手机游戏《明日方舟》里的阿米娅到底是兔子还是驴？

       import os import json import time import logging import urllib.request import urllib.error from urllib.parse import urlparse  from multiprocessing import Pool from user_agent import generate_user_agent from selenium import webdriver from selenium.webdriver.common.keys import Keys   def get_image_links(main_keyword, supplemented_keywords, link_file_path, num_requested = 1000):      number_of_scrolls = int(num_requested / 400) + 1      # number_of_scrolls * 400 images will be opened in the browser      img_urls = set()     driver = webdriver.Firefox()     for i in range(len(supplemented_keywords)):         search_query = main_keyword + ' ' + supplemented_keywords[i]         url = "https://www.google.com/search?q="+search_query+"&source=lnms&tbm=isch"         driver.get(url)                  for _ in range(number_of_scrolls):             for __ in range(10):                 # multiple scrolls needed to show all 400 images                 driver.execute_script("window.scrollBy(0, 1000000)")                 time.sleep(2)             # to load next 400 images             time.sleep(5)             try:                 driver.find_element_by_xpath("//input[@value='Show more results']").click()             except Exception as e:                 print("Process-{0} reach the end of page or get the maximum number of requested images".format(main_keyword))                 break          # imges = driver.find_elements_by_xpath('//div[@class="rg_meta"]') # not working anymore         imges = driver.find_elements_by_xpath('//div[contains(@class,"rg_meta")]')         for img in imges:             img_url = json.loads(img.get_attribute('innerHTML'))["ou"]             # img_type = json.loads(img.get_attribute('innerHTML'))["ity"]             img_urls.add(img_url)         print('Process-{0} add keyword {1} , got {2} image urls so far'.format(main_keyword, supplemented_keywords[i], len(img_urls)))     print('Process-{0} totally get {1} images'.format(main_keyword, len(img_urls)))     driver.quit()      with open(link_file_path, 'w') as wf:         for url in img_urls:             wf.write(url +'
')     print('Store all the links in file {0}'.format(link_file_path))   def download_images(link_file_path, download_dir, log_dir):      print('Start downloading with link file {0}..........'.format(link_file_path))     if not os.path.exists(log_dir):         os.makedirs(log_dir)     main_keyword = link_file_path.split('/')[-1]     log_file = log_dir + 'download_selenium_{0}.log'.format(main_keyword)     logging.basicConfig(level=logging.DEBUG, filename=log_file, filemode="a+", format="%(asctime)-15s %(levelname)-8s  %(message)s")     img_dir = download_dir + main_keyword + '/'     count = 0     headers = {}     if not os.path.exists(img_dir):         os.makedirs(img_dir)     # start to download images     with open(link_file_path, 'r') as rf:         for link in rf:             try:                 o = urlparse(link)                 ref = o.scheme + '://' + o.hostname                 #ref = 'https://www.google.com'                 ua = generate_user_agent()                 headers['User-Agent'] = ua                 headers['referer'] = ref                 print('
{0}
{1}
{2}'.format(link.strip(), ref, ua))                 req = urllib.request.Request(link.strip(), headers = headers)                 response = urllib.request.urlopen(req, timeout = 30)                 data = response.read()                 file_path = img_dir + '{0}.jpg'.format(count)                 with open(file_path,'wb') as wf:                     wf.write(data)                 print('Process-{0} download image {1}/{2}.jpg'.format(main_keyword, main_keyword, count))                 count += 1                 if count % 10 == 0:                     print('Process-{0} is sleeping'.format(main_keyword))                     time.sleep(5)              except urllib.error.URLError as e:                 print('URLError')                 logging.error('URLError while downloading image {0}reason:{1}'.format(link, e.reason))                 continue             except urllib.error.HTTPError as e:                 print('HTTPError')                 logging.error('HTTPError while downloading image {0}http code {1}, reason:{2}'.format(link, e.code, e.reason))                 continue             except Exception as e:                 print('Unexpected Error')                 logging.error('Unexpeted error while downloading image {0}error type:{1}, args:{2}'.format(link, type(e), e.args))                 continue   if __name__ == "__main__":      list_keyword = 'rabbit'     main_keywords = [list_keyword]     supplemented_keywords = ['']      download_dir = './data/'     link_files_dir = './link_files/'     log_dir = './logs/'      max_pic_num = 2000      # multiple processes     p = Pool(1) # default number of process is the number of cores of your CPU, change it by yourself     for keyword in main_keywords:         p.apply_async(get_image_links, args=(keyword, supplemented_keywords, link_files_dir + keyword, max_pic_num))     p.close()     p.join()     print('Fininsh getting all image links')               p = Pool() # default number of process is the number of cores of your CPU, change it by yourself     for keyword in main_keywords:         p.apply_async(download_images, args=(link_files_dir + keyword, download_dir, log_dir))     p.close()     p.join()     print('Finish downloading all images')     

       _________________________________________________________________ Layer (type)                 Output Shape              Param #    ================================================================= conv2d_1 (Conv2D)            (None, 126, 126, 32)      896        _________________________________________________________________ batch_normalization_1 (Batch (None, 126, 126, 32)      128        _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 63, 63, 32)        0          _________________________________________________________________ dropout_1 (Dropout)          (None, 63, 63, 32)        0          _________________________________________________________________ conv2d_2 (Conv2D)            (None, 61, 61, 64)        18496      _________________________________________________________________ batch_normalization_2 (Batch (None, 61, 61, 64)        256        _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 30, 30, 64)        0          _________________________________________________________________ dropout_2 (Dropout)          (None, 30, 30, 64)        0          _________________________________________________________________ conv2d_3 (Conv2D)            (None, 28, 28, 128)       73856      _________________________________________________________________ batch_normalization_3 (Batch (None, 28, 28, 128)       512        _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 14, 14, 128)       0          _________________________________________________________________ dropout_3 (Dropout)          (None, 14, 14, 128)       0          _________________________________________________________________ flatten_1 (Flatten)          (None, 25088)             0          _________________________________________________________________ dense_1 (Dense)              (None, 512)               12845568   _________________________________________________________________ batch_normalization_4 (Batch (None, 512)               2048       _________________________________________________________________ dropout_4 (Dropout)          (None, 512)               0          _________________________________________________________________ dense_2 (Dense)              (None, 1)                 513        ================================================================= Total params: 12,942,273 Trainable params: 12,940,801 Non-trainable params: 1,472 _________________________________________________________________