本篇文章给大家谈谈Python的代码c语言可以用吗，以及python代码大全和用法，希望对各位有所帮助，不要忘了收藏本站喔。
深度学习的图片等比resize后，再把图片反向resize回来，验证通过
import cv2
import numpy as npdef restore_image(image, original_size):# 获取图像的原始宽度和高度height, width = original_size# 计算缩放比例scale = min(image.shape[1]/width, image.shape[0]/height)# 计算缩放后的宽度和高度new_width = int(width * scale)new_height = int(height * scale)# 计算填充位置x_offset = (image.shape[1] - new_width) // 2y_offset = (image.shape[0] - new_height) // 2# 将调整后的图像放置在原始图像中心restored_image = image[y_offset:y_offset + new_height, x_offset:x_offset + new_width]# 调整图像大小# restored_image = cv2.resize(restored_image, None,None,1/scale,1/scale,interpolation=cv2.INTER_LANCZOS4)restored_image = cv2.resize(restored_image,(width,height),interpolation=cv2.INTER_LANCZOS4)return restored_imagedef resize_image(image, target_size, fill_value):# 获取图像的原始宽度和高度height, width = image.shape[:2]# 计算缩放比例scale = min(target_size[0] / width, target_size[1] / height)# 计算缩放后的宽度和高度new_width = int(width * scale)new_height = int(height * scale)# print(" new_width = {}, new_height = {} ".format(new_width, new_height))# 调整图像大小resized_image = cv2.resize(image, (new_width, new_height),interpolation=cv2.INTER_LANCZOS4)# 创建目标大小的空白图像，并填充特定值padded_image = np.full((target_size[1], target_size[0], image.shape[2]), fill_value, dtype=np.uint8)# 计算填充位置x_offset = (target_size[0] - new_width) // 2y_offset = (target_size[1] - new_height) // 2# print(" x_offset = {}, y_offset = {} ".format(x_offset, y_offset))# 将调整后的图像放置在填充图像中心padded_image[y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized_imagereturn padded_image# 读取图像
image = cv2.imread('1.jpg')# 设置目标大小（这里设置为300x300）
target_size = (512, 512)# 设置填充值
fill_value = 114# 进行图像缩放和填充
resized_image = resize_image(image, target_size, fill_value)# 将缩放后的图像恢复到原始大小
restored_image = restore_image(resized_image, (image.shape[0],image.shape[1]))cv2.imwrite("1_reszie.jpg",resized_image )
save_params = [cv2.IMWRITE_JPEG_QUALITY, 100]
cv2.imwrite("1_restored.jpg",restored_image,save_params)
# # 显示原始图像和缩放后的图像
# cv2.imshow('Original Image', image)
# cv2.imshow('Resized Image', resized_image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()文件夹遍历和文件遍历
# 遍历文件夹和文件，返回文件的路径和对应的文件的名称，同时可以根据自己的应用需要进行更改
def get_img_file(file_name):imagelist = []for parent, dirnames, filenames in os.walk(file_name):for filename in filenames:if filename.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):imagelist.append(os.path.join(parent, filename))return imagelist,filenames只遍历当前文件或者只遍历当前文件夹
def GetDirectory(path):directory=os.listdir(path)directory_list=[]for dirs in directory:if not os.path.isfile(os.path.join(path,dirs)):directory_list.append(os.path.join(path,dirs))return directory_list
def GetFile(path):files=os.listdir(path)file_list=[]for file in files:if os.path.isfile(os.path.join(path,file)):file_list.append(os.path.join(path,file))return file_list和上段代码的功能类似，遍历根目录下的所有文件夹和文件，只是这个是组个遍历，可以人为的调整和修改：
def preprocess(src_root, dst_root):""":param src_root::param dst_root::return:"""if not os.path.isdir(src_root):print("[Err]: invalid source root")returnif not os.path.isdir(dst_root):os.makedirs(dst_root)print("{} made".format(dst_root))# 创建用于训练MOT的目录结构dst_img_dir_train = dst_root + '/images/train'dst_img_dir_test = dst_root + '/images/test'dst_labels_with_ids = dst_root + '/labels_with_ids'if not os.path.isdir(dst_img_dir_train):os.makedirs(dst_img_dir_train)if not os.path.isdir(dst_img_dir_test):os.makedirs(dst_img_dir_test)if not os.path.isdir(dst_labels_with_ids):os.makedirs(dst_labels_with_ids)# 遍历src_root, 进一步完善训练目录并拷贝文件for x in os.listdir(src_root):x_path = src_root + '/' + xif os.path.isdir(x_path):for y in os.listdir(x_path):if y.endswith('.jpg'):y_path = x_path + '/' + yif os.path.isfile(y_path):# 创建用于训练的图片目标目录dst_img1_dir = dst_img_dir_train + '/' + x + '/img1'if not os.path.isdir(dst_img1_dir):os.makedirs(dst_img1_dir)# copy image to train image dirdst_f_path = dst_img1_dir + yif os.path.isfile(dst_f_path):shutil.copy(y_path, dst_img1_dir)print('{} cp to {}'.format(y, dst_img1_dir))else:print('{} already exists.'.format(dst_f_path))等比resize
import cv2
import matplotlib.pyplot as plt# 封装resize函数
def resize_img_keep_ratio(img_name,target_size):img = cv2.imread(img_name) # 读取图片old_size= img.shape[0:2] # 原始图像大小ratio = min(float(target_size[i])/(old_size[i]) for i in range(len(old_size))) # 计算原始图像宽高与目标图像大小的比例，并取其中的较小值new_size = tuple([int(i*ratio) for i in old_size]) # 根据上边求得的比例计算在保持比例前提下得到的图像大小img = cv2.resize(img,(new_size[1], new_size[0])) # 根据上边的大小进行放缩pad_w = target_size[1] - new_size[1] # 计算需要填充的像素数目（图像的宽这一维度上）pad_h = target_size[0] - new_size[0] # 计算需要填充的像素数目（图像的高这一维度上）top,bottom = pad_h//2, pad_h-(pad_h//2)left,right = pad_w//2, pad_w -(pad_w//2)img_new = cv2.copyMakeBorder(img,top,bottom,left,right,cv2.BORDER_CONSTANT,None,(0,0,0)) return img_newif __name__ == "__main__":img = r'D:\SAMM\crop\006_1\1.jpg' # 待处理的图片地址, 替换成你的地址就好target_size=[224, 224] # 目标图像大小resized_img = resize_img_keep_ratio(img, target_size) plt.imshow(resized_img)plt.show()
多张图片生成视频代码
import os
import cv2def generate_video(path,size,fps=25):# fps = 24 #帧率# size = (640, 480)videowriter = cv2.VideoWriter("./resultout/test.mp4",cv2.VideoWriter_fourcc(*'mp4v'),fps,size)#path = r'F:/data/predict_landmark/'for rootpath, dirs, names in os.walk(path):names.sort()for name in names:img = cv2.imread(os.path.join(rootpath, name))print("--", name)videowriter.write(img)videowriter.release()generate_video('./resultout/frame/',(1920, 1080),25)
删除文件和文件夹
需要在执行某些代码前清空指定的文件夹，如果直接用os.remove()，可能出现因文件夹中文件被占用而无法删除，解决方法也很简单，先强制删除文件夹，再重新建同名文件夹即可
import shutil
shutil.rmtree('要清空的文件夹名')
os.mkdir('要清空的文件夹名')把一个文件从一个文件夹移动到另一个文件夹，并同时重命名，用shutil也很简单
shutil.move('原文件夹/原文件名','目标文件夹/目标文件名')同理一个文件夹的文件复制到另一个文件夹可以使用下面的代码：
shutil.copy("源目录/文件名", "目的目录/文件名")数据集元素分割
这里有多种方式，先上一个简单的，然后来一个比较完全的。
import os
import randomdef main():random.seed(0) # 设置随机种子，保证随机结果可复现files_path = "F:\\车道线大图"assert os.path.exists(files_path), "path: '{}' does not exist.".format(files_path)val_rate = 0.5# 读取数据，并遍历图片名称，通过'.'进行分割字符串，取第一部分，然后排序files_name = sorted([file.split(".")[0] for file in os.listdir(files_path)])files_num = len(files_name)# 生成随机索引，k表示从输入的序列中随机选择k个无重复的元素val_index = random.sample(range(0, files_num), k=int(files_num*val_rate))train_files = []val_files = []for index, file_name in enumerate(files_name):if index in val_index:val_files.append(file_name)else:train_files.append(file_name)try:train_f = open("train.txt", "x")eval_f = open("val.txt", "x")train_f.write("\n".join(train_files))eval_f.write("\n".join(val_files))except FileExistsError as e:print(e)exit(1)'''
Deion: 学习使用
Version: 1.0
Author: 赵守风
Email: 1583769112@qq.com
Date: 2021-03-24 14:17:19
LastEditors: zsf
LastEditTime: 2021-03-24 17:06:01
'''import torch
import os
import random
import shutil
import math'''
@deion: 直接切分数据集，copy数据到对应的训练集、测试集和验证集，这里不考虑类别的问题
@param {*} datapath: 原始数据路径
@param {*} rootpath: 保存切分后的路径root
@param {*} train_rate: 训练集占总数的百分比
@param {*} val_rate: 验证集占总数百分比
@param {*} test_rate: 测试集占总数的百分比
@return {*}
@author: zsf
'''
def data_split(datapath, rootpath="./data_split", train_rate=0.8, val_rate=0.1, test_rate=0.1):for parent, dirnames, filenames in os.walk(datapath):for dirname in dirnames:filepaths = os.listdir(os.path.join(parent, dirname))# 删除其他的文件filepaths = [x for x in filepaths if x.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')) ] # 开始切分random.shuffle(filepaths) for i in range(len(filepaths)):if i < math.floor(train_rate*len(filepaths)):sub_path = os.path.join(rootpath, 'train_set', dirname)elif i < math.floor((train_rate+val_rate)*len(filepaths)):sub_path = os.path.join(rootpath, 'val_set', dirname)elif i < len(filepaths):sub_path = os.path.join(rootpath, 'test_set', dirname)if os.path.exists(sub_path) == 0:os.makedirs(sub_path)shutil.copy(os.path.join(datapath, dirname, filepaths[i]), os.path.join(sub_path, filepaths[i])) # 复制图片，从源到目的地'''
@deion: 这里生成对应的txt文件不挪动图片
@param {*} datapath: 原始数据路径
@param {*} rootpath: 保存切分后的路径root
@param {*} train_rate: 训练集占总数的百分比
@param {*} val_rate: 验证集占总数百分比
@param {*} test_rate: 测试集占总数的百分比
@return {*}
@author: zsf
'''
def data_split_txt(datapath, rootpath="./data_split", train_rate=0.8, val_rate=0.1, test_rate=0.1):for parent, dirnames, filenames in os.walk(datapath):index = 0for dirname in dirnames:filepaths = os.listdir(os.path.join(parent, dirname))# 删除其他的文件filepaths = [x for x in filepaths if x.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')) ] # 开始切分random.shuffle(filepaths) for i in range(len(filepaths)):if i < math.floor(train_rate*len(filepaths)):txt_path = 'train_set.txt'elif i < math.floor((train_rate+val_rate)*len(filepaths)):txt_path = 'val_set.txt'elif i < len(filepaths):txt_path = 'test_set.txt'with open(os.path.join(rootpath, txt_path), mode='a') as file:file.write(str(index) + ' ' + os.path.join(datapath, dirname, filepaths[i]) + '\n') index += 1 一张图片均匀切割
import os
from PIL import Image
path="D:\\SR\\USRNet-master\\USRNet-master\\testsets\\image\\20.jpg"
smallpath='D:\\SR\\USRNet-master\\USRNet-master\\testsets\\small'
def splitimage(src, rownum, colnum, dstpath):img = Image.open(src)w, h = img.sizeif rownum <= h and colnum <= w:print('Original image info: %sx%s, %s, %s' % (w, h, img.format, img.mode))print('开始处理图片切割, 请稍候...')s = os.path.split(src)if dstpath == '':dstpath = s[0]fn = s[1].split('.')basename = fn[0]ext = fn[-1]num = 0rowheight = h // rownumcolwidth = w // colnumfor r in range(rownum):for c in range(colnum):box = (c * colwidth, r * rowheight, (c + 1) * colwidth, (r + 1) * rowheight)p = os.path.join(dstpath, basename + '_' + str(num) + '.' + ext)img.crop(box).save(p)num = num + 1print("all = {}, num = {}".format(rownum*colnum, num))print('图片切割完毕，共生成 %s 张小图片学python有用吗。' % num)else:print('不合法的行列切割参数！')splitimage(path, 10, 10, smallpath)视频转图片
cap = cv2.VideoCapture('D:/SR/src/2.mp4')
i =0
while(1):ret, frame = cap.read()#cv2.imshow("image",frame)cv2.imwrite("D:/SR/USRNet-master/USRNet-master/testsets/image/"+str(i)+'.jpg',frame)i+=1路径字符串处理
def save_image(output_dir, image_path):if not os.path.exists(output_dir):os.makedirs(output_dir)image_name = os.path.split(image_path)[-1]name, ext = os.path.splitext(image_name)return os.path.join(output_dir, "{}".format(name)) + ext两种文件缺少彼此的进行互删，如jpg对应xml文件，有时候会缺少jpg或者xml，需要删除多余的
import osimport shutil
imagelist=[]
for parent, dirnames, filenames in os.walk("E:\\MOTdata\\helmet_20210908\\data1"):for filename in filenames:if filename.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):path = os.path.join(parent, filename)print(filename[:-4]+".json")if filename[:-4]+".json" not in filenames:os.remove(path)jsonlist=[]
for parent, dirnames, filenames in os.walk("E:\\MOTdata\\helmet_20210908\\data1"):for filename in filenames:if filename.lower().endswith(('.json')):path = os.path.join(parent, filename)if filename[:-5]+".jpg" not in filenames:os.remove(path)Python ——保存字典到文件
在 Python 中使用 pickle 模块的 dump 函数将字典保存到文件中
import picklemy_dict = { 'Apple': 4, 'Banana': 2, 'Orange': 6, 'Grapes': 11}
# 保存文件
with open("myDictionary.pkl", "wb") as tf:pickle.dump(my_dict,tf)
# 读取文件
with open("myDictionary.pkl", "wb") as tf:new_dict = pickle.load(tf)print(new_dict.item())
在 Python 中使用 NumPy 库的 save 函数将一个字典保存到文件中
import numpy as npmy_dict = { 'Apple': 4, 'Banana': 2, 'Orange': 6, 'Grapes': 11}
# 保存文件
np.save('file.npy', my_dict)
# 读取文件
new_dict = np.load('file.npy', allow_pickle='TRUE')
print(new_dict)
在 Python 中使用 json 模块的 dump 函数将一个字典保存到文件中
import jsonmy_dict = { 'Apple': 4, 'Banana': 2, 'Orange': 6, 'Grapes': 11}
# 保存文件
tf = open("myDictionary.json", "w")
json.dump(my_dict,tf)
tf.close()
# 读取文件
tf = open("myDictionary.json", "r")
new_dict = json.load(tf)
print(new_dict)大数据集的遍历（数据分块，使用进程并行处理）
from ntpath import realpath
import os
from pathlib import Path
from random import shuffle
import shutil
import pandas as pd
from multiprocessing import Process,Queue
from time import sleep# root = "/data/FaceData/FaceTrainData"
# relative_path = "/data/FaceData/FaceTrainData_Mask"# file_path = os.path.join(root,"FaceTrainDataMask.txt")# with open(file_path) as files:
# for item in files:
# temp = item.strip('\n').split(" ")
# p = Path(item).parts
# p1 = os.path.join(relative_path,p[0])
# if(not os.path.exists(p1)):
# os.mkdir(p1)
# p2 = os.path.join(p1,p[1])
# if(not os.path.exists(p2)):
# os.mkdir(p2)# name = p[2].strip('\n').split(' ')# image_path = os.path.join(root, temp[0])
# save_path = os.path.join(p2, name[0])
# shutil.copy(image_path,save_path)def process_data(my_queue):root = "/data/FaceData/FaceTrainData"relative_path = "/data/FaceData/FaceTrainData_NotMask"# print(df)file_path = os.path.join(root,"FaceTrainDataNotMask.txt")# for row in df.itertuples():# print(row[1])# with open(file_path) as files:df = my_queue.get()i=0;for row in df.itertuples(): item = row[1]temp = item.strip('\n').split(" ") p = Path(item).partsp1 = os.path.join(relative_path,p[0])if(not os.path.exists(p1)):os.mkdir(p1)p2 = os.path.join(p1,p[1])if(not os.path.exists(p2)):os.mkdir(p2)name = p[2].strip('\n').split(' ')image_path = os.path.join(root, temp[0])save_path = os.path.join(p2, name[0])# print(image_path)shutil.copy(image_path,save_path)i+=1print("-------------------------------------i={}--------------------------".format(i))root = "/data/FaceData/FaceTrainData"
file_path = os.path.join(root,"FaceTrainDataNotMask.txt")x = int(20979331/40)
chunksize = x # 每100万行处理一次数据
reader = pd.read_table(file_path, encoding = 'utf-8', iterator=True, chunksize=chunksize,names=["A"])
chunk_num = 0
for chunk in reader:my_queue = Queue()print("Chunk: " + str(chunk_num) + ' >'*30 + '\n')df = chunkmy_queue.put(df)# print(df)# for row in df.itertuples():# print(row[1])p = Process(target=process_data,args=(my_queue,))p.start()chunk_num += 1while True:sleep(10)print("------------------")
