mport cv2 import numpy as np import glob # 找棋盘格角点 # 阈值 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) #棋盘格模板规格 w = 9 h = 6 # 世界坐标系中的棋盘格点,例如(0,0,0), (1,0,0), (2,0,0) ....,(8,5,0)，去掉Z坐标，记为二维矩阵 objp = np.zeros((w*h,3), np.float32) objp[:,:2] = np.mgrid[0:w,0:h].T.reshape(-1,2) # 储存棋盘格角点的世界坐标和图像坐标对 objpoints = [] # 在世界坐标系中的三维点 imgpoints = [] # 在图像平面的二维点 images = glob.glob('C:/yingxiang/biaoding.png') for fname in images: img = cv2.imread(fname) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # 找到棋盘格角点 ret, corners = cv2.findChessboardCorners(gray, (w,h),None) # 如果找到足够点对，将其存储起来 if ret == True: cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria) objpoints.append(objp) imgpoints.append(corners) # 将角点在图像上显示 cv2.drawChessboardCorners(img, (w,h), corners, ret) cv2.imshow('findCorners',img) cv2.waitKey(1) cv2.destroyAllWindows() # 标定 ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None) # 去畸变 img2 = cv2.imread('calib/00169.png') h, w = img2.shape[:2] newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),0,(w,h)) # 自由比例参数 dst = cv2.undistort(img2, mtx, dist, None, newcameramtx) # 根据前面ROI区域裁剪图片 #x,y,w,h = roi #dst = dst[y:y+h, x:x+w] cv2.imwrite('calibresult.png',dst) # 反投影误差 total_error = 0 for i in range(len(objpoints)): imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist) error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2) total_error += error print ("total error: ", total_error/len(objpoints))这段代码为什么会报错

mport numpy as np from scipy.optimize import linprog def integer_cutting_plane(c, A, b, bound): # 初始化整数规划问题的线性规划松弛问题 relaxed_c = c relaxed_A = A relaxed_b = b bounds = bound while True: # 解决线性规划松弛问题 res = linprog(c=relaxed_c, A_ub=relaxed_A, b_ub=relaxed_b, bounds=bounds) x = res.x result = res.fun print(x) print(result) # 检查解是否为整数解 if all(int(val) == val for val in x): return x.astype(int) # 添加割平面 new_constraint = (relaxed_A @ x <= relaxed_b) # 添加割平面约束 relaxed_A = np.vstack((relaxed_A, new_constraint)) # relaxed_b = np.append(relaxed_b, np.floor(relaxed_A @ x)) # 定义目标函数系数 # 定义决策变量取值范围 def get_bounds(): x1 = (0, None) x2 = (0, None) return x1, x2 # 定义目标函数系数 def get_c(): c = np.array([40, 90]) return c # 定义不等式约束条件左边系数 def get_A(): A = np.array([[-9, -7], [-7, -20]]) return A # 定义不等式约束条件右边系数 def get_b(): b = np.array([-56, -70]) return b # 主程序 if name == 'main': integer_cutting_plane(get_c(), get_A(), get_b(), get_bounds()) 优化这段代码

2. 使用稀疏矩阵：如果你的不等式约束条件矩阵A是稀疏的，你可以考虑使用稀疏矩阵表示，这可以减少内存占用和计算时间。 3. 设置线性规划求解器选项：linprog函数有一些可选参数，例如method和options，可以用来...

mport jieba import wordcloud import numpy as np from PIL import Image star_mask=np.array(Image.open("star.png")) f=open("text.txt","r",encoding="utf-8") t=f.read() f.close() ls=jieba.lcut(t) txt="".join(ls) w=wordcloud.WordCloud(width=1000,height=700, backgroud_color="white", font_path="simhei.ttf", stopwords =wordcloud.STOPWORDS.add("发展"), mask = star_mask) w.generate(txt) w.to_file("out2.png")错哪了

import numpy as np from PIL import Image # 加载图片和文本数据 star_mask = np.array(Image.open("star.png")) f = open("text.txt", "r", encoding="utf-8") t = f.read() f.close() # 对文本进行分词处理 ls ...

mport cv2 import numpy as np svm = cv2.ml.SVM_load("svm.xml") cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY_INV) kernel = np.ones((5,5), np.uint8) dilation = cv2.dilate(thresh, kernel, iterations=1) contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: (x, y, w, h) = cv2.boundingRect(contour) if w < 5 or h < 5: continue cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) roi = thresh[y:y + h, x:x + w] roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA) feature = np.reshape(roi, (1, 28 * 28)).astype(np.float32) result = svm.predict(feature)[1][0][0] cv2.putText(frame, str(int(result)), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) cv2.imshow("frame", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # 关闭摄像头 cap.release() cv2.destroyAllWindows()

1. 导入OpenCV和numpy库，并加载SVM模型。 2. 打开本地摄像头，并在while循环中读取每一帧图像。 3. 将图像转换为灰度图像，并进行二值化和膨胀，以便更好地识别数字轮廓。 4. 使用OpenCV的轮廓检测功能，找到...

修改代码为找出区间[-2,40]之间的曲率极大值点的横坐标：mport numpy as np # 定义高斯核函数 def gkernel(x, x0, sig): return np.exp(-(x-x0)**2/(2*sig2)) # 定义曲率函数 def curvature(x, y): dy = np.gradient(y, x) ddy = np.gradient(dy, x) k = np.abs(ddy) / (1 + dy2)**1.5 return k # 定义参数和数组 x1 = np.linspace(-2, 40, 10) x2 = np.linspace(-2, 40, 100) sig = 1 w = 1 y_rec = np.zeros_like(x2) curv_list = [] # 计算曲率值 for xi in x2: y = y_rec.copy() for k, xk in enumerate(x1): y += w * gkernel(xi, xk, sig) curv = curvature(x2, y) curv_list.append(curv[0]) # 找到曲率值最大的四个点 idx_max = np.argsort(curv_list)[-10:] x_max = x2[idx_max] x_max_diff = np.diff(x_max) while np.any(x_max_diff < 2): idx = np.argmin(x_max_diff) x_max[idx+1] += 1 x_max_diff = np.diff(x_max) print("曲率最大的十个点的横坐标为：", x_max)

import numpy as np import matplotlib.pyplot as plt # 定义高斯核函数 def gkernel(x, x0, sig): return np.exp(-(x-x0)**2/(2*sig**2)) # 定义曲率函数 def curvature(x, y): dy = np.gradient(y, x) ddy = ...

mport matplotlib.pyplot as plt from mpl_toolkits.mplot3d.art3d import Poly3DCollection # 读取3D医学图像数据 data = ... # 获取数据的形状和边界 x, y, z = data.shape xmin, xmax = 0, x ymin, ymax = 0, y zmin, zmax = 0, z # 创建3D图形对象 fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # 创建立方体并添加到图形中 verts = [(xmin, ymin, zmin), (xmax, ymin, zmin), (xmax, ymax, zmin), (xmin, ymax, zmin), (xmin, ymin, zmax), (xmax, ymin, zmax), (xmax, ymax, zmax), (xmin, ymax, zmax)] faces = [[0, 1, 2, 3], [4, 5, 6, 7], [0, 1, 5, 4], [1, 2, 6, 5], [2, 3, 7, 6], [3, 0, 4, 7]] collection = Poly3DCollection([verts[face] for face in faces], alpha=0.25, facecolor='blue', edgecolor='k') ax.add_collection3d(collection) # 设置坐标轴范围和标签 ax.set_xlim3d(xmin, xmax) ax.set_ylim3d(ymin, ymax) ax.set_zlim3d(zmin, zmax) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') # 显示图形 plt.show()

以上代码是使用 matplotlib 库绘制一个三...每个四元组中的四个整数分别对应于 verts（顶点）列表中的顶点索引，通过连接这些点可绘制出立方体的各个面。在这里，faces 中的每个四元组的顺序都是按照逆时针方向的。

python:mport string from zhon.hanzi import punctuation with open('shuihuzhuan.txt', 'r', encoding='utf-8') as f: txt = f.read() for fh in punctuation: txt = str(txt).replace(fh,' ') print(txt) import jieba # 使用jieba库进行中文分词 words = list(jieba.cut(txt)) # 统计每个两个字以上词语出现的次数 word_count = {} for word in words: if len(word) >= 2: word_count[word] = word_count.get(word, 0) + 1 # 按照词频从大到小排序 word_count_sorted = sorted(word_count.items(), key=lambda x: x[1], reverse=True) # 输出前20个结果 top_words = [x[0] for x in word_count_sorted[:20]] print('即将出现前20个词频') for word in top_words: print(f'{word}: {word_count[word]}') # 将结果保存到文件中 with open('shuihuzhuan_word_count.txt', 'w', encoding='utf-8') as f: for word, count in word_count_sorted: f.write(f"{word}: {count}\n") import matplotlib.pyplot as plt # 取出Top20的词和词频 from pylab import mpl mpl.rcParams["font.sans-serif"] = ["SimHei"] # 设置显示中文字体 mpl.rcParams["axes.unicode_minus"] = False # 设置正常显示符号 plt.figure(figsize=(10, 5)) plt.bar(range(len(top_words)), [word_count[word] for word in top_words], align='center') plt.xticks(range(len(top_words)), top_words, fontsize=12, rotation=45) plt.xlabel(' ',fontsize=14) plt.ylabel(' ',fontsize=14) plt.title('数据来自水浒传', fontsize=16) plt.show()如何理解如何写出来的

这段代码是一个Python程序，目的是对《水浒传》这本小说进行中文分词，并统计每个词语出现的次数。程序首先读取《水浒传》这本小说的文本文件，然后通过引入zhon.hanzi库中的中文标点符号和jieba库进行中文分词。...

mport numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D 创建网格点 x = np.linspace(-4, 4, 100) y = np.linspace(1, 3, 100) z = np.linspace(-4, 4, 100) 创建二维网格 x, y = np.meshgrid(x, y) 第一个曲面方程 eq1 = x2 + z2 + y**2 - 4*y 第二个曲面方程 eq2 = x2 + y2 + z**2 - 4 设置绘图 fig = plt.figure() ax = fig.add_subplot(111, projection='3d') 绘制第一个曲面 ax.plot_surface(x, y,z,eq1, cmap='viridis') 绘制第二个曲面 ax.plot_surface(x, y,z,eq2, cmap='plasma') 设置坐标轴,标签 ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') 显示图形 plt.show() TypeError: Axes3D.plot_surface() takes 4 positional arguments but 5 were given

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # 创建网格点 x = np.linspace(-4, 4, 100) y = np.linspace(1, 3, 100) z = np.linspace(-4, 4, 100) # 创建二维...

mport pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv('data（北深）.csv') df['date'] = pd.to_datetime(df['date']) # 将日期字符串转换为日期格式 df['Month'] = df['date'].dt.month # 增加一列表示月份 df['days_to_departure'] = df['days_to_departure'].astype(int) # 将天数转换为整数类型 sns.set(style='whitegrid') fig, ax = plt.subplots(figsize=(10, 10)) sns.heatmap(df.pivot_table(index='days_to_departure', columns='date', values='lowest_price'), cmap='YlOrRd', ax=ax) ax.set_title('Flight Price Heatmap') ax.set_xlabel('Date') ax.set_ylabel('Days to Departure') plt.show()上述代码生成的热力图中将横轴的日期格式改为YYYY- MM- DD的形式

import numpy as np import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv('data（北深）.csv') df['date'] = pd.to_datetime(df['date']) df['Month'] = df['date'].dt.month df['days_...

帮我看看下面代码哪里错了：mport os,sys,torch # sys.path.append(os.path.dirname()) import numpy as np import scipy from scipy import ndimage import tensorflow as tf import matplotlib.pyplot as plt from keras.applications.resnet50 import ResNet50 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions from PIL import Image import random # from numba import cuda报错：在 'init.py' 中找不到引用 'resnet50'

从错误信息来看，可能是你的keras版本过低导致的。请尝试升级你的keras版本...pip install --upgrade tensorflow-gpu keras numpy scipy matplotlib pillow 如果还有问题，请提供完整的错误信息以便更好地帮助你。

mport numpy as np import scipy.stats as ss import pandas as pd import matplotlib.pyplot as plt import seaborn as sns df=pd.read_excel("./tmp_apply.xlsx") sns.set_context(font_scale=20) plt.rcParams['font.family'] = 'FangSong' plv_tb=pd.pivot_table(df,values="sale",index=["FactoryName","JiJXH"],columns=["Xian"],aggfunc=np.mean) fig, ax = plt.subplots(figsize=(200,10)) sns.heatmap(plv_tb,vmin=plv_tb.min().min(), vmax=plv_tb.max().max(),cmap=sns.color_palette("RdYlBu",n_colors=10),annot=True, fmt='g', annot_kws={"size": 20}, ax=ax) plt.subplots_adjust(left=0.2, bottom=0.2) # 调整左边和底部的距离 plt.show() 怎么使热力图和色阶图距离变小

你可以通过调整 plt.subplots_adjust() 方法中的参数来调整热力图和色阶图之间的距离。具体来说，你可以将 left 和 bottom 的值减小，例如： python plt.subplots_adjust(left=0.1, bottom=0.1) ...

mport numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" half_window = (window_size - 1) // 2 data_length = len(data) smoothed_data = np.zeros(data_length) for i in range(half_window, data_length - half_window): window = data[i - half_window:i + half_window + 1] median = np.median(window) deviation = np.abs(window - median) threshold = smooth_factor * np.median(deviation) if np.abs(data[i] - median) > threshold: smoothed_data[i] = median else: smoothed_data[i] = data[i] # 处理首尾值 smoothed_data[:half_window] = data[:half_window] smoothed_data[data_length - half_window:] = data[data_length - half_window:] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 3 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)这段代码如果输入数据有很多维度怎么改

import numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" ...

mport requests from bs4 import BeautifulSoup import csv def get_top250_movies(): url = 'https://movie.douban.com/top250' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} movie_info_list = [] for i in range(0, 250, 25): params = {'start': str(i)} res = requests.get(url, headers=headers, params=params) soup = BeautifulSoup(res.text, 'html.parser') movie_list = soup.find_all('div', class_='info') for movie in movie_list: title = movie.find('span', class_='title').text info = movie.find('div', class_='bd').p.text.strip().split('\n') director = info[0][4:] actors = info[1][3:] year = info[1][-5:-1] rating = movie.find('span', class_='rating_num').text comment_num = movie.find('div', class_='star').find_all('span')[3].text[:-3] movie_info_list.append([title, director, actors, year, rating, comment_num]) return movie_info_list def save_to_csv(movie_info_list): with open('movie_info.csv', 'w', newline='', encoding='utf-8-sig') as f: writer = csv.writer(f) writer.writerow(['电影名称', '导演', '演员', '上映年份', '评分', '评论数']) for movie_info in movie_info_list: writer.writerow(movie_info) if name == 'main': movie_info_list = get_top250_movies() save_to_csv(movie_info_list) print('电影信息保存成功！') 在此代码的基础上对爬取的电影类型进行生成按照评分生成词云

import matplotlib.pyplot as plt # 从电影信息中获取所有电影类型 genre_list = [] for movie_info in movie_info_list: genres = movie_info[2].split('/') genre_list.extend(genres) # 生成词云 wordcloud =...

mport jieba import jieba.posseg as pseg from utils.dbutils import * from question_answer.获取天气情况 import * import re from utils.user_base import * jieba.enable_paddle() def get_loc_list(text): per_list = [] # 人名列表 word_list = jieba.lcut(text) # print(word_list) for word in word_list: if len(word)==1: # 不加判断会爆 continue words = pseg.cut(word, use_paddle=True) # paddle模式 # print(list(words)) word, flag = list(words)[0] if flag=='LOC': # 这里写成LOC是地名 per_list.append(word) per_list = list(set(per_list)) print(per_list) if len(per_list)==0: per_list.append(word_list[0]) return per_list

这段代码是一个函数，用于从文本中提取出地名列表。它使用了jieba库对文本进行分词，并使用了PaddlePaddle深度学习库来进行词性标注。具体来说，它首先将文本进行分词，然后对每个词进行词性标注，最后将词性为“LOC...

mport pandas as pd from sklearn.feature_extraction.text import CountVectorizer # 读取CSV文件 df = pd.read_csv(r'F:\RIOS\Patents\fillter.csv') # 获取要计算词向量的列 text = df['filtered_draw_desc_text'].values # 计算词向量 vectorizer = CountVectorizer() X = vectorizer.fit_transform(text) 阅读这个代码并在这个基础上生成一个新的表，这个表有两个attribution，一个是单词名称，一个是对应向量

可以使用以下代码将稀疏矩阵表示的词向量转化为一个DataFrame表格： # 将稀疏矩阵转化为DataFrame df_words = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names()) # 添加单词名称列 ...

mport numpy as np ModuleNotFoundError: No module named 'numpy'

### 解决 Python 导入 Numpy 时 ModuleNotFoundError 错误当遇到 ModuleNotFoundError: No module named 'numpy' 的错误提示时，这通常意味着当前使用的 Python 环境未能找到名为 numpy 的包。此问题可能源于...

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