pip install you-get -i http://pypi.douban.com/simple --trusted-host pypi.douban.com

you-get -i https://www.bilibili.com/video/BV1rD4y1Q7jc?from=search&seid=10634574434789745619
you-get -o 本地保存路徑 視頻鏈接 

import requests
import pandas as pd
import re
import time
import random
from concurrent.futures import ThreadPoolExecutor
import datetime
from fake_useragent import UserAgent
# 隨機(jī)產(chǎn)生請求頭
ua = UserAgent(verify_ssl=False, path='fake_useragent.json')
start_time = datetime.datetime.now()

def  Grab_barrage(date):
 # 偽裝請求頭
 headers = {
  "sec-fetch-dest": "empty",
  "sec-fetch-mode": "cors",
  "sec-fetch-site": "same-site",
  "accept-encoding": "gzip",
  "origin": "https://www.bilibili.com",
  "referer": "https://www.bilibili.com/video/BV1rD4y1Q7jc?from=search&seid=10634574434789745619",
  "user-agent": ua.random,
  "cookie": "chage to your cookies"
 }
 # 構(gòu)造url訪問需要用到的參數(shù)  爬取指定日期的彈幕
 params = {
  'type': 1,
  'oid': '206344228',
  'date': date
 }
 # 發(fā)送請求  獲取響應(yīng)
 response = requests.get(url, params=params, headers=headers)
 # print(response.encoding)重新設(shè)置編碼
 response.encoding = 'utf-8'
 # print(response.text)
 # 正則匹配提取數(shù)據(jù)  轉(zhuǎn)成集合去除重復(fù)彈幕
 comment = set(re.findall('<d p=".*?">(.*?)</d>', response.text))
 # 將每條彈幕數(shù)據(jù)寫入txt
 with open('bullet.txt', 'a+') as f:
  for con in comment:
f.write(con + '\n')
print(con)
 time.sleep(random.randint(1, 3))# 休眠

def main():
 # 開多線程爬取提高爬取效率
 with ThreadPoolExecutor(max_workers=4) as executor:
  executor.map(Grab_barrage, date_list)
 # 計(jì)算所用時(shí)間
 delta = (datetime.datetime.now() - start_time).total_seconds()
 print(f'用時(shí)：{delta}s  -----------> 彈幕數(shù)據(jù)成功保存到本地txt')

if __name__ == '__main__':
 # 目標(biāo)url
 url = "https://api.bilibili.com/x/v2/dm/history"
 start = '20201201'
 end = '20210128'
 # 生成時(shí)間序列
 date_list = [x for x in pd.date_range(start, end).strftime('%Y-%m-%d')]
 print(date_list)
 count = 0
 # 調(diào)用主函數(shù)
 main()

import cv2
# ============================ 視頻處理 分割成一幀幀圖片 =======================================
cap = cv2.VideoCapture(r"beauty.flv")
num = 1
while True:
 # 逐幀讀取視頻  按順序保存到本地文件夾
 ret, frame = cap.read()
 if ret:
  if 88 <= num < 888:
cv2.imwrite(f"./pictures/img_{num}.jpg", frame)# 保存一幀幀的圖片
print(f'========== 已成功保存第{num}張圖片 ==========')
  num += 1
 else:
  break
cap.release()# 釋放資源

# -*- coding: UTF-8 -*-
"""
@Author  ：葉庭云
@公眾號(hào)  ：修煉Python
@百度AI  ：https://ai.baidu.com/tech/body/seg
"""
import cv2
import base64
import numpy as np
import os
from aip import AipBodyAnalysis
import time
import random
# 利用百度AI的人像分割服務(wù) 轉(zhuǎn)化為二值圖  有小姐姐身影的蒙版
# 百度云中已創(chuàng)建應(yīng)用的  APP_ID API_KEY SECRET_KEY
APP_ID = '23485847'
API_KEY = 'VwGY053Y1A8ow3CFBTFrK0Pm'
SECRET_KEY = '**********************************'
client = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
# 保存圖像分割后的路徑
path = './mask_img/'
# os.listdir  列出保存到圖片名稱
img_files = os.listdir('./pictures')
print(img_files)
for num in range(88, len(img_files) + 1):
 # 按順序構(gòu)造出圖片路徑
 img = f'./pictures/img_{num}.jpg'
 img1 = cv2.imread(img)
 height, width, _ = img1.shape
 # print(height, width)
 # 二進(jìn)制方式讀取圖片
 with open(img, 'rb') as fp:
  img_info = fp.read()
 # 設(shè)置只返回前景也就是分割出來的人像
 seg_res = client.bodySeg(img_info)
 labelmap = base64.b64decode(seg_res['labelmap'])
 nparr = np.frombuffer(labelmap, np.uint8)
 labelimg = cv2.imdecode(nparr, 1)
 labelimg = cv2.resize(labelimg, (width, height), interpolation=cv2.INTER_NEAREST)
 new_img = np.where(labelimg == 1, 255, labelimg)
 mask_name = path + 'mask_{}.png'.format(num)
 # 保存分割出來的人像
 cv2.imwrite(mask_name, new_img)
 print(f'======== 第{num}張圖像分割完成 ========')
 time.sleep(random.randint(1,2))

# -*- coding: UTF-8 -*-
from wordcloud import WordCloud
import collections
import jieba
import re
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np

# 讀取數(shù)據(jù)
with open('bullet.txt') as f:
 data = f.read()
# 文本預(yù)處理  去除一些無用的字符只提取出中文出來
new_data = re.findall('[\u4e00-\u9fa5]+', data, re.S)
new_data = "/".join(new_data)
# 文本分詞
seg_list_exact = jieba.cut(new_data, cut_all=True)
result_list = []
with open('stop_words.txt', encoding='utf-8') as f:
 con = f.read().split('\n')
 stop_words = set()
 for i in con:
  stop_words.add(i)
for word in seg_list_exact:
 # 設(shè)置停用詞并去除單個(gè)詞
 if word not in stop_words and len(word) > 1:
  result_list.append(word)
# 篩選后統(tǒng)計(jì)詞頻
word_counts = collections.Counter(result_list)
path = './wordcloud/'
for num in range(88, 888):
 img = f'./mask_img/mask_{num}'
 # 獲取蒙版圖片
 mask_ = 255 - np.array(Image.open(img))
 # 繪制詞云
 plt.figure(figsize=(8, 5), dpi=200)
 my_cloud = WordCloud(
  background_color='black',  # 設(shè)置背景顏色  默認(rèn)是black
  mask=mask_,# 自定義蒙版
  mode='RGBA',
  max_words=500,
  font_path='simhei.ttf',# 設(shè)置字體  顯示中文
 ).generate_from_frequencies(word_counts)
 # 顯示生成的詞云圖片
 plt.imshow(my_cloud)
 # 顯示設(shè)置詞云圖中無坐標(biāo)軸
 plt.axis('off')
 word_cloud_name = path + 'wordcloud_{}.png'.format(num)
 my_cloud.to_file(word_cloud_name) # 保存詞云圖片
 print(f'======== 第{num}張?jiān)~云圖生成 ========')

# -*- coding: UTF-8 -*-
import cv2
import os
# 輸出視頻的保存路徑
video_dir = 'result.mp4'
# 幀率
fps = 30
# 圖片尺寸
img_size = (1920, 1080)
fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', 'V')  # opencv3.0 mp4會(huì)有警告但可以播放
videoWriter = cv2.VideoWriter(video_dir, fourcc, fps, img_size)
img_files = os.listdir('./wordcloud')
for i in range(88, 888):
 img_path = './wordcloud/' + 'wordcloud_{}.png'.format(i)
 frame = cv2.imread(img_path)
 frame = cv2.resize(frame, img_size)# 生成視頻圖片尺寸和設(shè)定尺寸相同
 videoWriter.write(frame)# 寫進(jìn)視頻里
 print(f'======== 按照視頻順序第{i}張圖片合進(jìn)視頻 ========')
videoWriter.release()# 釋放資源

# -*- coding: UTF-8 -*-
import moviepy.editor as mpy
# 讀取詞云視頻
my_clip = mpy.VideoFileClip('result.mp4')
# 截取背景音樂
audio_background = mpy.AudioFileClip('song.mp4').subclip(17, 44)
audio_background.write_audiofile('vmt.mp3')
# 視頻中插入音頻
final_clip = my_clip.set_audio(audio_background)
# 保存為最終的視頻動(dòng)聽的音樂！漂亮小姐姐詞云跳舞視頻！
final_clip.write_videofile('final_video.mp4')