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.gitignore 0 → 100644
View file @ d1b63291
.idea
__pycache__
env
tasks/zhihu/captcha/__init__.py 0 → 100644
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from zhihu_captcha import zhihu_captcha
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tasks/zhihu/captcha/ocrmodel.py 0 → 100644
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import tensorflow as tf
from zhihu_captcha import utils
from tensorflow.python.training import moving_averages
FLAGS = utils.FLAGS
num_classes = utils.num_classes
class LSTMOCR(object):
def __init__(self, mode):
self.mode = mode
# 图像输入
self.inputs = tf.placeholder(tf.float32, [None, FLAGS.image_height, FLAGS.image_width, FLAGS.image_channel])
# ctc_loss 需要的是稀疏矩阵
self.labels = tf.sparse_placeholder(tf.int32)
# 一维数组，大小[batch_size]
self.seq_len = tf.placeholder(tf.int32, [None])
# l2
self._extra_train_ops = [] # 存储调整平滑均值，平滑方差的操作
def build_graph(self):
self._build_model()
self._build_train_op()
self.merged_summay = tf.summary.merge_all()
def _build_model(self):
"""
构建模型，前两个卷积的卷积核size 分别是7，5 是很重要的，换成其他的效果会差很多
"""
filters = [32, 64, 128, 128, FLAGS.max_stepsize]
strides = [1, 2]
with tf.variable_scope('cnn'):
with tf.variable_scope('unit-0'):
x = self._conv2d(self.inputs, 'cnn-0', 7, 1, filters[0], strides[0]) # 卷积
x = self._batch_norm('bn0', x) # 批标准化
x = self._leaky_relu(x, 0.01) # 非线性激活
x = self._max_pool(x, 2, strides[0]) # 池化
with tf.variable_scope('unit-1'):
x = self._conv2d(x, 'cnn-1', 5, filters[0], filters[1], strides[0])
x = self._batch_norm('bn1', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('unit-2'):
x = self._conv2d(x, 'cnn-2', 3, filters[1], filters[2], strides[0])
x = self._batch_norm('bn2', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('unit-3'):
x = self._conv2d(x, 'cnn-3', 3, filters[2], filters[3], strides[0])
x = self._batch_norm('bn3', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('unit-4'):
x = self._conv2d(x, 'cnn-4', 3, filters[3], filters[4], strides[0])
x = self._batch_norm('bn4', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('lstm'):
shp = x.get_shape().as_list()
x = tf.reshape(x, [-1, filters[4], shp[1]*shp[2]])
# tf.nn.rnn_cell.RNNCell, tf.nn.rnn_cell.GRUCell
cell = tf.contrib.rnn.LSTMCell(FLAGS.num_hidden, state_is_tuple=True)
if self.mode == 'train':
cell = tf.contrib.rnn.DropoutWrapper(cell=cell, output_keep_prob=0.8)
cell1 = tf.contrib.rnn.LSTMCell(FLAGS.num_hidden, state_is_tuple=True)
if self.mode == 'train':
cell1 = tf.contrib.rnn.DropoutWrapper(cell=cell1, output_keep_prob=0.8)
# 将rnn堆成2层深度
stack = tf.contrib.rnn.MultiRNNCell([cell, cell1], state_is_tuple=True)
# outputs是所有step的结果， state 是最后一个step的结果这里不需要
outputs, _ = tf.nn.dynamic_rnn(stack, x, self.seq_len, dtype=tf.float32)
# reshape 使其满足模型的step长度
outputs = tf.reshape(outputs, [-1, FLAGS.num_hidden])
W = tf.get_variable(name='W',
shape=[FLAGS.num_hidden, num_classes],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable(name='b',
shape=[num_classes],
dtype=tf.float32,
initializer=tf.constant_initializer())
self.logits = tf.matmul(outputs, W) + b
# reshape 使得最后一个维度是 num_classes
shape = tf.shape(x)
self.logits = tf.reshape(self.logits, [shape[0], -1, num_classes])
# Time major
self.logits = tf.transpose(self.logits, (1, 0, 2))
def _build_train_op(self):
self.global_step = tf.Variable(0, trainable=False)
# ctc 损失函数，使用前后向算法和最大似然
self.loss = tf.nn.ctc_loss(labels=self.labels,
inputs=self.logits,
sequence_length=self.seq_len)
self.cost = tf.reduce_mean(self.loss)
tf.summary.scalar('cost', self.cost)
self.lrn_rate = tf.train.exponential_decay(FLAGS.initial_learning_rate,
self.global_step,
FLAGS.decay_steps,
FLAGS.decay_rate,
staircase=True)
# self.optimizer = tf.train.RMSPropOptimizer(learning_rate=self.lrn_rate,
# momentum=FLAGS.momentum).minimize(self.cost,
# global_step=self.global_step)
# self.optimizer = tf.train.MomentumOptimizer(learning_rate=self.lrn_rate,
# momentum=FLAGS.momentum,
# use_nesterov=True).minimize(self.cost,
# global_step=self.global_step)
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.initial_learning_rate,
beta1=FLAGS.beta1,
beta2=FLAGS.beta2).minimize(self.loss,
global_step=self.global_step)
train_ops = [self.optimizer] + self._extra_train_ops
self.train_op = tf.group(*train_ops)
# Option 2: tf.contrib.ctc.ctc_beam_search_decoder
# (it's slower but you'll get better results)
# decoded, log_prob = tf.nn.ctc_greedy_decoder(logits, seq_len,merge_repeated=False)
self.decoded, self.log_prob = tf.nn.ctc_beam_search_decoder(self.logits,
self.seq_len,
merge_repeated=False) # 寻找最优路径
self.dense_decoded = tf.sparse_tensor_to_dense(self.decoded[0], default_value=-1) # 解码
# 卷积
def _conv2d(self, x, name, filter_size, in_channels, out_channels, strides):
with tf.variable_scope(name):
kernel = tf.get_variable(name='DW',
shape=[filter_size, filter_size, in_channels, out_channels],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable(name='bais',
shape=[out_channels],
dtype=tf.float32,
initializer=tf.constant_initializer())
con2d_op = tf.nn.conv2d(x, kernel, [1, strides, strides, 1], padding='SAME')
return tf.nn.bias_add(con2d_op, b) #加上偏置，然后返回
# 批标准化
def _batch_norm(self, name, x):
"""批标准化."""
with tf.variable_scope(name):
params_shape = [x.get_shape()[-1]] #获取tensor的最后一个维度，后面的均值，方差都是这个维度
# 标准化数据为均值为0方差为1之后，还有一个x=x*gamma+beta的调整
# 这个会随着训练不断调整
beta = tf.get_variable(
'beta', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32))
gamma = tf.get_variable(
'gamma', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32))
# 训练的时候不断调整平滑均值，平滑方差
# 预测的时候，回复权重使用的是训练过程中调整出来的平滑方差均值去做标准化
if self.mode == 'train':
mean, variance = tf.nn.moments(x, [0, 1, 2], name='moments') #获取批均值和方差，size[最后一个维度]
# moving_mean, moving_variance 这两个name一定要让训练和预测的时候都相等，不然就没法恢复训练好的值了
moving_mean = tf.get_variable(
'moving_mean', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32),
trainable=False)
moving_variance = tf.get_variable(
'moving_variance', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32),
trainable=False)
self._extra_train_ops.append(moving_averages.assign_moving_average(
moving_mean, mean, 0.9))
self._extra_train_ops.append(moving_averages.assign_moving_average(
moving_variance, variance, 0.9))
else:
# mean的name一定要跟train的时候的一样 moving_mean
mean = tf.get_variable(
'moving_mean', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32),
trainable=False)
# variance的name一定要跟train的时候的一样 moving_variance
variance = tf.get_variable(
'moving_variance', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32),
trainable=False)
# 可视化
tf.summary.histogram(mean.op.name, mean)
tf.summary.histogram(variance.op.name, variance)
# 计算，标准化，最后一个值为误差，一般设置很小即可
x_bn = tf.nn.batch_normalization(x, mean, variance, beta, gamma, 0.001)
x_bn.set_shape(x.get_shape())
return x_bn
# 变种Relu
# Relu 简单而强大，方便求导
# 非负区间的梯度为常数，一定程度上能够防止梯度消失问题
def _leaky_relu(self, x, leakiness=0.0):
return tf.where(tf.less(x, 0.0), leakiness * x, x, name='leaky_relu')
def _max_pool(self, x, ksize, strides):
return tf.nn.max_pool(x,
ksize=[1, ksize, ksize, 1],
strides=[1, strides, strides, 1],
padding='SAME',
name='max_pool')
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tasks/zhihu/captcha/utils.py 0 → 100644
View file @ d1b63291
import os
import numpy as np
import tensorflow as tf
#import cv2
from PIL import Image
# +-* + () + 10 digit + blank + space
num_classes = 38#3 + 2 + 10 + 1 + 1
maxPrintLen = 100
tf.app.flags.DEFINE_boolean('restore', True, 'whether to restore from the latest checkpoint')
tf.app.flags.DEFINE_string('checkpoint_dir', './checkpoint/', 'the checkpoint dir')
tf.app.flags.DEFINE_float('initial_learning_rate', 1e-3, 'inital lr')
tf.app.flags.DEFINE_integer('image_height', 60, 'image height')
tf.app.flags.DEFINE_integer('image_width', 150, 'image width')
tf.app.flags.DEFINE_integer('image_channel', 1, 'image channels as input')
tf.app.flags.DEFINE_integer('max_stepsize', 64, 'max stepsize in lstm, as well as '
'the output channels of last layer in CNN')
tf.app.flags.DEFINE_integer('num_hidden', 128, 'number of hidden units in lstm')
tf.app.flags.DEFINE_integer('num_epochs', 1000, 'maximum epochs')
tf.app.flags.DEFINE_integer('batch_size', 128, 'the batch_size')
tf.app.flags.DEFINE_integer('save_steps', 500, 'the step to save checkpoint')
tf.app.flags.DEFINE_integer('validation_steps', 500, 'the step to validation')
tf.app.flags.DEFINE_float('decay_rate', 0.98, 'the lr decay rate')
tf.app.flags.DEFINE_float('beta1', 0.9, 'parameter of adam optimizer beta1')
tf.app.flags.DEFINE_float('beta2', 0.999, 'adam parameter beta2')
tf.app.flags.DEFINE_integer('decay_steps', 1000, 'the lr decay_step for optimizer')
tf.app.flags.DEFINE_float('momentum', 0.9, 'the momentum')
tf.app.flags.DEFINE_string('train_dir', './imgs/train/', 'the train data dir')
tf.app.flags.DEFINE_string('val_dir', './imgs/val/', 'the val data dir')
tf.app.flags.DEFINE_string('infer_dir', './imgs/infer/', 'the infer data dir')
tf.app.flags.DEFINE_string('logs_dir', './log', 'the logging dir')
tf.app.flags.DEFINE_string('mode', 'train', 'train, val or infer')
tf.app.flags.DEFINE_integer('num_gpus', 1, 'num of gpus')
FLAGS = tf.app.flags.FLAGS
# num_batches_per_epoch = int(num_train_samples/FLAGS.batch_size)
import string
charset = string.digits + string.ascii_lowercase#'0123456789+-*()'
encode_maps = {}
decode_maps = {}
for i, char in enumerate(charset, 1):
encode_maps[char] = i
decode_maps[i] = char
SPACE_INDEX = 0
SPACE_TOKEN = ''
encode_maps[SPACE_TOKEN] = SPACE_INDEX
decode_maps[SPACE_INDEX] = SPACE_TOKEN
class DataIterator:
def __init__(self, data_dir):
self.image = [] # 所有的图片都加载进内存待等待提取，加载进内存当然是为了速度了
self.labels = []
for root, sub_folder, file_list in os.walk(data_dir):
for file_path in file_list:
image_name = os.path.join(root, file_path)
# im = np.array(Image.open(image_name)).astype(np.float32)/255.
im = np.array(Image.open(image_name).convert("L")).astype(np.float32)/255.
# im = np.array(Image.open(image_name).convert("L").point(lambda x: 0 if x < 150 else 1)).astype(np.float32)
# im = cv2.imread(image_name, 0).astype(np.float32)/255.
# resize to same height, different width will consume time on padding
# im = cv2.resize(im, (image_width, image_height))
im = np.reshape(im, [FLAGS.image_height, FLAGS.image_width, FLAGS.image_channel])
self.image.append(im)
# image is named as /.../<folder>/00000_abcd.png
code = image_name.split(os.sep)[-1].split('_')[1].split('.')[0] # code 是验证码
code = [SPACE_INDEX if code == SPACE_TOKEN else encode_maps[c] for c in list(code)] # code转成[1,2,3,4] 字码列表
self.labels.append(code)
# 使size方法变成属性，调用的时候self.size即可，不用调用self.size() #这里体现不出@property的优点
@property
def size(self):
return len(self.labels)
# 给定index, 抽取labels
def the_label(self, indexs):
labels = []
for i in indexs:
labels.append(self.labels[i])
return labels
# 给定index, 得到一个批次的训练数据
def input_index_generate_batch(self, index=None):
if index:
image_batch = [self.image[i] for i in index]
label_batch = [self.labels[i] for i in index]
else:
image_batch = self.image
label_batch = self.labels
def get_input_lens(sequences):
# 分片的序列长度，因为验证码图片序列长度都是一样的，不像句子有长有短
# 所以这里的长度都是一样的
lengths = np.asarray([FLAGS.max_stepsize for _ in sequences], dtype=np.int64)
return sequences, lengths
batch_inputs, batch_seq_len = get_input_lens(np.array(image_batch))
batch_labels = sparse_tuple_from_label(label_batch) # 转成稀疏矩阵
return batch_inputs, batch_seq_len, batch_labels
# 对比解码得到的label和真实label，计算正确率
def accuracy_calculation(original_seq, decoded_seq, ignore_value=-1, isPrint=False):
if len(original_seq) != len(decoded_seq):
print('original lengths is different from the decoded_seq, please check again')
return 0
count = 0
for i, origin_label in enumerate(original_seq):
decoded_label = [j for j in decoded_seq[i] if j != ignore_value]
if isPrint and i < maxPrintLen:
# print('seq{0:4d}: origin: {1} decoded:{2}'.format(i, origin_label, decoded_label))
with open('./test.csv', 'w') as f:
f.write(str(origin_label) + '\t' + str(decoded_label))
f.write('\n')
if origin_label == decoded_label:
count += 1
return count * 1.0 / len(original_seq)
def sparse_tuple_from_label(sequences, dtype=np.int32):
"""
根据[[1,2,3,4],[5,2,6,5],...] 这种形式，生成稀疏矩阵
稀疏矩阵由三个元素的tuple组成，即(indices, values, shape)
indices和values的都是一个列表，列表元素刚好一一对应，
一个代表坐标位置，一个代表这个位置的值，其中indices是一个
[(0,1),(0,2),...(10,3),(10,4),...]这样的形式的列表，指示了
对应的values的值在密集矩阵的坐标，values 是[1,2,3,...,100,...]
这样的形式，最后一个shape描述密集矩阵的shape
示例：
indices = [(0,1),(0,2),(0,3),(1,1),(1,3),(2,2)]
values = [1,2,3,4,5,6]
shape = [4,3]
则对应的密集矩阵就是
0 1 2 3
0 4 0 5
0 0 6 0
参数:
sequences: 一个列表，列表里面是每个验证码的码字列表
返回:
(indices, values, shape)
"""
indices = []
values = []
for n, seq in enumerate(sequences):
indices.extend(zip([n] * len(seq), range(len(seq))))
values.extend(seq)
indices = np.asarray(indices, dtype=np.int64)
values = np.asarray(values, dtype=dtype)
shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
return indices, values, shape
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tasks/zhihu/captcha/zhihu_captcha.py 0 → 100644
View file @ d1b63291
import requests
import time
import json
import os
import sys
from bs4 import BeautifulSoup as BS
import urllib.parse
import webbrowser
from io import BytesIO
from zhihu_captcha import utils
from zhihu_captcha import orcmodel
import tensorflow as tf
from PIL import Image
import numpy as np
try:
type (eval('model'))
except:
model = orcmodel.LSTMOCR('infer')
model.build_graph()
config = tf.ConfigProto(allow_soft_placement=True)
checkpoint_dir=os.path.join(os.path.dirname(os.path.abspath(__file__)), "checkpoint")
class ZhihuCaptcha():
def __init__(self, username=None, password=None):
if sys.path[0]: os.chdir(sys.path[0]) # 设置脚本所在目录为当前工作目录
# 恢复权重
self.__sess = self.__restoreSess(checkpoint_dir)
# 恢复权重
def __restoreSess(self, checkpoint=checkpoint_dir):
sess=tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
ckpt = tf.train.latest_checkpoint(checkpoint)
if ckpt:
#回复权限，这里连 global_step 也会被加载进来
saver.restore(sess, ckpt)
# print('restore from the checkpoint{0}'.format(ckpt))
print('已加载checkpoint{0}'.format(ckpt))
else:
print('警告：未加载任何chechpoint')
print('如果这不是你预期中的，请确保以下目录存在可用的checkpoint:\n{0}'.format(checkpoint_dir))
return sess
def recgImg(self, img):
"""
可以在线测试验证码识别功能
参数：
img 一个 (60, 150) 的图片
"""
im = np.array(img.convert("L")).astype(np.float32)/255.
im = np.reshape(im, [60, 150, 1])
inp=np.array([im])
seq_len_input=np.array([np.array([64 for _ in inp], dtype=np.int64)])
#seq_len_input = np.asarray(seq_len_input)
seq_len_input = np.reshape(seq_len_input, [-1])
imgs_input = np.asarray([im])
feed = {model.inputs: imgs_input,model.seq_len: seq_len_input}
dense_decoded_code = self.__sess.run(model.dense_decoded, feed)
expression = ''
for i in dense_decoded_code[0]:
if i == -1:
expression += ''
else:
expression += utils.decode_maps[i]
return expression
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tasks/zhihu/old_spider.py 0 → 100644
View file @ d1b63291
import pymysql
import hashlib
import requests
import execjs
import os
import re
import sys
import time
from datetime import datetime
import kdl
HOST = '172.18.51.14'
PORT = 3306
USER = 'spider'
PASSWD = 'Gengmei123'
DB = 'spider'
JS_FILE_PATH = '/srv/apps/crawler/crawler_sys/site_crawler/zhihu.js'
# JS_FILE_PATH = '/Users/haowei/workspace/gm/crawler/crawler_sys/site_crawler/zhihu.js'
APIKEY = 'quxguz4hwm9cxnx6wpjhkokx04klpr8v'
def login():
url = 'https://www.zhihu.com'
loginUrl = 'https://www.zhihu.com/login/email'
headers = {
# "User-Agent": 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:41.0) Gecko/20100101 Firefox/41.0',
'User-Agent' : 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/55.0.2883.87 Chrome/55.0.2883.87 Safari/537.36',
"Referer": "http://www.zhihu.com/",
'Host': 'www.zhihu.com',
'rememberme': "true"
}
data = {
'email': 'yousangdandan@yeah.net',
'password': '5358569'
}
global session
session = requests.session()
login_req = session.post(loginUrl, data=data,headers=headers)
print('loginReq:{}'.format(loginReq.status_code))
return login_req
def get_proxy():
auth = kdl.Auth("990866563045611", APIKEY)
client = kdl.Client(auth)
ips = client.get_dps(1, sign_type='hmacsha1', format='json', area='北京,上海,广东')
print("dps proxy: ", ips, client.get_proxy_authorization())
# return { "http": "http://{}".format(ips[0]), "https": "https://{}".format(ips[0]), }, client.get_proxy_authorization()
return { "http": "http://{}".format('171.35.213.172:9999'), "https": "https://{}".format('171.35.213.172:9999'), }, client.get_proxy_authorization()
def retry_get_url(url, retrys=5, headers={}, timeout=10, **kwargs):
retry_c = 0
while retry_c < retrys:
time.sleep(3)
try:
# proxies, proxy_authorization = get_proxy()
# headers.update(proxy_authorization)
# get_resp = requests.get(url, headers=headers, timeout=timeout, proxies=proxies, **kwargs)
get_resp = requests.get(url, headers=headers, timeout=timeout, **kwargs)
return get_resp
except Exception as e:
retry_c += 1
print(e)
# proxies, proxy_authorization = get_proxy()
print('Failed to get page %s after %d retries, %s'
% (url, retrys, datetime.now()))
return None
class Spider(object):
def __init__(self, spider_url):
'''
初始化数据库，调整js规则
'''
self.conn = pymysql.connect(host=HOST, port=PORT, user=USER,
passwd=PASSWD,
db=DB, charset='utf8')
self.cur = self.conn.cursor()
self.page_count = 1000
self.use_proxy = True
self.spider_url = spider_url
detail_url = '/answers?include=data%5B*%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Cmark_infos%2Ccreated_time%2Cupdated_time%2Creview_info%2Cexcerpt%2Cis_labeled%2Clabel_info%2Crelationship.is_authorized%2Cvoting%2Cis_author%2Cis_thanked%2Cis_nothelp%2Cis_recognized%3Bdata%5B*%5D.author.badge%5B%3F(type%3Dbest_answerer)%5D.topics%3Bdata%5B*%5D.question.has_publishing_draft%2Crelationship&offset={}&limit=20&sort_by=created'
self.ANSWER_URL = self.spider_url.replace("https://www.zhihu.com/people", "https://www.zhihu.com/api/v4/members") + detail_url
os.environ["EXECJS_RUNTIME"] = 'Node'
try:
with open('./zhihu.js', 'r', encoding='utf-8') as f:
js = f.read()
except:
with open(JS_FILE_PATH, 'r', encoding='utf-8') as f:
js = f.read()
# self.exec_js = execjs.compile(js)
self.exec_js = execjs.compile(js, cwd='/home/gmuser/node_modules')
def headers_handle(self, url):
'''
url请求中的头部伪装
'''
'''
{
'Server': 'CLOUD ELB 1.0.0',
'Date': 'Fri, 01 Jan 2021 08:36:59 GMT',
'Content-Type': 'text/html; charset=utf-8',
'Vary': 'Accept-Encoding',
'content-security-policy': "default-src * blob:; img-src * data: blob: resource: t.captcha.qq.com cstaticdun.126.net necaptcha.nosdn.127.net; connect-src * wss: blob: resource:; frame-src 'self' *.zhihu.com mailto: tel: weixin: *.vzuu.com mo.m.taobao.com getpocket.com note.youdao.com safari-extension://com.evernote.safari.clipper-Q79WDW8YH9 zhihujs: captcha.guard.qcloud.com pos.baidu.com dup.baidustatic.com openapi.baidu.com wappass.baidu.com passport.baidu.com *.cme.qcloud.com vs-cdn.tencent-cloud.com t.captcha.qq.com c.dun.163.com; script-src 'self' blob: *.zhihu.com g.alicdn.com qzonestyle.gtimg.cn res.wx.qq.com open.mobile.qq.com 'unsafe-eval' unpkg.zhimg.com unicom.zhimg.com resource: captcha.gtimg.com captcha.guard.qcloud.com pagead2.googlesyndication.com cpro.baidustatic.com pos.baidu.com dup.baidustatic.com i.hao61.net 'nonce-8555a150-24a4-490b-9a1e-d48bdb590dfe' hm.baidu.com zz.bdstatic.com b.bdstatic.com imgcache.qq.com vs-cdn.tencent-cloud.com gw.alipayobjects.com ssl.captcha.qq.com t.captcha.qq.com cstaticdun.126.net c.dun.163.com ac.dun.163.com/ acstatic-dun.126.net; style-src 'self' 'unsafe-inline' *.zhihu.com unicom.zhimg.com resource: captcha.gtimg.com ssl.captcha.qq.com t.captcha.qq.com cstaticdun.126.net c.dun.163.com ac.dun.163.com/ acstatic-dun.126.net", 'x-content-security-policy': "default-src * blob:; img-src * data: blob: resource: t.captcha.qq.com cstaticdun.126.net necaptcha.nosdn.127.net; connect-src * wss: blob: resource:; frame-src 'self' *.zhihu.com mailto: tel: weixin: *.vzuu.com mo.m.taobao.com getpocket.com note.youdao.com safari-extension://com.evernote.safari.clipper-Q79WDW8YH9 zhihujs: captcha.guard.qcloud.com pos.baidu.com dup.baidustatic.com openapi.baidu.com wappass.baidu.com passport.baidu.com *.cme.qcloud.com vs-cdn.tencent-cloud.com t.captcha.qq.com c.dun.163.com; script-src 'self' blob: *.zhihu.com g.alicdn.com qzonestyle.gtimg.cn res.wx.qq.com open.mobile.qq.com 'unsafe-eval' unpkg.zhimg.com unicom.zhimg.com resource: captcha.gtimg.com captcha.guard.qcloud.com pagead2.googlesyndication.com cpro.baidustatic.com pos.baidu.com dup.baidustatic.com i.hao61.net 'nonce-8555a150-24a4-490b-9a1e-d48bdb590dfe' hm.baidu.com zz.bdstatic.com b.bdstatic.com imgcache.qq.com vs-cdn.tencent-cloud.com gw.alipayobjects.com ssl.captcha.qq.com t.captcha.qq.com cstaticdun.126.net c.dun.163.com ac.dun.163.com/ acstatic-dun.126.net; style-src 'self' 'unsafe-inline' *.zhihu.com unicom.zhimg.com resource: captcha.gtimg.com ssl.captcha.qq.com t.captcha.qq.com cstaticdun.126.net c.dun.163.com ac.dun.163.com/ acstatic-dun.126.net", 'x-webkit-csp': "default-src * blob:; img-src * data: blob: resource: t.captcha.qq.com cstaticdun.126.net necaptcha.nosdn.127.net; connect-src * wss: blob: resource:; frame-src 'self' *.zhihu.com mailto: tel: weixin: *.vzuu.com mo.m.taobao.com getpocket.com note.youdao.com safari-extension://com.evernote.safari.clipper-Q79WDW8YH9 zhihujs: captcha.guard.qcloud.com pos.baidu.com dup.baidustatic.com openapi.baidu.com wappass.baidu.com passport.baidu.com *.cme.qcloud.com vs-cdn.tencent-cloud.com t.captcha.qq.com c.dun.163.com; script-src 'self' blob: *.zhihu.com g.alicdn.com qzonestyle.gtimg.cn res.wx.qq.com open.mobile.qq.com 'unsafe-eval' unpkg.zhimg.com unicom.zhimg.com resource: captcha.gtimg.com captcha.guard.qcloud.com pagead2.googlesyndication.com cpro.baidustatic.com pos.baidu.com dup.baidustatic.com i.hao61.net 'nonce-8555a150-24a4-490b-9a1e-d48bdb590dfe' hm.baidu.com zz.bdstatic.com b.bdstatic.com imgcache.qq.com vs-cdn.tencent-cloud.com gw.alipayobjects.com ssl.captcha.qq.com t.captcha.qq.com cstaticdun.126.net c.dun.163.com ac.dun.163.com/ acstatic-dun.126.net; style-src 'self' 'unsafe-inline' *.zhihu.com unicom.zhimg.com resource: captcha.gtimg.com ssl.captcha.qq.com t.captcha.qq.com cstaticdun.126.net c.dun.163.com ac.dun.163.com/ acstatic-dun.126.net",
'x-frame-options': 'SAMEORIGIN',
'strict-transport-security': 'max-age=15552000; includeSubDomains',
'surrogate-control': 'no-store',
'cache-control': 'no-cache, no-store, must-revalidate, private, max-age=0',
'pragma': 'no-cache',
'expires': '0',
'x-content-type-options': 'nosniff',
'x-xss-protection': '1; mode=block',
'X-Backend-Response': '0.022',
'Referrer-Policy': 'no-referrer-when-downgrade',
'X-SecNG-Response': '0.023000001907349',
'x-lb-timing': '0.023',
'x-idc-id': '2',
'Set-Cookie': 'KLBRSID=fb3eda1aa35a9ed9f88f346a7a3ebe83|1609490219|1609490131; Path=/',
'X-Cache-Lookup': 'Cache Miss, Cache Miss',
'Content-Encoding': 'gzip',
'Transfer-Encoding': 'chunked',
'X-NWS-LOG-UUID': '16179100128830453442',
'Connection': 'keep-alive',
'x-edge-timing': '0.061',
'x-cdn-provider': 'tencent'}
'''
'''
html
cache-control: no-cache, no-store, must-revalidate, private, max-age=0
content-encoding: gzip
content-type: application/json
date: Fri, 01 Jan 2021 07:01:52 GMT
etag: W/"6a7ddf80b3ab19ba789d570163ac1eacb4bde53e"
expires: Fri, 02 Jan 2000 00:00:00 GMT
pragma: no-cache
referrer-policy: no-referrer-when-downgrade
server: CLOUD ELB 1.0.0
set-cookie: KLBRSID=5430ad6ccb1a51f38ac194049bce5dfe|1609484511|1609484497; Path=/
vary: Accept-Encoding
x-backend-response: 0.573
x-cache-lookup: Cache Miss
x-cdn-provider: tencent
x-edge-timing: 0.634
x-idc-id: 2
x-lb-timing: 0.599
x-nws-log-uuid: 12448536375904178345
x-secng-response: 0.59800004959106
x-udid: AADj9V7Fuw6PTkTnxjUXHGA8UqXz_bkbAN4=
'''
res_cookies_dict = self.get_serach_page_cookies()
referer = self.spider_url.replace("https://www.zhihu.com/people", "https://www.zhihu.com/api/v4/members")
headers_search = {
"accept": "*/*",
"accept-encoding": "gzip, deflate",
"accept-language": "zh-CN,zh;q=0.9",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36",
"x-ab-param": "li_yxzl_new_style_a=1;se_major=0;se_adsrank=4;se_hi_trunc=0;se_v053=1;li_panswer_topic=0;zr_test_aa1=1;pf_noti_entry_num=2;zr_search_sim2=2;zr_slotpaidexp=2;se_zp_boost=0;tp_club_entrance=1;pf_profile2_tab=0;ug_newtag=1;li_answer_card=0;ls_recommend_test=5;qap_labeltype=1;zr_rec_answer_cp=open;se_sug_term=0;tp_topic_tab=0;ge_ge01=5;se_wil_act=0;se_videobox=0;tsp_ad_cardredesign=0;qap_question_visitor= 0;zr_slot_training=2;tp_clubhyb=0;li_ebook_gen_search=2;se_v_v005=0;zw_sameq_sorce=999;ge_ge02=6;se_mobilecard=0;se_auth_src=0;tp_header_style=1;tp_flow_ctr=0;pf_creator_card=1;li_viptab_name=0;zr_intervene=0;se_bert128=1;se_ffzx_jushen1=0;top_v_album=1;se_preset=0;tp_discover=1;ls_fmp4=0;tp_club_top=0;top_universalebook=1;li_svip_cardshow=1;li_paid_answer_exp=0;tp_topic_style=0;zr_art_rec=base;se_colorfultab=1;se_auth_src2=0;tp_club_qa_entrance=1;tp_club__entrance2=1;tsp_hotlist_ui=3;li_svip_tab_search=1;se_entity22=1;tp_meta_card=0;tp_topic_tab_new=0-0-0;tp_zrec=0;top_ebook=0;pf_adjust=1;qap_question_author=0;zr_topic_rpc=0;se_topicfeed=0;tp_club_feed=0;tsp_ioscard2=0;zr_rel_search=base;se_recommend=1;se_usercard=0;tp_club_fdv4=0;tp_m_intro_re_topic=1;pf_foltopic_usernum=0;li_vip_verti_search=0;zr_training_boost=false;se_v054=0;tp_contents=1;soc_feed_intelligent=3;tsp_ios_cardredesign=0;pf_fuceng=1;pf_newguide_vertical=0;ug_follow_topic_1=2;ls_video_commercial=0;li_car_meta=1;se_sug_dnn=0;tp_fenqu_wei=0;li_catalog_card=1;top_quality=0;se_click_v_v=1;se_aa_base=1;se_club_ui=0;se_return_1=0;soc_notification=1;zr_ans_rec=gbrank;zr_search_paid=1;zr_expslotpaid=3;zr_rerank=0;se_college=default;se_whitelist=1;top_root=0;li_yxxq_aut=A1;tsp_adcard2=0;ls_videoad=2;se_col_boost=1;li_edu_page=old;zr_training_first=false;se_t2sug=1;se_vbert3=0;se_merge=0;li_video_section=1;zr_km_answer=open_cvr;zr_sim3=0;se_v_v006=0;tp_dingyue_video=0;li_topics_search=0;se_searchwiki=0;se_guess=0;se_major_v2=0;tp_club_bt=0;tp_sft=a;top_test_4_liguangyi=1",
"x-api-version": "3.0.91",
"x-app-za": "OS=Web",
"x-requested-with": "fetch",
"x-zse-83": "3_2.0",
"x-zse-86": None,
"referer": referer + "/answers?page=1",
}
# cookies_dict = {
# "d_c0": '"AADj9V7Fuw6PTkTnxjUXHGA8UqXz_bkbAN4=|1545899265"',
# "KLBRSID": '5430ad6ccb1a51f38ac194049bce5dfe|1609484506|1609484497',
# }
# cookies_dict.update(res_cookies_dict)
f = "+".join(
["3_2.0", url.replace("https://www.zhihu.com", ""), headers_search["referer"], cookies_dict["d_c0"]])
fmd5 = hashlib.new('md5', f.encode()).hexdigest()
headers_search["x-zse-86"] = "1.0_" + self.exec_js.call("b", fmd5)
return headers_search, cookies_dict
def get_serach_page_cookies(self):
'''
cookies更新
'''
headers = {
"accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9",
"cache-control": "max-age=0",
"cookie": '_xsrf=vTWamiEoaOQszAl6fjdlxOtqyhDvOen9; d_c0="AADj9V7Fuw6PTkTnxjUXHGA8UqXz_bkbAN4=|1545899265"; q_c1=3e9e37a46b1d4bfd87f7d1fcb084daad|1545899267000|1545899267000; _ga=GA1.2.929033900.1582626815; capsion_ticket="2|1:0|10:1608602928|14:capsion_ticket|44:MmRhNDdmYWJhZjU3NGQ4ODg3NDAzNGIwNDNiMTdlNDE=|7924fa0d0e36d3ed2a4af65dafa4684c9b36a70d586ec3adb1963c8df5f55e81"; _zap=6fd2d768-daa1-4be1-9a96-43d86c1bbc75; Hm_lvt_98beee57fd2ef70ccdd5ca52b9740c49=1609325059,1609337218,1609401296,1609405637; Hm_lpvt_98beee57fd2ef70ccdd5ca52b9740c49=1609484506; KLBRSID=5430ad6ccb1a51f38ac194049bce5dfe|1609484506|1609484497',
"referer": self.spider_url,
"sec-fetch-dest": "document",
"sec-fetch-mode": "navigate",
"sec-fetch-site": "same-origin",
"sec-fetch-user": "?1",
"upgrade-insecure-requests": "1",
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36",
}
requests_res = retry_get_url(self.spider_url, headers=headers)
return requests_res.cookies.get_dict()
def update_page_count(self, answer_count):
count = int(answer_count / 20)
temp = int(answer_count % 20)
if temp > 0:
count += 1
self.page_count = count
def check_data_exist(self, data_dict, mark):
'''
数据插入前检测
'''
sql = "select id from {table} where answer_id = {id_}"
exist = None
if mark == 0:
select_sql = sql.format(table='zhihu_answer', id_=data_dict["id"])
self.cur.execute(select_sql)
exist = self.cur.fetchone()
if mark == 1:
select_sql = sql.format(table='zhihu_article', id_=data_dict["id"])
self.cur.execute(select_sql)
exist = self.cur.fetchone()
if exist:
return True
return False
def parse_sigle_page(self, data_dict, mark):
'''
插入主要内容数据和图片的url
'''
if not self.check_data_exist(data_dict, mark):
if mark == 0:
into = "insert into zhihu_answer(title, content, answer_id, created_time, comment_count, new_content) value(%s, %s, %s, %s, %s, %s)"
values = (data_dict["question"]["title"], data_dict["content"], data_dict["id"], data_dict["created_time"],
data_dict["comment_count"], data_dict["content"])
elif mark == 1:
into = "insert into zhihu_article(title, content, article_id, created_time, comment_count, new_content) value(%s, %s, %s, %s, %s, %s)"
values = (
data_dict["title"], data_dict["content"], data_dict["id"], data_dict["created"], data_dict["comment_count"],
data_dict["content"])
print(data_dict["question"]["title"])
self.cur.execute(into, values)
self.conn.commit()
return
def search_page(self, mark, page_max, start_page=0, need_commend=False):
'''
函数主入口
params:
mark 0 answer, 1 article, 2 thought
'''
offset = start_page
for i in range(page_max):
if i > self.page_count - 1:
break
if mark == 0:
self.search_answer_article_page(offset, 0, 0)
elif mark == 1:
self.search_answer_article_page(offset, 1, 0)
elif mark == 2:
self.search_thought_page(offset)
offset = offset + 20
time.sleep(10)
self.conn.close()
return
def get_page_data(self, url, headers_search, cookies_dict):
get_page = retry_get_url(url, headers=headers_search, cookies=cookies_dict)
if get_page.status_code != 200:
# retry once
get_page = retry_get_url(url)
if get_page.status_code != 200:
print("article_error, url : ", url, " status_code: ", get_page.status_code)
try:
page_dict = get_page.json()
print('get page json data success! {}', offset)
except:
print('retry get page data : {}', offset)
self.get_page_data(url)
def search_answer_article_page(self, offset, mark, proxies_num=0):
'''
实现文章和回答的数据包请求
'''
offset = str(offset)
if mark == 0:
url = self.ANSWER_URL.format(offset)
elif mark == 1:
url = ARTICLE_URL.format(offset)
[headers_search, cookies_dict] = self.headers_handle(url)
page_dict = self.get_page_data(url, headers_search, cookies_dict)
if page_dict.get("data"):
print(self.page_count)
if self.page_count == 1000:
self.update_page_count(page_dict["paging"].get("totals", 0))
for one_line in page_dict['data']:
try:
if one_line["content"] != None:
self.parse_sigle_page(one_line, mark)
print("finshed_crawler " + offset)
except KeyError:
# It's totally ok to drop the last return data value.
# The search api just return something seems related to search
print('page data error')
continue
else:
print("article_data_error, offset: ", offset, " url: ", url)
self.use_proxy = True
self.search_answer_article_page(offset=offset, mark=mark)
return
if __name__ == '__main__':
'''
python tasks/zhihu/spider.py 0 1 0 'https://www.zhihu.com/people/taoxi-1130'
python script_file_path mark(指定是问题还是其他， 0 是问题， 1是文章， 2是想法) max_page(最大页码) start_page(起始页码 0开始) spider_url(要爬取用户知乎首页地址)
'''
mark = int(sys.argv[1])
max_page = int(sys.argv[2])
start_page = int(sys.argv[3])
spider_url = sys.argv[4]
# spider_url = 'https://www.zhihu.com/people/geng-mei-suo-chang/answers'
print(datetime.now())
spider = Spider(spider_url=spider_url)
if mark == 0:
spider.search_page(mark, max_page, start_page)
elif mark == 1:
spider.search_page(mark, max_page, start_page)
elif mark == 2:
spider.search_page(mark, max_page, start_page)
print(datetime.now())
tasks/zhihu/spider.py
View file @ d1b63291
......@@ -7,43 +7,18 @@ import re
import sys
import time
from datetime import datetime
import kdl
import base64
from requests_html import HTMLSession
from PIL import Image
from captcha.zhihu_captcha import ZhihuCaptcha
HOST = '172.18.51.14'
PORT = 3306
USER = 'spider'
PASSWD = 'Gengmei123'
DB = 'spider'
# JS_FILE_PATH = '/srv/apps/crawler/crawler_sys/site_crawler/zhihu.js'
JS_FILE_PATH = '/Users/haowei/workspace/gm/crawler/crawler_sys/site_crawler/zhihu.js'
APIKEY = 'quxguz4hwm9cxnx6wpjhkokx04klpr8v'
def get_proxy():
auth = kdl.Auth("990866563045611", APIKEY)
client = kdl.Client(auth)
ips = client.get_dps(1, sign_type='hmacsha1', format='json', area='北京,上海,广东')
print("dps proxy: ", ips)
return { "http": "http://{}".format(ips[0]), "https": "https://{}".format(ips[0]), }
def retry_get_url(url, retrys=5, timeout=10, proxies=None, **kwargs):
retry_c = 0
while retry_c < retrys:
try:
if proxies:
get_resp = requests.get(url, timeout=timeout, proxies=proxies, **kwargs)
else:
get_resp = requests.get(url, timeout=timeout, **kwargs)
return get_resp
except Exception as e:
retry_c += 1
time.sleep(3)
print(e)
proxies = get_proxy()
print('Failed to get page %s after %d retries, %s'
% (url, retrys, datetime.now()))
return None
JS_FILE_PATH = '/srv/apps/crawler/crawler_sys/site_crawler/zhihu.js'
# JS_FILE_PATH = '/Users/haowei/workspace/gm/crawler/crawler_sys/site_crawler/zhihu.js'
class Spider(object):
......@@ -52,6 +27,7 @@ class Spider(object):
'''
初始化数据库，调整js规则
'''
self.login_req = self._login()
self.conn = pymysql.connect(host=HOST, port=PORT, user=USER,
passwd=PASSWD,
db=DB, charset='utf8')
......@@ -71,8 +47,76 @@ class Spider(object):
except:
with open(JS_FILE_PATH, 'r', encoding='utf-8') as f:
js = f.read()
self.exec_js = execjs.compile(js)
# self.exec_js = execjs.compile(js, cwd='/home/gmuser/node_modules')
# self.exec_js = execjs.compile(js)
self.exec_js = execjs.compile(js, cwd='/home/gmuser/node_modules')
self.session = requests.session()
self.HTMLSession = HTMLSession()
def _login(self):
url = 'https://www.zhihu.com'
loginUrl = 'https://www.zhihu.com/login/email'
headers = {
'User-Agent' : 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/55.0.2883.87 Chrome/55.0.2883.87 Safari/537.36',
"Referer": "http://www.zhihu.com/",
'Host': 'www.zhihu.com',
'rememberme': "true"
}
self.headers = headers
data = {
'email': 'yousangdandan@yeah.net',
'password': '5358569'
}
login_req = self.session.post(loginUrl, data=data, headers=headers)
print('loginReq:{}'.format(login_req.status_code))
self.set_cookie(self.session.cookies)
return login_req
def set_cookies(self, cookies):
self.HTMLSession.cookies = cookies
self.session.cookies = cookies
def appeal(self, url):
self.captcha_model = ZhihuCaptcha()
r = self.HTMLSession.get('https://www.zhihu.com/api/v4/anticrawl/captcha_appeal')
captchaUrl = r.json()['img_base64']
captchaUrl = re.sub('\n', '', captchaUrl)
with open('cache/captcha2.png', 'wb') as f:
img_base64 = base64.b64decode(captchaUrl.strip('data:image/png;base64,').strip())
print(img_base64)
f.write(img_base64)
im = Image.open('cache/captcha2.png')
captcha = self.captcha_model.recgImg(im)
print(captcha)
r = self.HTMLSession.post('https://www.zhihu.com/api/v4/anticrawl/captcha_appeal',
data=json.dumps({"captcha": captcha}),
headers={"User-Agent": user_agent,
"referer": 'https://www.zhihu.com/account/unhuman?type=unhuman&message=%E7%B3%BB%E7%BB%9F%E7%9B%91%E6%B5%8B%E5%88%B0%E6%82%A8%E7%9A%84%E7%BD%91%E7%BB%9C%E7%8E%AF%E5%A2%83%E5%AD%98%E5%9C%A8%E5%BC%82%E5%B8%B8%EF%BC%8C%E4%B8%BA%E4%BF%9D%E8%AF%81%E6%82%A8%E7%9A%84%E6%AD%A3%E5%B8%B8%E8%AE%BF%E9%97%AE%EF%BC%8C%E8%AF%B7%E8%BE%93%E5%85%A5%E9%AA%8C%E8%AF%81%E7%A0%81%E8%BF%9B%E8%A1%8C%E9%AA%8C%E8%AF%81%E3%80%82&need_login=false',
'Content-Type': 'application/json',
'x-xsrftoken': self.HTMLSession.cookies._cookies['.zhihu.com']['/']['_xsrf'].value})
return self.HTMLSession.get(url, allow_redirects=False)
def retry_get_url(self, url, retrys=5, timeout=10, **kwargs):
retry_c = 0
while retry_c < retrys:
time.sleep(5)
try:
import pdb; pdb.set_trace()
get_resp = self.session.get(url, headers=self.headers, timeout=timeout, **kwargs)
if get_resp.status_code == 403:
get_resp = self.appeal(url)
return get_resp
except Exception as e:
retry_c += 1
print(e)
print('Failed to get page %s after %d retries, %s'
% (url, retrys, datetime.now()))
return None
def update_page_count(self, answer_count):
count = int(answer_count / 20)
......@@ -81,28 +125,6 @@ class Spider(object):
count += 1
self.page_count = count
def get_serach_page_cookies(self):
'''
cookies更新
'''
headers = {
"accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9",
"cache-control": "max-age=0",
"cookie": 'SESSIONID=UIZ9mtCMPNttU11zx8a9e5eJcTm92PhBGKiz9oqWgDr; JOID=UlsQAkk86vULooRFHj177i1UY9UKHM_SK4OkYDkdytAsgqVlOzFzH1WjgUcZ-R9yCKLnnTcSKj5UlS_DhJu9iUI=; osd=VFoXB0466_IOpYJEGTh86CxTZtIMHcjXLIWlZzwazNErh6JjOjZ2GFOihkIe_x51DaXhnDAXLThVkirEgpq6jEU=; SESSIONID=rsVkcWbq9ESuP7O4fOw4qdMJdkNGnCFu59zCNAAkoIL; JOID=VV4TCkoAD-uttc-DPQ6Y9IZDJxUtIizHhpDtoBElLciBnuqhHkmjAfyyzow6twj5biJFaHi7j_WoTqKkbWlN0QI=; osd=UFkVCk4FCO2tscqEOw6c8YFFJxEoJSrHgpXqphEhKM-Hnu6kGU-jBfm1yIw-sg__biZAb367i_CvSKKgaG5L0QY=; _xsrf=vTWamiEoaOQszAl6fjdlxOtqyhDvOen9; d_c0="AADj9V7Fuw6PTkTnxjUXHGA8UqXz_bkbAN4=|1545899265"; q_c1=3e9e37a46b1d4bfd87f7d1fcb084daad|1545899267000|1545899267000; _ga=GA1.2.929033900.1582626815; capsion_ticket="2|1:0|10:1608602928|14:capsion_ticket|44:MmRhNDdmYWJhZjU3NGQ4ODg3NDAzNGIwNDNiMTdlNDE=|7924fa0d0e36d3ed2a4af65dafa4684c9b36a70d586ec3adb1963c8df5f55e81"; _zap=6fd2d768-daa1-4be1-9a96-43d86c1bbc75; Hm_lvt_98beee57fd2ef70ccdd5ca52b9740c49=1609325059,1609337218,1609401296,1609405637; Hm_lpvt_98beee57fd2ef70ccdd5ca52b9740c49=1609410807; KLBRSID=0a401b23e8a71b70de2f4b37f5b4e379|1609410806|1609401296',
"referer": self.spider_url,
"sec-fetch-dest": "document",
"sec-fetch-mode": "navigate",
"sec-fetch-site": "same-origin",
"sec-fetch-user": "?1",
"upgrade-insecure-requests": "1",
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36",
}
requests_res = retry_get_url(self.spider_url, headers=headers)
return requests_res.cookies.get_dict()
def check_data_exist(self, data_dict, mark):
'''
数据插入前检测
......@@ -124,7 +146,7 @@ class Spider(object):
def parse_sigle_page(self, data_dict, mark):
'''
插入主要内容数据和图片的url，寻找评论
插入主要内容数据和图片的url
'''
if not self.check_data_exist(data_dict, mark):
......@@ -142,8 +164,6 @@ class Spider(object):
self.cur.execute(into, values)
self.conn.commit()
return
def search_page(self, mark, page_max, start_page=0, need_commend=False):
'''
函数主入口
......@@ -167,7 +187,20 @@ class Spider(object):
time.sleep(10)
self.conn.close()
return
def get_page_data(self, url):
get_page = self.retry_get_url(url)
if get_page.status_code != 200:
# retry once
get_page = self.retry_get_url(url)
if get_page.status_code != 200:
print("article_error, url : ", url, " status_code: ", get_page.status_code)
try:
page_dict = get_page.json()
print('get page json data success! {}', offset)
except:
print('retry get page data : {}', offset)
self.get_page_data(url)
def search_answer_article_page(self, offset, mark, proxies_num=0):
'''
......@@ -178,19 +211,8 @@ class Spider(object):
url = self.ANSWER_URL.format(offset)
elif mark == 1:
url = ARTICLE_URL.format(offset)
[headers_search, cookies_dict] = self.headers_handle(url)
proxies = None
if self.use_proxy:
proxies = get_proxy()
get_page = retry_get_url(url, headers=headers_search, cookies=cookies_dict, proxies=proxies)
if get_page.status_code != 200:
# retry once
time.sleep(3)
get_page = retry_get_url(url, proxies=proxies)
if get_page.status_code != 200:
print("article_error, url : ", url, " status_code: ", get_page.status_code)
page_dict = get_page.json()
page_dict = self.get_page_data(url)
if page_dict.get("data"):
print(self.page_count)
if self.page_count == 1000:
......@@ -201,54 +223,15 @@ class Spider(object):
self.parse_sigle_page(one_line, mark)
print("finshed_crawler " + offset)
except KeyError:
# It's totally ok to drop the last return data value.
# The search api just return something seems related to search
print('page data error')
continue
else:
print("article_data_error, offset: ", offset, " url: ", url)
self.use_proxy = True
time.sleep(3)
self.search_answer_article_page(offset=offset, mark=mark)
return
def headers_handle(self, url):
'''
url请求中的头部伪装
'''
res_cookies_dict = self.get_serach_page_cookies()
referer = self.spider_url.replace("https://www.zhihu.com/people", "https://www.zhihu.com/api/v4/members")
headers_search = {
"accept": "*/*",
"accept-encoding": "gzip, deflate",
"accept-language": "zh-CN,zh;q=0.9",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36",
"x-ab-param": "li_yxzl_new_style_a=1;se_major=0;se_adsrank=4;se_hi_trunc=0;se_v053=1;li_panswer_topic=0;zr_test_aa1=1;pf_noti_entry_num=2;zr_search_sim2=2;zr_slotpaidexp=2;se_zp_boost=0;tp_club_entrance=1;pf_profile2_tab=0;ug_newtag=1;li_answer_card=0;ls_recommend_test=5;qap_labeltype=1;zr_rec_answer_cp=open;se_sug_term=0;tp_topic_tab=0;ge_ge01=5;se_wil_act=0;se_videobox=0;tsp_ad_cardredesign=0;qap_question_visitor= 0;zr_slot_training=2;tp_clubhyb=0;li_ebook_gen_search=2;se_v_v005=0;zw_sameq_sorce=999;ge_ge02=6;se_mobilecard=0;se_auth_src=0;tp_header_style=1;tp_flow_ctr=0;pf_creator_card=1;li_viptab_name=0;zr_intervene=0;se_bert128=1;se_ffzx_jushen1=0;top_v_album=1;se_preset=0;tp_discover=1;ls_fmp4=0;tp_club_top=0;top_universalebook=1;li_svip_cardshow=1;li_paid_answer_exp=0;tp_topic_style=0;zr_art_rec=base;se_colorfultab=1;se_auth_src2=0;tp_club_qa_entrance=1;tp_club__entrance2=1;tsp_hotlist_ui=3;li_svip_tab_search=1;se_entity22=1;tp_meta_card=0;tp_topic_tab_new=0-0-0;tp_zrec=0;top_ebook=0;pf_adjust=1;qap_question_author=0;zr_topic_rpc=0;se_topicfeed=0;tp_club_feed=0;tsp_ioscard2=0;zr_rel_search=base;se_recommend=1;se_usercard=0;tp_club_fdv4=0;tp_m_intro_re_topic=1;pf_foltopic_usernum=0;li_vip_verti_search=0;zr_training_boost=false;se_v054=0;tp_contents=1;soc_feed_intelligent=3;tsp_ios_cardredesign=0;pf_fuceng=1;pf_newguide_vertical=0;ug_follow_topic_1=2;ls_video_commercial=0;li_car_meta=1;se_sug_dnn=0;tp_fenqu_wei=0;li_catalog_card=1;top_quality=0;se_click_v_v=1;se_aa_base=1;se_club_ui=0;se_return_1=0;soc_notification=1;zr_ans_rec=gbrank;zr_search_paid=1;zr_expslotpaid=3;zr_rerank=0;se_college=default;se_whitelist=1;top_root=0;li_yxxq_aut=A1;tsp_adcard2=0;ls_videoad=2;se_col_boost=1;li_edu_page=old;zr_training_first=false;se_t2sug=1;se_vbert3=0;se_merge=0;li_video_section=1;zr_km_answer=open_cvr;zr_sim3=0;se_v_v006=0;tp_dingyue_video=0;li_topics_search=0;se_searchwiki=0;se_guess=0;se_major_v2=0;tp_club_bt=0;tp_sft=a;top_test_4_liguangyi=1",
"x-api-version": "3.0.91",
"x-app-za": "OS=Web",
"x-requested-with": "fetch",
"x-zse-83": "3_2.0",
"x-zse-86": None,
"referer": referer + "/answers?page=1",
}
cookies_dict = {
"d_c0": '"AADj9V7Fuw6PTkTnxjUXHGA8UqXz_bkbAN4=|1545899265"',
"KLBRSID": '0a401b23e8a71b70de2f4b37f5b4e379|1609410806|1609401296'
}
cookies_dict.update(res_cookies_dict)
f = "+".join(
["3_2.0", url.replace("https://www.zhihu.com", ""), headers_search["referer"], cookies_dict["d_c0"]])
fmd5 = hashlib.new('md5', f.encode()).hexdigest()
headers_search["x-zse-86"] = "1.0_" + self.exec_js.call("b", fmd5)
return headers_search, cookies_dict
if __name__ == '__main__':
'''
......
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