(classNumStr) trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr) testFileList = listdir('testDigits') # iterate through the test set errorCount = 0.0 mTest = len(testFileList) for i in range(mTest): fileNameStr = testFileList[i] fileStr = fileNameStr.split('.')[0] # take off .txt classNumStr = int(fileStr.split('_')[0]) vectorUnderTest = img2vector('testDigits/%s' % fileNameStr) classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3) print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)) if (classifierResult != classNumStr): errorCount += 1.0 print("\nthe total number of errors is: %d" % errorCount) print("\nthe total error rate is: %f" % (errorCount / float(mTest)))解释一下这段代码

翻译这段代码：tags = os.listdir(path) n = 0 for tag in tags: _tag_ = os.listdir(path+tag) n += len(_tag_)

n = len(_tag_) # 统计某个文件夹下的文件数量，并将其赋值给n变量该代码段主要功能是：统计指定路径下所有文件夹中文件的数量，并将其保存到变量n中。其中，os.listdir()方法可以获取指定路径下所有的文件夹...

folder_path = r'D:\pythonProject\csv' file_list = os.listdir(folder_path) for file_name in file_list: subfolder_path = os.path.join(folder_path, file_name) if os.path.isdir(subfolder_path): # 进入子文件夹 os.chdir(subfolder_path) # 获取子文件夹中的.csv文件列表 csv_list = [f for f in os.listdir(subfolder_path) if f.endswith('.csv')] # 读取.csv文件 dfs = [] for csv_file in csv_list: df = pd.read_csv(csv_file, header=None) dfs.append(df) csv_folder_name = os.path.basename(subfolder_path) last_char = csv_folder_name[-1] wave = [0]256 # form = [] records = dfs beat = [0]len(records) i = 0 for wave in records: form = last_char # print(wave) # print(form) beat[i] = [wave, form] # print('beat[{}] ='.format(i), beat[i]) # print('len(beat[{}]) ='.format(i), len(beat[i])) i = i + 1 # 划分特征与标签 for i in range(len(beat)): i = 0 beat = beat[i] X = [item[0] for item in beat] y = [item[1] for item in beat] print(X, y) i = i + 1

csv_list = [f for f in os.listdir(subfolder_path) if f.endswith('.csv')] # 读取.csv文件 dfs = [] for csv_file in csv_list: df = pd.read_csv(csv_file, header=None) dfs.append(df) csv_folder_name...

解释代码 def dir_numtoname(path,myfilelablepath): dirlist = os.listdir(path) data = [] lableflags = []

def dir_numtoname(path, myfilelablepath): dirlist = os.listdir(path) data = [] lableflags = [] for i in range(len(dirlist)): if not os.path.isdir(os.path.join(path, dirlist[i])): continue data....

label2int = {'buildings':0,'forest':1,'glacier':2,'mountain':3,'sea':4,'street':5} int2label = dict([val,key] for key,val in label2int.items()) def get_images(directory): Images = [] Labels = [] for labels in os.listdir(directory): label = label2int[labels] for image_file in os.listdir(directory+labels): #Extracting the file name of the image from Class Label folder image = cv2.imread(directory+labels+r'/'+image_file) # Reading the image (OpenCV) image = cv2.resize(image,(150,150)) #Resize the image, Some images are different sizes. (Resizing is very Important) Images.append(image) Labels.append(label) return shuffle(Images,Labels,random_state=817328462) #Shuffle the dataset you just prepared. def get_classlabel(class_code): labels = {2:'glacier', 4:'sea', 0:'buildings', 1:'forest', 5:'street', 3:'mountain'} return labels[class_code] Images, Labels = get_images('../input/seg_train/') #Extract the training images from the folders. Images = np.array(Images) #converting the list of images to numpy array. Labels = np.array(Labels) 请用中文分别解释上述每行代码的作用

3. def get_images(directory):：定义一个函数，用于从给定目录中获取图像和标签。 4. Images = []：创建一个空列表，用于存储图像。 5. Labels = []：创建一个空列表，用于存储标签。 6. for labels in os....

def get_File_List(self, filePath): filenames = os.listdir(filePath) return filenames的含义

这段代码定义了一个名为 get_File_...在方法内部，通过调用 os.listdir 方法获取指定路径下的所有文件名，并将它们存储在 filenames 变量中。最后，该方法返回 filenames 变量，即指定路径下的所有文件名列表。

ROWS = 150 COLS = 150 # # ROWS = 128 # COLS = 128 CHANNELS = 3 def read_image(file_path): img = cv2.imread(file_path, cv2.IMREAD_COLOR) return cv2.resize(img, (ROWS, COLS), interpolation=cv2.INTER_CUBIC) def predict(): TEST_DIR = 'D:/final/CatVsDog-master/media/img/' result = [] # model = load_model('my_model.h5') model = load_model('D:/final/CatVsDog-master/venv/Include/VGG/model.h5') test_images = [TEST_DIR + i for i in os.listdir(TEST_DIR)] count = len(test_images) # data = np.ndarray((count, CHANNELS, ROWS, COLS), dtype=np.uint8) data = np.ndarray((count, ROWS, COLS, CHANNELS), dtype=np.uint8) # print("图片网维度：") print(data.shape) for i, image_file in enumerate(test_images): image = read_image(image_file) # print() data[i] = image # data[i] = image.T if i % 250 == 0: print('处理 {} of {}'.format(i, count)) test = data predictions = model.predict(test, verbose=0) dict = {} urls = [] for i in test_images: ss = i.split('/') url = '/' + ss[3] + '/' + ss[4] + '/' + ss[5] urls.append(url) for i in range(0, len(predictions)): if predictions[i, 0] >= 0.5: print('I am {:.2%} sure this is a Dog'.format(predictions[i][0])) dict[urls[i]] = "图片预测为：关！" else: print('I am {:.2%} sure this is a Cat'.format(1 - predictions[i][0])) dict[urls[i]] = "图片预测为：开！" plt.imshow(test[i]) # plt.imshow(test[i].T) plt.show() # time.sleep(2) # print(dict) # for key,value in dict.items(): # print(key + ':' + value) return dict if name == 'main': result = predict() for i in result: print(i)

这段代码是一个使用 VGG 模型进行图片分类的程序。代码中首先定义了图片的尺寸和通道数，然后定义了一个读取图片并进行缩放的函数 read_image。接着定义了一个预测函数 predict，其中加载了之前训练好的 VGG 模型并...

import pandas as pd import os # 读取.csv文件 folder_path = r'D:\pythonProject\csv' file_list = os.listdir(folder_path) for file_name in file_list: subfolder_path = os.path.join(folder_path, file_name) if os.path.isdir(subfolder_path): # 进入子文件夹 os.chdir(subfolder_path) # 获取子文件夹中的.csv文件列表 csv_list = [f for f in os.listdir(subfolder_path) if f.endswith('.csv')] # 读取.csv文件 dfs = [] for csv_file in csv_list: df = pd.read_csv(csv_file, header=None) dfs.append(df) csv_folder_name = os.path.basename(subfolder_path) last_char = csv_folder_name[-1] wave = [0]256 # form = [] records = dfs beat = [0]len(records) i = 0 for wave in records: form = last_char # print(wave) # print(form) beat[i] = [wave, form] # print('beat[{}] ='.format(i), beat[i]) # print('len(beat[{}]) ='.format(i), len(beat[i])) i = i + 1 # 划分特征与标签 X = beat[:, 0] y = beat[:, 1] print(X, y)怎么改

在你的代码中，beat是一个列表，其中每个元素都是一个列表，其中第一个元素是wave，第二个元素是form。在最后一行代码中，你尝试使用[:, 0]和[:, 1]对beat进行切片来获取X和y，但这样做是不正确的，...

帮我用python优化一下以下代码：def scanFile(filePath): # fp = open("folder.log", "w+") for path in os.walk(filePath): lists.append(path[0]) for i in lists: try: if len(os.listdir(i)) != 0: continue else: ls.append(i) except: continue for m in range(len(ls)): for x in ls: os.rmdir(ls[m]) with open("folder.log", "a+") as f: data = ls[m] f.write(data) f.write('\n') f.close() print(datetime.now(), "正在删除", ls[m]) ls.clear() scanFile(filePath) # fp.close()

def scanFile(filePath): with open(filePath, 'r') as f: # 读取文件内容 contents = f.read() # 在这里处理文件内容 # ... 2. 将文件内容读取为列表，这样可以使用列表推导式（list comprehension）或 ...

代码错误：import unicodedata as ud from openpyxl import load_workbook filenames = [f for f in os.listdir('.') if f.endswith('.xlsx')] # 指定表格文件名列表 dfs = [] for file in files: sheets = pd.read_excel(file, sheet_name=None) # 使用列表推导式读取多个表格 workbooks = [load_workbook(filename) for filename in filenames] # 遍历每个工作表 for workbook in workbooks: for sheetname in workbook.sheetnames: sheet = workbook[sheetname] # 遍历每个单元格 for row in sheet.iter_rows(): for cell in row: # 判断单元格是否为字符串类型 if isinstance(cell.value, str): # 将全角字符转换为半角字符 cell.value = ud.normalize('NFKC', cell.value) # 保存修改后的 Excel 文件 for i, workbook in enumerate(workbooks): filename = filenames[i].replace(".xlsx", "-1.xlsx") workbook.save(filename)

filenames = [f for f in os.listdir('.') if f.endswith('.xlsx')] # 指定表格文件名列表 dfs = [] for file in filenames: # 修改此处的变量名 sheets = pd.read_excel(file, sheet_name=None) # 使用列表推导式...

修改错误import os def main(p): files = os.listdir(p) print(files) path = "C:\\student\\stopwords.txt" print(main(path))

这段代码有一些问题，主要是在调用main函数时传入的参数path不正确，应该传入C:\... files = os.listdir(p) return files path = "C:\\student" print(main(path)) 这段代码会输出指定路径下的所有文件名。

提升下面代码的运行速度：import os from PIL import Image # 定义一个函数，判断图片是否全黑 def is_black_image(filepath): img = Image.open(filepath) width, height = img.size for x in range(width): for y in range(height): pixel = img.getpixel((x,y)) if sum(pixel) != 0: return False return True # 遍历文件夹，判断并删除纯黑照片 def batch_delete_black_images(folder_path): for f in os.listdir(folder_path): file_path = os.path.join(folder_path, f) if os.path.isfile(file_path): if is_black_image(file_path): os.remove(file_path) print('{} has del'.format(file_path)) # 调用函数批量删除 batch_delete_black_images(r'F:\H18\Dfinal640.tif\8-2')

for f in os.listdir(folder_path): file_path = os.path.join(folder_path, f) if os.path.isfile(file_path): file_list.append(file_path) with Pool() as p: p.map(delete_black_images, file_list) if ...

def findSOPs(path): dcmFileNames = os.listdir(path) cuts = [] SOPs = [] for dcmFileName in dcmFileNames: if 'CT.' in dcmFileName: dcmFileName = path+'\\'+dcmFileName dcmFileNames = list(map(str,dcmFileName.split("."))) #print(dcmFileNames) cut = re.findall("\d+",dcmFileNames[2]) ds = pydicom.dcmread(dcmFileName) SOPs.append(ds.SOPInstanceUID) cuts.append(int(cut[0])) cuts = np.array(cuts) #print(np.sort(cuts)) #cuts_index = cuts.argsort() SOPs = np.array(SOPs) SOPs = SOPs[np.argsort(cuts)] SOPs = SOPs.tolist() #print(SOPs) return SOPs

具体来说，它首先使用 os.listdir() 函数列出给定目录下所有文件的文件名，并遍历这些文件名。如果文件名中包含 'CT.'，则说明该文件是一个 CT 格式的 DICOM 文件，将其读入内存。然后使用正则表达式 \d+ 提取文件名...

"" base64编解码处理用于处理某些被加密的敏感词库 author : @h-j-13 time : 2018-7-18 """ import os import base64 def decode64file(path_file): """解码base64加密的文件""" with open(path_file, 'rb') as f: str_set = set() for line in f: s = line.strip() # strip()方法消除多余的空格之类的特殊字符 if s.endswith(b'Cg=='): s = s.replace(b'Cg==', b'') str_set.add(base64.b64decode(s)) return str_set train_data_url = r"C:\Users\曹福滨\Downloads\tc-corpus-\answer" def get_all_file_by_path(path=train_data_url): """获取某个目录下的所有训练文件""" file_path = [] dir_list = os.listdir(train_data_url) for d in dir_list: file_path.extend(map(lambda x: train_data_url + d + '/' + x, os.listdir(train_data_url + d))) return file_path def decode_file2utf8(file_path): """将文件从GB2312编码解码为utf8文件""" decode_error = False file_data = [] with open(file_path, 'r') as f: for l in f.readlines(): try: tmp = l.encode('gbk').decode('utf8') except Exception as e: decode_error = True tmp = '' file_data.append(tmp) if decode_error: os.remove(file_path) else: with open(file_path, 'w') as f: f.writelines(file_data) if name == 'main': for p in get_all_file_by_path(): decode_file2utf8(p)

这段代码是用来解码base64加密的文件，主要是用于处理一些被加密的敏感词库。作者是@h-j-13，时间是2018年7月18日。代码中使用了Python的os和base64模块，其中decode64file函数接受一个文件路径作为参数，读取文件...

def readfile(path, label): # label 是一个 boolean variable, 代表需不需要回传 y 值 image_dir = sorted(os.listdir(path)) # os.listdir(path)将path路径下的文件名以列表形式读出 # print(os.listdir(path)) # print(image_dir) x = np.zeros((len(image_dir), 128, 128, 3), dtype=np.uint8) y = np.zeros((len(image_dir)), dtype=np.uint8) for i, file in enumerate(image_dir): img = cv2.imread(os.path.join(path, file)) # os.path.join(path, file) 路径名合并 x[i, :, :] = cv2.resize(img, (128, 128)) if label: y[i] = int(file.split("_")[0]) if label: return x, y else: return x

函数首先通过 os.listdir(path) 函数获取路径下所有文件名，并对其进行排序。然后，创建一个大小为 (len(image_dir), 128, 128, 3) 的 0 数组 x，用来存放图片数据。如果 label 为真，则创建一个大小为 (len(image_...

# 目录下某CT文件的SOP值 def findSOPs(path): dcmFileNames = os.listdir(path) cuts = [] SOPs = [] for dcmFileName in dcmFileNames: if 'CT.' in dcmFileName: dcmFileName = path+'\\'+dcmFileName dcmFileNames = list(map(str,dcmFileName.split("."))) #print(dcmFileNames) cut = re.findall("\d+",dcmFileNames[2]) ds = pydicom.dcmread(dcmFileName) SOPs.append(ds.SOPInstanceUID) cuts.append(int(cut[0])) cuts = np.array(cuts) #print(np.sort(cuts)) #cuts_index = cuts.argsort() SOPs = np.array(SOPs) SOPs = SOPs[np.argsort(cuts)] SOPs = SOPs.tolist() #print(SOPs) return SOPs

1.使用os.listdir方法获取目录下所有文件的文件名，存储在dcmFileNames数组中。 2.遍历dcmFileNames数组，如果发现文件名中包含“CT.”，则将其作为CT文件进行处理。 3.使用pydicom.dcmread方法读取CT文件，获取其...

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