import jittor as jt import jrender as jr jt.flags.use_cuda = 1 # 开启GPU加速 import os import tqdm import numpy as np import imageio import argparse # 获取当前文件所在目录路径和数据目录路径 current_dir = os.path.dirname(os.path.realpath(__file__)) data_dir = os.path.join(current_dir, 'data') def main(): # 创建命令行参数解析器 parser = argparse.ArgumentParser() parser.add_argument('-i', '--filename-input', type=str, default=os.path.join(data_dir, 'obj/spot/spot_triangulated.obj')) parser.add_argument('-o', '--output-dir', type=str, default=os.path.join(data_dir, 'results/output_render')) args = parser.parse_args() # other settings camera_distance = 2.732 elevation = 30 azimuth = 0 # load from Wavefront .obj file mesh = jr.Mesh.from_obj(args.filename_input, load_texture=True, texture_res=5, texture_type='surface', dr_type='softras') # create renderer with SoftRas renderer = jr.Renderer(dr_type='softras') os.makedirs(args.output_dir, exist_ok=True) # draw object from different view loop = tqdm.tqdm(list(range(0, 360, 4))) writer = imageio.get_writer(os.path.join(args.output_dir, 'rotation.gif'), mode='I') imgs = [] from PIL import Image for num, azimuth in enumerate(loop): # rest mesh to initial state mesh.reset_() loop.set_description('Drawing rotation') renderer.transform.set_eyes_from_angles(camera_distance, elevation, azimuth) rgb = renderer.render_mesh(mesh, mode='rgb') image = rgb.numpy()[0].transpose((1, 2, 0)) writer.append_data((255*image).astype(np.uint8)) writer.close() # draw object from different sigma and gamma loop = tqdm.tqdm(list(np.arange(-4, -2, 0.2))) renderer.transform.set_eyes_from_angles(camera_distance, elevation, 45) writer = imageio.get_writer(os.path.join(args.output_dir, 'bluring.gif'), mode='I') for num, gamma_pow in enumerate(loop): # rest mesh to initial state mesh.reset_() renderer.set_gamma(10**gamma_pow) renderer.set_sigma(10**(gamma_pow - 1)) loop.set_description('Drawing blurring') images = renderer.render_mesh(mesh, mode='rgb') image = images.numpy()[0].transpose((1, 2, 0)) # [image_size, image_size, RGB] writer.append_data((255*image).astype(np.uint8)) writer.close() # save to textured obj mesh.reset_() mesh.save_obj(os.path.join(args.output_dir, 'saved_spot.obj')) if __name__ == '__main__': main()在每行代码后添加注释

hci_packet.rar_As They Were

For backward compatibility, lower 16 bits used as they were. Lower 16 bit: NIC_TYPE values Uppoer 16 bit: NIC_TYPE Flags.

解释这段代码import jittor as jt from jittor import nn jt.flags.use_cuda = 1 import os import tqdm import numpy as np import imageio import argparse import jrender as jr from jrender import neg_iou_loss, LaplacianLoss, FlattenLoss current_dir = os.path.dirname(os.path.realpath(file)) data_dir = os.path.join(current_dir, 'data') class Model(nn.Module): def init(self, template_path): super(Model, self).init() # set template mesh self.template_mesh = jr.Mesh.from_obj(template_path, dr_type='n3mr') self.vertices = (self.template_mesh.vertices * 0.5).stop_grad() self.faces = self.template_mesh.faces.stop_grad() self.textures = self.template_mesh.textures.stop_grad() # optimize for displacement map and center self.displace = jt.zeros(self.template_mesh.vertices.shape) self.center = jt.zeros((1, 1, 3)) # define Laplacian and flatten geometry constraints self.laplacian_loss = LaplacianLoss(self.vertices[0], self.faces[0]) self.flatten_loss = FlattenLoss(self.faces[0]) def execute(self, batch_size): base = jt.log(self.vertices.abs() / (1 - self.vertices.abs())) centroid = jt.tanh(self.center) vertices = (base + self.displace).sigmoid() * nn.sign(self.vertices) vertices = nn.relu(vertices) * (1 - centroid) - nn.relu(-vertices) * (centroid + 1) vertices = vertices + centroid # apply Laplacian and flatten geometry constraints laplacian_loss = self.laplacian_loss(vertices).mean() flatten_loss = self.flatten_loss(vertices).mean() return jr.Mesh(vertices.repeat(batch_size, 1, 1), self.faces.repeat(batch_size, 1, 1), dr_type='n3mr'), laplacian_loss, flatten_loss

然后将 Jittor 框架的 GPU 标志设置为使用 CUDA。接下来导入了一些 Python 标准库和 JRender 库。在代码中定义了一个名为 Model 的类，它继承自 nn.Module。在类的构造函数中，使用 JRender 库从给定的模板路径加载...

解释这段代码import jittor as jt import jrender as jr jt.flags.use_cuda = 1 import os import tqdm import numpy as np import imageio import argparse current_dir = os.path.dirname(os.path.realpath(file)) data_dir = os.path.join(current_dir, 'data') def main(): parser = argparse.ArgumentParser() parser.add_argument('-i', '--filename-input', type=str, default=os.path.join(data_dir, 'obj/spot/spot_triangulated.obj')) parser.add_argument('-o', '--output-dir', type=str, default=os.path.join(data_dir, 'results/output_render')) args = parser.parse_args() # other settings camera_distance = 2.732 elevation = 30 azimuth = 0 # load from Wavefront .obj file mesh = jr.Mesh.from_obj(args.filename_input, load_texture=True, texture_res=5, texture_type='surface', dr_type='softras') # create renderer with SoftRas renderer = jr.Renderer(dr_type='softras') os.makedirs(args.output_dir, exist_ok=True) # draw object from different view loop = tqdm.tqdm(list(range(0, 360, 4))) writer = imageio.get_writer(os.path.join(args.output_dir, 'rotation.gif'), mode='I') imgs = [] from PIL import Image for num, azimuth in enumerate(loop): # rest mesh to initial state mesh.reset_() loop.set_description('Drawing rotation') renderer.transform.set_eyes_from_angles(camera_distance, elevation, azimuth) rgb = renderer.render_mesh(mesh, mode='rgb') image = rgb.numpy()[0].transpose((1, 2, 0)) writer.append_data((255image).astype(np.uint8)) writer.close() # draw object from different sigma and gamma loop = tqdm.tqdm(list(np.arange(-4, -2, 0.2))) renderer.transform.set_eyes_from_angles(camera_distance, elevation, 45) writer = imageio.get_writer(os.path.join(args.output_dir, 'bluring.gif'), mode='I') for num, gamma_pow in enumerate(loop): # rest mesh to initial state mesh.reset_() renderer.set_gamma(10gamma_pow) renderer.set_sigma(10(gamma_pow - 1)) loop.set_description('Drawing blurring') images = renderer.render_mesh(mesh, mode='rgb') image = images.numpy()[0].transpose((1, 2, 0)) # [image_size, image_size, RGB] writer.append_data((255image).astype(np.uint8)) writer.close() # save to textured obj mesh.reset_() mesh.save_obj(os.path.join(args.output_dir, 'saved_spot.obj')) if name == 'main': main()

1. 导入必要的库，包括 Jittor、Jrender、os、tqdm、numpy、imageio 和 argparse。 2. 定义了两个变量 current_dir 和 data_dir，用于指定当前目录和数据目录。 3. 定义了一个 main() 函数，用于实现程序的...

详细分析代码“from future import absolute_import from future import division from future import print_function import argparse import sys import numpy as np import pandas as pd from sklearn import metrics import tensorflow as tf learn = tf.contrib.learn FLAGS = None # 文档最长长度 MAX_DOCUMENT_LENGTH = 100 # 最小词频数 MIN_WORD_FREQUENCE = 2 # 词嵌入的维度 EMBEDDING_SIZE = 20 # filter个数 N_FILTERS = 10 # 10个神经元 # 感知野大小 WINDOW_SIZE = 20 #filter的形状 FILTER_SHAPE1 = [WINDOW_SIZE, EMBEDDING_SIZE]#[20,20] FILTER_SHAPE2 = [WINDOW_SIZE, N_FILTERS] #[20,10] # 池化 POOLING_WINDOW = 4 POOLING_STRIDE = 2 n_words = 0 def cnn_model(features, target): ”详细分析每一句代码含义，作用

6. import numpy as np: 用于处理数值数组。 7. import pandas as pd: 用于处理数据表格。 8. from sklearn import metrics: 用于评估模型的性能。 9. import tensorflow as tf: 引入TensorFlow库。 10. ...

import cv2 import numpy as np import torch as torch from torchvision.models import densenet121 # Load the DenseNet model model = densenet121(pretrained=True) # Read the image image = cv2.imread('C:/Users/23594/Desktop/888.jpg') # Convert the image to grayscale grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Resize the image to the size of the model input resized_image = cv2.resize(grayscale_image, (224, 224)) # Normalize the image normalized_image = resized_image / 255.0 # Convert the image to a tensor image_tensor = torch.from_numpy(normalized_image).float() # Predict the key points of the person predictions = model(image_tensor) # Convert the predictions to a list of points points = [] for i in range(len(predictions[0])): points.append((predictions[0][i][0], predictions[0][i][1])) # Draw the key points on the image cv2.drawKeypoints(image, points, np.array([]), (0, 255, 0), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # Display the image cv2.imshow('Image', image) cv2.waitKey(0)import cv2 import numpy as np import torch as torch from torchvision.models import densenet121 # Load the DenseNet model model = densenet121(pretrained=True) # Read the image image = cv2.imread('C:/Users/23594/Desktop/888.jpg') # Convert the image to grayscale grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Resize the image to the size of the model input resized_image = cv2.resize(grayscale_image, (224, 224)) # Normalize the image normalized_image = resized_image / 255.0 # Convert the image to a tensor image_tensor = torch.from_numpy(normalized_image).float() # Predict the key points of the person predictions = model(image_tensor) # Convert the predictions to a list of points points = [] for i in range(len(predictions[0])): points.append((predictions[0][i][0], predictions[0][i][1])) # Draw the key points on the image cv2.drawKeypoints(image, points, np.array([]), (0, 255, 0), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # Display the image cv2.imshow('Image', image) cv2.waitKey(0)

1. 导入必要的库，包括OpenCV、NumPy和PyTorch等。 2. 加载预训练的DenseNet模型。 3. 读取待检测的人物图片，并将其转为灰度图像。 4. 调整图片大小为与模型输入大小相同。 5. 将像素值归一化到0到1之间，并将其...

import cv2 import mediapipe as mp mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles mp_holistic = mp.solutions.holistic # For webcam input: filepath='F:\\video000\\ce.mp4' cap = cv2.VideoCapture(filepath) with mp_holistic.Holistic( min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic: while cap.isOpened(): success, image = cap.read() if not success: print("Ignoring empty camera frame.") # If loading a video, use 'break' instead of 'continue'. break # To improve performance, optionally mark the image as not writeable to # pass by reference. image.flags.writeable = False image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = holistic.process(image) # Draw landmark annotation on the image. image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) mp_drawing.draw_landmarks( image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, landmark_drawing_spec=None, connection_drawing_spec=mp_drawing_styles .get_default_face_mesh_contours_style()) mp_drawing.draw_landmarks( image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, landmark_drawing_spec=mp_drawing_styles .get_default_pose_landmarks_style()) # Flip the image horizontally for a selfie-view display. cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1)) if cv2.waitKey(5) & 0xFF == 27: break cap.release() 在这段代码中引入注意力机制，使人体步态特征更好的提取

可以在这段代码中引入注意力机制，来增强人体步态特征的提取。具体来说，可以在姿态估计模型的输出中，加入注意力机制来突出人体关键点的重要性。例如，可以使用自注意力机制（self-attention）来对输入的关键点序列...

import cv2 import numpy as np def main(): # 1.导入图片 img_src = cv2.imread("cc.jpg") # 2.灰度化,二值化 img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY) ret, img_bin = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY) # 3.连通域分析 img_bin, contours, hierarchy = cv2.findContours(img_bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # 4.获取最小外接矩形并打印 min_rect = cv2.minAreaRect(contours[0]) print("返回值min_rect:\n", min_rect) rect_points = cv2.boxPoints(min_rect) print("返回值rect_points:\n", rect_points) rect_points = np.int0(rect_points) # 5.绘制最小外接矩形 img_result = img_src.copy() cv2.drawContours(img_result, [rect_points], 0, (255, 255, 255), 2) # 6.显示结果图片 cv2.imshow("img_src", img_src) cv2.imshow("img_result", img_result) cv2.waitKey() cv2.destroyAllWindows() if name == 'main': main()怎么把图像摆正

img_result_rotated = cv2.warpAffine(img_result, M, img_src.shape[1::-1], flags=cv2.INTER_CUBIC) 这将生成一个旋转后的图像img_result_rotated。您可以将其保存为图像文件并使用cv2.imshow()函数显示。

补全下面代码中模型预测部分import paho.mqtt.client as mqtt import numpy as np import json import tensorflow as tf from tensorflow.python.keras.backend import set_session model = tf.keras.models.load_model('flowers.hd5') classes=["daisy","dandelion","roses","sunflowers","tulips"] def on_connect(client,userdata,flags,rc): if rc==0: print("successfully connected to broker.") client.subscribe("Group_04/IMAGE/classify") else: print("Connection failed with code: %d." %rc) def classify_flower(filename,data): print("Start classifying") model.predict(data) print("Done.") return {"filename":filename,"prediction":classes[win],"score":0.99,"index":win} def on_message(client,userdata,msg): recv_dict=json.loads(msg.payload) img_data=np.array(recv_dict["data"]) result=classify_flower(recv_dict["filename"],img_data) print("Sending results: ",result) client.publish("Group_04/IMAGE/predict",json.dumps(result)) def setup(hostname): client=mqtt.Client() client.on_connect=on_connect client.on_message=on_message client.connect(hostname) client.loop_start() return client def main(): model = tf.keras.models.load_model('flowers.hd5') client=setup("172.17.0.3") for filename in os.listdir(samples_dir): file_path=os.path.join(samples_dir,filename) classify_flower(file_path) while True: pass if name=='main': main()

接下来，我们使用加载的模型对输入数据进行预测，并通过 np.argmax 函数获取最高概率的类别索引。最后，我们构造一个结果字典，包含文件名、预测类别、预测得分和类别索引，并将其返回。请确保在补全代码之前...

import cv2 import numpy as np from matplotlib import pyplot as plt # 读取原始图像 image = cv2.imread('D:\\QMJC\\2023.7.12\\TIAOWEN\\AVERAGE\\30A.png', 0) # 进行二维离散傅里叶变换（DFT） dft = cv2.dft(np.float32(image), flags=cv2.DFT_COMPLEX_OUTPUT) dft_shift = np.fft.fftshift(dft) # 创建高通滤波器 rows, cols = image.shape crow, ccol = rows // 2, cols // 2 # 中心点 mask = np.ones((rows, cols, 2), np.uint8) mask[crow - 30: crow + 30, ccol - 30: ccol + 30] = 0 # 选择中心区域，半径为30 # 应用滤波器 fshift = dft_shift * mask # 进行逆移位操作 f_ishift = np.fft.ifftshift(fshift) # 进行逆傅里叶变换（IDFT） filtered_image = cv2.idft(f_ishift) filtered_image = cv2.magnitude(filtered_image[:, :, 0], filtered_image[:, :, 1]) cv2.imwrite('D:\\QMJC\\2023.7.12\\TIAOWEN\\IMAGE\\000.png',filtered_image) # 显示原始图像和滤波后的图像 plt.subplot(121), plt.imshow(image, cmap='gray') plt.title('Original Image'), plt.xticks([]), plt.yticks([]) plt.subplot(122), plt.imshow(filtered_image, cmap='gray') plt.title('Filtered Image'), plt.xticks([]), plt.yticks([]) plt.show()

代码看起来基本正确，您使用了OpenCV库进行了傅里叶变换和滤波操作，然后使用Matplotlib库进行图像显示。您选择了半径为30的中心区域作为高通滤波器的掩码，并应用于傅里叶变换后的频域图像。最后，通过逆傅里叶变换...

import numpy as np import cv2 as cv import matplotlib.pyplot as plt img1 = cv.imread('D:/junzhi/3-7000-10k.png', ) img2 = cv.imread('D:/junzhi/3-7000-10y.png', ) 初始化 AKAZE 探测器 akaze = cv.AKAZE_create() 使用 SIFT 查找关键点和描述 kp1, des1 = akaze.detectAndCompute(img1, None) kp2, des2 = akaze.detectAndCompute(img2, None) BFMatcher 默认参数 bf = cv.BFMatcher() matches = bf.knnMatch(des1, des2, k=2) 旋转测试 good_matches = [] for m,n in matches: if m.distance < 0.75*n.distance: good_matches.append([m]) 画匹配点 img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good_matches,None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) #cv.imwrite('matches.png', img3) 选择匹配关键点 ref_matched_kpts = np.float32([kp1[m[0].queryIdx].pt for m in good_matches]).reshape(-1,1,2) sensed_matched_kpts = np.float32([kp2[m[0].trainIdx].pt for m in good_matches]).reshape(-1,1,2) 计算 homography H, status = cv.findHomography(ref_matched_kpts, sensed_matched_kpts, cv.RANSAC,1.0) 变换 warped_image = cv.warpPerspective( img2, H, (img2.shape[1], img2.shape[0])) cv.imwrite('warped.png', warped_image)改进这个程序使变换图像前后位深度一致

import numpy as np import cv2 as cv import matplotlib.pyplot as plt img1 = cv.imread('D:/junzhi/3-7000-10k.png') img2 = cv.imread('D:/junzhi/3-7000-10y.png') # 初始化 AKAZE 探测器 akaze = cv.AKAZE_...

import cv2 import numpy as np # 读取两幅图像 img1 = cv2.imread('D:\wzk\JIEMIAN\images\er_duibidu.jpg') img2 = cv2.imread('D:\wzk\JIEMIAN\images\yi_duibidu.jpg') # 将两幅图像转换为灰度图像 gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) # 提取图像特征点 orb = cv2.ORB_create() kp1, des1 = orb.detectAndCompute(gray1, None) kp2, des2 = orb.detectAndCompute(gray2, None) # 匹配特征点 matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING) matches = matcher.match(des1, des2) # 选择最佳匹配点 matches = sorted(matches, key=lambda x: x.distance) good_matches = matches[:int(len(matches)*0.15)] # 绘制特征点连接图 img3 = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) # 计算变换矩阵 src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2) dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) # 拼接图像 result = cv2.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0])) result[0:img2.shape[0], 0:img2.shape[1]] = img2 # 保存连接图 cv2.imwrite('D:\wzk\JIEMIAN\Result\ORB-pz.jpg', img3) # 保存第二幅图像 cv2.imwrite('D:\wzk\JIEMIAN\Result\ORB-pj.jpg', result) # 显示结果 cv2.namedWindow("Keypoint Matches", cv2.WINDOW_NORMAL) cv2.imshow("Keypoint Matches", img3) cv2.namedWindow("Result",cv2.WINDOW_NORMAL) cv2.imshow('Result', result) cv2.waitKey(0) cv2.destroyAllWindows()改进为对文件夹内的多幅图像进行配准拼接

import numpy as np import os # 定义文件夹路径和结果保存路径 folder_path = 'D:\wzk\JIEMIAN\images' result_path = 'D:\wzk\JIEMIAN\Result\ORB-pj.jpg' # 获取文件夹内所有图像路径 img_paths = [os.path....

import paho.mqtt.client as mqtt import numpy as np import json #插入模型 import torch import torchvision.transforms as transforms from PIL import Image classes=["daisy","dandelion","roses","sunflowers","tulips"] #连接函数 def on_connect(client,userdata,flags,rc): if rc==0: print("successfully connected to broker.") client.subscribe("Group_04/IMAGE/classify") else: print("Connection failed with code: %d." %rc) #分类 def classify_flower(filename,data): print("Start classifying") win=4 print("Done.") return {"filename":filename,"prediction":classes[win],"score":0.99,"index":win} #消息处理 def on_message(client,userdata,msg): recv_dict=json.loads(msg.payload) img_data=np.array(recv_dict["data"]) result=classify_flower(recv_dict["filename"],img_data) print("Sending results: ",result) client.publish("Group_04/IMAGE/predict",json.dumps(result)) # def setup(hostname): client=mqtt.Client() client.on_connect=on_connect client.on_message=on_message client.connect(hostname) client.loop_start() return client def main(): client=setup("172.17.0.3") while True: pass if name=='main': main() 请补全插入模型分类相关的代码

import torchvision.transforms as transforms from PIL import Image # 加载模型 model = torch.load("flower_model.pth") model.eval() # 图像预处理函数 def preprocess_image(image): transform = transforms...

img=cv.imread("E:\python\project\gray.jpg") img_float32=np.float32(img) dft=cv.dft(img_float32,flags=cv.DFT_COMPLEX_OUTPUT) dft_shift=np.fft.fftshift(dft) rows,cols=img.shape crow,ccol=int(row/2),int(cols/2) mask=np.zeros((rows,cols,2),np.uint8) mask[crow-30:crow+30,ccol-30:ccol+30]=1 fshift=dft_shift*mask f_ishift=np.fft.ifftshift(fshift) img_back=cv.idft(f_ishift) img_back=cv.magnitude(img_back[:,:,0],img_back[:,:,1]) plt.subplot(121).plt.imshow(img,camp='gray') plt.title('input image'),plt.xticks([]),plt.yticks([]) plt.subplot(122),plt.imshow(img_back,camp=gray) plt.titele('result'),plt.xticks([]),plt.yticks([]) plt.show() cv.waitKey(0) #按下任意键后再关闭显示窗口 cv.destroyAllWindows() 修改代码，解决图像不显示问题

import numpy as np import matplotlib.pyplot as plt img = cv.imread("E:\python\project\gray.jpg") img_float32 = np.float32(img) dft = cv.dft(img_float32, flags=cv.DFT_COMPLEX_OUTPUT) dft_shift = np....

import io import re import tkinter import requests import threading import tqdm from pydub import AudioSegment root = tkinter.Tk() root.title('在线视频解析') root.geometry('500x590+550+350') headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/115.0'} ac = tkinter.Listbox(root, width=50, height=20, font=('黑体', 12)) ac.grid(row=2, columnspan=10, sticky="n" + "s" + "w" + "e") def sousuo(): i = b1.get() ac.delete(0, 'end') def extract_music_info(content): p = '|' content = re.sub(p, '', content, flags=re.S) pattern = re.compile('subject.?href="(.?)">(.?)', flags=re.S) return pattern.findall(content) def search_music(): url = 'https://www.hifini.com/search-' + i + '-1.htm' response = requests.get(url=url, headers=headers) return response.text def update_listbox(music_list): for music in music_list: pppp = music[1] + ":" + music[0] ac.insert('end', pppp) content = search_music() music_list = extract_music_info(content) update_listbox(music_list) def xiazzi(): def download_music(): ppp = ac.get(ac.curselection()) pp = re.search('thread.?htm', ppp) v = pp.group() url1 = 'https://www.hifini.com/' + v response = requests.get(url=url1, headers=headers) ppp = response.text l2 = re.search('<script>.?title:..(.?).,.?author:.(.?).,.?url:..(.?).,', ppp, flags=re.S) p = 'https://www.hifini.com/' + l2.group(3) response = requests.get(url=p, headers=headers, stream=True) # 设置 stream=True 以启用流式下载 total_size = int(response.headers.get('Content-Length')) music_name = '{}-{}.mp3'.format(l2.group(2), l2.group(1)) progress_bar = tqdm(total=total_size, unit='B', unit_scale=True) # 创建进度条 with open(music_name, 'wb') as f: for data in response.iter_content(chunk_size=1024): progress_bar.update(len(data)) # 更新进度条 f.write(data) progress_bar.close() # 关闭进度条 print(music_name) threading.Thread(target=download_music).start() a1 = tkinter.Label(root, text='音乐下载器', anchor="center", font=('黑体', 24)) a1.grid(row=0, columnspan=10, sticky="n" + "s" + "w" + "e") b1 = tkinter.Entry(root, width=35, font=('黑体', 16), ) b1.grid(row=1, column=3, padx=15) search_button = tkinter.Button(root, text='搜索', command=sousuo) search_button.grid(row=1, column=4) download_button = tkinter.Button(root, text='下载', command=xiazzi) download_button.grid(row=3, column=4) root.mainloop() 报错TypeError: 'module' object is not callable

你可以尝试更改导入语句，将 tqdm 给一个不同的变量名，例如将 import tqdm 改为 import tqdm as td。然后在代码中使用 td 来表示 tqdm 模块。以下是修改后的代码示例： python import io import re...

import cv2 import numpy as np #读入需要配准的两张图像 img1 = cv2.imread('men4.jpg') img2 = cv2.imread('men3.jpg') #将图像转换为灰度图像 gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) #使用 Shi-Tomasi 算法寻找关键点并计算特征描述子 sift = cv2.SIFT_create() kp1, des1 = sift.detectAndCompute(gray1, None) kp2, des2 = sift.detectAndCompute(gray2, None) #使用 FLANN 匹配器进行特征匹配 FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1, des2, k=2) #选择好的匹配点 good = [] for m, n in matches: if m.distance < 0.7 * n.distance: good.append(m) #获取匹配点对应的坐标 src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2) dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2) #使用 RANSAC 算法进行配准 M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) #对第一张图像进行变换并输出结果 result = cv2.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0])) #将第二张图像拼接到全景图中 result[0:img2.shape[0], img1.shape[1]:img1.shape[1] + img2.shape[1]] = img2 #输出全景图 cv2.namedWindow("result",cv2.WINDOW_NORMAL) cv2.imshow('result', result) cv2.waitKey(0) cv2.destroyAllWindows()改进这段代码，使其能够输出匹配连线图

import numpy as np # 读入需要配准的两张图像 img1 = cv2.imread('men4.jpg') img2 = cv2.imread('men3.jpg') # 将图像转换为灰度图像 gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(img...

import re,tkinter,requests,threading,tqdm as tt root = tkinter.Tk() root.title('在线视频解析') root.geometry('500x590+550+350') headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/115.0'} ac = tkinter.Listbox(root, width=50, height=20, font=('黑体', 12)) ac.grid(row=2, columnspan=10, sticky="n" + "s" + "w" + "e") def sousuo(): i = b1.get() ac.delete(0, 'end') def extract_music_info(content): p = '|' content = re.sub(p, '', content, flags=re.S) pattern = re.compile('subject.?href="(.?)">(.?)', flags=re.S) return pattern.findall(content) def search_music(): url = 'https://www.hifini.com/search-' + i + '-1.htm' response = requests.get(url=url, headers=headers) return response.text def update_listbox(music_list): for music in music_list: pppp = music[1] + ":" + music[0] ac.insert('end', pppp) content = search_music() music_list = extract_music_info(content) update_listbox(music_list) def xiazzi(): def download_music(): ppp = ac.get(ac.curselection()) pp = re.search('thread.?htm', ppp) v = pp.group() url1 = 'https://www.hifini.com/' + v response = requests.get(url=url1, headers=headers) ppp = response.text l2 = re.search('<script>.?title:..(.?).,.?author:.(.?).,.?url:..(.?).,', ppp, flags=re.S) p = 'https://www.hifini.com/' + l2.group(3) response = requests.get(url=p, headers=headers, stream=True) # 设置 stream=True 以启用流式下载 total_size = int(response.headers.get('Content-Length')) music_name = '{}-{}.mp3'.format(l2.group(2), l2.group(1)) progress_bar = tt.tqdm(total=total_size, unit='B', unit_scale=True) # 创建进度条 with open(music_name, 'wb') as f: for data in response.iter_content(chunk_size=1024): progress_bar.update(len(data)) # 更新进度条 f.write(data) progress_bar.close() # 关闭进度条 print(music_name) threading.Thread(target=download_music).start() a1 = tkinter.Label(root, text='音乐下载器', anchor="center", font=('黑体', 24)) a1.grid(row=0, columnspan=10, sticky="n" + "s" + "w" + "e") b1 = tkinter.Entry(root, width=35, font=('黑体', 16), ) b1.grid(row=1, column=3, padx=15) search_button = tkinter.Button(root, text='搜索', command=sousuo) search_button.grid(row=1, column=4) download_button = tkinter.Button(root, text='下载', command=xiazzi) download_button.grid(row=3, column=4) root.mainloop() 将download_button带有下载行为的按钮添加进列表，

import tqdm as tt root = tkinter.Tk() root.title('在线视频解析') root.geometry('500x590+550+350') headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Fire...

import jieba from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np import pandas as pd # 读取停用词文件 def read_stopwords(file_path): with open(file_path, 'r', encoding='gbk') as f: stopwords = [line.strip() for line in f] return set(stopwords) # 中文分词 def chinese_word_cut(text, stopwords): words = jieba.cut(text) result = [] for word in words: if word not in stopwords: result.append(word) return " ".join(result) # 读取CSV文件 weibo_data = pd.read_csv('E:\Python自然语言处理\data\weibo_Convid19.csv', sep='\t') df = weibo_data['text_raw'] # 获取停用词集合 stopwords = read_stopwords('E:\Python自然语言处理\data\stopword.txt') # 对每条微博进行分词和去停用词 corpus_list = df.apply(lambda x: chinese_word_cut(x, stopwords)) # 提取关键词 corpus = ' '.join(corpus_list) tfidf = TfidfVectorizer() tf_key = tfidf.fit_transform([corpus]) word = tfidf.get_feature_names() weight = tf_key.toarray()[0] w_sort = np.argsort(-weight) print('Top 20 keywords:') for i in range(20): print(word[w_sort[i]])结果含有表情包，怎么去除

import re def remove_emoji(text): emoji_pattern = re.compile("[" u"\U0001F600-\U0001F64F" # emoticons u"\U0001F300-\U0001F5FF" # symbols & pictographs u"\U0001F680-\U0001F6FF" # transport & map ...

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