pip install google_images_download

# importing google_images_download module
from google_images_download import google_images_download

# creating object
response = google_images_download.googleimagesdownload()

search_queries =
[
'The smartphone also features an in display fingerprint sensor.',
'The pop up selfie camera is placed aligning with the rear cameras.',
'''In terms of storage Vivo V15 Pro could offer
up to 6GB of RAM and 128GB of onboard storage.''',
'The smartphone could be fuelled by a 3 700mAh battery.',
]


def downloadimages(query):
	# keywords is the search query
	# format is the image file format
	# limit is the number of images to be downloaded
	# print urs is to print the image file url
	# size is the image size which can
	# be specified manually ("large, medium, icon")
	# aspect ratio denotes the height width ratio
	# of images to download. ("tall, square, wide, panoramic")
	arguments = {"keywords": query,
				"format": "jpg",
				"limit":4,
				"print_urls":True,
				"size": "medium",
				"aspect_ratio":"panoramic"}
	try:
		response.download(arguments)
	
	# Handling File NotFound Error	
	except FileNotFoundError:
		arguments = {"keywords": query,
					"format": "jpg",
					"limit":4,
					"print_urls":True,
					"size": "medium"}
					
		# Providing arguments for the searched query
		try:
			# Downloading the photos based
			# on the given arguments
			response.download(arguments)
		except:
			pass

# Driver Code
for query in search_queries:
	downloadimages(query)
	print()

import re
import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse
import os
f = open("images_flowers.txt", "w")
res=[]
def download_google(url):
    #url = 'https://www.google.com/search?q=flowers&sxsrf=ALeKk00uvzQYZFJo03cukIcMS-pcmmbuRQ:1589501547816&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjEm4LZyrTpAhWjhHIEHewPD1MQ_AUoAXoECBAQAw&biw=1440&bih=740'
    page = requests.get(url).text
    soup = BeautifulSoup(page, 'html.parser')

    for raw_img in soup.find_all('img'):
        link = raw_img.get('src')
        res.append(link)
        if link:
            f.write(link +"\n")


download_google('https://www.google.com/search?q=flowers&sxsrf=ALeKk00uvzQYZFJo03cukIcMS-pcmmbuRQ:1589501547816&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjEm4LZyrTpAhWjhHIEHewPD1MQ_AUoAXoECBAQAw&biw=1440&bih=740')

f.close()

# USAGE
# python download_images.py --urls urls.txt --output images/santa

# import the necessary packages
from imutils import paths
import argparse
import requests
import cv2
import os

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-u", "--urls", required=True,
	help="path to file containing image URLs")
ap.add_argument("-o", "--output", required=True,
	help="path to output directory of images")
args = vars(ap.parse_args())

# grab the list of URLs from the input file, then initialize the
# total number of images downloaded thus far
rows = open(args["urls"]).read().strip().split("\n")
total = 0

# loop the URLs
for url in rows:
	try:
		# try to download the image
		r = requests.get(url, timeout=60)

		# save the image to disk
		p = os.path.sep.join([args["output"], "{}.jpg".format(
			str(total).zfill(8))])
		f = open(p, "wb")
		f.write(r.content)
		f.close()

		# update the counter
		print("[INFO] downloaded: {}".format(p))
		total += 1

	# handle if any exceptions are thrown during the download process
	except:
		print("[INFO] error downloading {}...skipping".format(p))

# loop over the image paths we just downloaded
for imagePath in paths.list_images(args["output"]):
	# initialize if the image should be deleted or not
	delete = False

	# try to load the image
	try:
		image = cv2.imread(imagePath)

		# if the image is `None` then we could not properly load it
		# from disk, so delete it
		if image is None:
			print("None")
			delete = True

	# if OpenCV cannot load the image then the image is likely
	# corrupt so we should delete it
	except:
		print("Except")
		delete = True

	# check to see if the image should be deleted
	if delete:
		print("[INFO] deleting {}".format(imagePath))
		os.remove(imagePath)

pip install pytube3

import cv2
from collections import defaultdict

import matplotlib.pyplot as plt 
import numpy as np
import pandas as pd
import warnings
from pytube import YouTube


warnings.filterwarnings('ignore')

video = YouTube('https://www.youtube.com/watch?v=GTkU4qj6v7g')
# print(video.streams.all())
print(video.streams.filter(file_extension = "mp4").all())
# [,
# ,
# ,
# ,
# ,
# ,
# ,
# ,
# ]

# 為要下載的視頻的分辨率使用適當的 itag。如果您需要高分辨率視頻下載，
# 請在以下步驟中選擇最高分辨率的 itag 進行下載
print(video.streams.get_by_itag(137).download())
# '/Users/sapnasharma/Documents/github/video_clips/Akshay Kumars Fitness Mantras for a Fit India  GOQii Play Exclusive.mp4'
video_path = video.title
print(video_path)
# "Akshay Kumar's Fitness Mantras for a Fit India | GOQii Play Exclusive"

# 視頻標題在名稱之間添加了一個管道，因此實際名稱已損壞。我稍后會修復這個錯誤，
# 現在我們可以直接粘貼視頻的名字來達到我們的目的。
video_path = "Akshay Kumars Fitness Mantras for a Fit India  GOQii Play Exclusive.mp4"
# Video Capture Using OpenCV
cap = cv2.VideoCapture(video_path)

frame_cnt = int(cap.get(cv2.cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)

print('Frames in video: ', frame_cnt)
print(f"Frames per sec: {fps}")
# Frames in video:  34249
# Frames per sec: 25.0
# （1）要獲取整個視頻的幀，請使用下面的代碼塊。
# Use this for accessing the entire video
index = 1

for x in range(frame_cnt):
    ret, frame = cap.read()    
    if not ret:
        break
        
    # Get frame timestamp
    frame_timestamp = cap.get(cv2.CAP_PROP_POS_MSEC)
    
    # fetch frame every sec
    if frame_timestamp >= (index * 1000.0): # change the value from 1000 to anyother value if not needed per second
        index = index + 2   # decides the freq. of frames to be saved
        print(f"++ {index}")
        cv2.imwrite(f"images/cv_{index}.png", frame)   
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break
        
cap.release()
cv2.destroyAllWindows()


# （2）要獲取特定持續時間之間的幀，請使用以下代碼塊。
# Use this in case frames are to be fetched within a certain time frame
# frame_timestamp will be calculated as fps*time*1000 and set the starting index accordingly
index = 1560

for x in range(frame_cnt):
    ret, frame = cap.read()
    
    if not ret:
        break
        
    # Get frame timestamp
    frame_timestamp = cap.get(cv2.CAP_PROP_POS_MSEC)
    if frame_timestamp >= 1560000.0 and frame_timestamp <= 1800000.0 :
        # fetch frame every sec
        if frame_timestamp >= (index * 1000.0):
            index = index + 4   # decides the freq. of frames to be saved
            print(f"++ {index}")
            
            
            cv2.imwrite(f"images/cv_{index}.png", frame)
    
    
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break
        
cap.release()
cv2.destroyAllWindows()