Hello Word!

其他的题解可能答案也是对的，但是省略了一些必要的说明，导致理解起来有gap。大致就是分情况讨论，然后使用动态规划。关键在于分情况讨论，要足够清楚！这个题目纠结了很久，看了很多题解思路，都有没解释清楚的地方。可能题目本身也有没说的特别清楚的地方。

【代码】Python双向链表插入节点。

语法糖就是程序语言中提供[奇技淫巧]的一种手段和方式而已。通过这类方式编写出来的代码，即好看又好用,好似糖一般的语法。【不知不觉之中一直在用的，并没有特别注意！Python语法糖包含哪些？

题目地址：https://leetcode.cn/problems/combination-sum/

【代码】python读取excel获取单元格超链接的原始网址。

$ conda update condaFetching package metadata ...CondaHTTPError: HTTP None None for url <https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/linux-64/repodata.json>Elapsed: NoneAn HTTP error occurred when trying to retrieve this URL.HTTP er

报错：cv2.error: OpenCV(4.5.4-dev) :-1: error: (-5:Bad argument) in function 'polylines'> Overload resolution failed:> - Can't parse 'pts'. Sequence item with index 0 has a wrong type> - Can't parse 'pts'. Sequence item with index 0 has a wrong

>>> import torch>>> a = torch.tensor([[1,2,3],[4,5,6]])# 和a shape相同，但是用0填充>>> b = torch.full_like(a,0)>>> c = torch.tensor([[0,0,1],[1,0,1]])# 赋值索引>>> c[:,0]tensor([0, 1])# 广播机制赋值>>> b[range(n)

不明原因就杀死了没有任何其他报错就 Killed很可能是内存不够（比如 某个数据的load导致），换个大内存的机器再试一下吧，兄dei

pass

代码：yolo_layers = [layers[i[0] - 1] for i in network.getUnconnectedOutLayers()]报错：Traceback (most recent call last): File "Run.py", line 201, in <module> main() File "Run.py", line 137, in main detections = yolo.detect(yolo_img) File

明明在命令行运行得好好的，TM就是找不到Exception has occurred: ModuleNotFoundErrorNo module named 'xxx'解决：

文章目录报错现象：原因：解决：报错报错1:TypeError: can't concat str to bytes报错2：TypeError: sequence item 0: expected str instance, bytes found现象：读取的json中带有中文，做了一番处理之后另存为另一个json但是，保存出来的那个json文件中出现了 \u53a2\u5f0f\u8d27\u8f66 类似这种的乱码原因：没有做好编码转换，应该跟系统本身安装的编码字符集关系不大解

解决：# 不要搞错，不要用自己的虚拟环境env下的binexport PATH="~/miniconda3/bin:$PATH"source activate xxx

发现直接装装不上：各种报错报错一：Failed to build cryptacularERROR: Could not build wheels for cryptacular which use PEP 517 and cannot be installed directly报错二：Failed to build cryptacularERROR: Could not build wheels for cryptacular, which is required to install p

报错：Traceback (most recent call last): File "/nfs/volume-902-6/xxxx/bad_test.py", line 66, in generate_pre_annot pickle.dump(proposals, f, protocol=None, fix_imports=True)TypeError: can't pickle module objects又一次阴沟里翻船，还好同事帮忙指出来的你这个pickle啊，它保存的东西需

你在用cv2的一些api画图的时候，可能会遇到这种报错：TypeError: Scalar value for argument ‘color‘ is not numeric原因是：你应该使用 (11，11，11) 这种颜色表示方法，就是三个整数的三元组。你好像在说我在说废话哥话还没说完，你可能觉得你就是这样干的然而还是报错了，没错，可能你print出来的跟实际用的不太一样，这种情况下，你需要把这三个数强制转成整数再试一下：a,b,c = int(a),int(b),int(c)cv2.circ

编译后的DCN V2不能import不知道啥原因，最后也是莫名其妙的解决了参考：

ssss

https://docs.anaconda.com/mkl-optimizations/index.html

功能：根据图片和点的坐标去精修点的坐标代码：import cv2img = cv2.imread('xxx.jpg')subpix_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)pts = [[852, 698], [1122, 702]]corners = corners.reshape((len(pts),

model = ArcfaceModel()# 加载原始权重文件ckpt_path = './checkpoints/arc_mbv2' # 该文件夹下存放了4个文件：checkpoint, e_8_b_40000.ckpt.data-00000-of-00002, e_8_b_40000.ckpt.data-00001-of-00002,e_8_b_40000.ckpt.indexckpt_path = tf.train.latest_checkpoint(ckpt_path)model.load

import jsondef json_to_dict(): json_dict = json.load(open('xxx.json', 'w')) pydict = {k:np.array(json_dict[k]) for k in json_dict.keys()} return pydict def dict_to_json(): pydict[k] = v with open('xxx.json', 'w') as f: js

def get_id_prob_imgs(id_path, prob_path): # 这里假设id图片只有一层目录，每个id一张照片; prob照片有两层目录，第一层子目录为id名称，子目录下面才是prob照片 id_imgs = [os.path.join(idpath,i) for i in os.listdir(id_path)] probsubpaths = [os.path.join(probpath,i) for i in os.listdir(probpath)]

def img_to_batch(): img = cv2.imread('xxxx.jpg') img = cv2.resize(img, (112,112)).astype(np.float32) / 255. img_batch = np.expand_dims(img,0)def tf2_to_pb(): # load tf2 model = AAA(xxx) ckpt_path = tf.train.latest_checkpoint('./xx

Traceback (most recent call last):File “/home/data/PJS/test_bed/img_show.py”, line 18, in cv2.imshow(‘img’, img)cv2.error: OpenCV(4.5.3) /tmp/pip-req-build-9gwtlx3c/opencv/modules/highgui/src/window.cpp:1274: error: (-2:Unspecified error) The function i

def copy_imgs(src, dstPath, newFileName=False): import shutil import sys try: # if dstPath is path and newFileName=False, then copy without rename # if dstPath is new file name, then copy and rename if not newFileName:

文章目录简单示例按键检测、转发报错和解决进程间通信简单示例linux ubuntu Python 多进程执行程序按键检测、转发通过SSH协议给python多进程转发按键python multiprocessing多进程按键检测，优雅地终止多进程(signal信号量和pynput等多种方法)报错和解决python多进程 multiprocessing.pipe 管道没有数据接收端会卡死，无提示，无报错！！！python多进程管道 multiprocessing.Pipe OSErro

读取txt文件# read txt file content to listdef ReadTxt_to_list(rootdir): lines = [] with open(rootdir, 'r') as file_to_read: while True: line = file_to_read.readline() if not line: break li.

# kmeans 聚类import numpy as npdef kmeans(data,num,max_iter,tol): # step1 centers = {} for i in range(num): centers[i] = data[i] # step2 for i in range(max_iter): clf = {} for feature in data:

from functools import partial pt = partial(add_mask, args) # multi process from multiprocessing import Process import multiprocessing as mp ctx = mp.get_context('spawn') q = ctx.Queue() split_list = r'/home/xxx/data/CASI...

pass

背景：每个ID有ID图片和prob图片做法：加载模型，提取特征，计算id和prob的平均相似度，排序，保存import numpy as npimport osimport mxnet as mxfrom menpo.visualize import print_progressfrom collections import namedtupleBatch = namedtuple('Batch', ['data'])def constructmodel(prefix, ctx, epo

# 编写适用于lfwa两级目录的图片文件列表提取def get_imgs(imgDir): subdirs = os.listdir(imgDir) subdirs = [os.path.join(imgDir, i) for i in subdirs] imgs = [] for subdir in subdirs: imgss = os.listdir(subdir) imgs.extend([os.path.join(subdir, i

>>> 'XXX'.join('abcabcabcabc'.rsplit('abc', 1))'abcabcabcXXX'

import osimport cv2import numpy as npfrom tqdm import tqdm# read txt file content to listdef ReadTxt_to_list(rootdir): lines = [] with open(rootdir, 'r') as file_to_read: while True: line = file_to_read.readline()

报错：Traceback (most recent call last): File "C:/projects/0Fea/plot_test/plot_loss.py", line 23, in <module> lines = ReadTxtName(txt) File "C:/projects/0Fea/plot_test/plot_loss.py", line 11, in ReadTxtName with open(rootdir, 'r', ) as file

固定python随机数rng = numpy.random.RandomState(23355)rng.uniform(0,1,(2,3))rng = numpy.random.RandomState(23355)rng.uniform(0,1,(2,3))

左边不管有多少个变量之间用等于号连接，仅仅是为了声明，最后一个等于号左边的所有变量都等于最右边的那个变量。

报错：JetBrains Account connection error: java.security.SignatureException: Signature length not correct: got 256 but was expecting 512解决：在help - edit vm options中加一行配置:-Djava.net.preferIPv4Stack=true然后重启，重新registerhttps://blog.youkuaiyun.com/getture/article/det