feat: 抖音三种方式登录实现 & 抖音滑块模拟滑动实现

tools/utils.py

@@ -1,11 +1,16 @@
 import re
+import os
 import time
 import random
 import base64
 import logging
 from io import BytesIO
+from urllib.parse import urlparse
 from typing import Optional, Dict, List, Tuple
 
+import cv2
+import httpx
+import numpy as np
 from PIL import Image, ImageDraw
 from playwright.async_api import Cookie
 from playwright.async_api import Page
@@ -71,6 +76,8 @@ def convert_str_cookie_to_dict(cookie_str: str) -> Dict:
         if not cookie:
             continue
         cookie = cookie.split("=")
+        if len(cookie) != 2:
+            continue
         cookie_value = cookie[1]
         if isinstance(cookie_value, list):
             cookie_value = "".join(cookie_value)
@@ -102,3 +109,157 @@ def init_loging_config():
         datefmt='%Y-%m-%d  %H:%M:%S'
     )
     logging.Logger("Media Crawler")
+
+
+class Slide:
+    """
+    copy from https://blog.csdn.net/weixin_43582101 thanks for author
+    update: relakkes
+    """
+
+    def __init__(self, gap, bg, gap_size=None, bg_size=None, out=None):
+        """
+        :param gap: 缺口图片链接或者url
+        :param bg: 带缺口的图片链接或者url
+        """
+        self.img_dir = os.path.join(os.getcwd(), 'temp_image')
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+
+        bg_resize = bg_size if bg_size else (340, 212)
+        gap_size = gap_size if gap_size else (68, 68)
+        self.bg = self.check_is_img_path(bg, 'bg', resize=bg_resize)
+        self.gap = self.check_is_img_path(gap, 'gap', resize=gap_size)
+        self.out = out if out else os.path.join(self.img_dir, 'out.jpg')
+
+    @staticmethod
+    def check_is_img_path(img, img_type, resize):
+        if img.startswith('http'):
+            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,en-GB;q=0.8,en;q=0.7,ja;q=0.6",
+                "Cache-Control": "max-age=0",
+                "Connection": "keep-alive",
+                "Host": urlparse(img).hostname,
+                "Upgrade-Insecure-Requests": "1",
+                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+                              "Chrome/91.0.4472.164 Safari/537.36",
+            }
+            img_res = httpx.get(img, headers=headers)
+            if img_res.status_code == 200:
+                img_path = f'./temp_image/{img_type}.jpg'
+                image = np.asarray(bytearray(img_res.content), dtype="uint8")
+                image = cv2.imdecode(image, cv2.IMREAD_COLOR)
+                if resize:
+                    image = cv2.resize(image, dsize=resize)
+                cv2.imwrite(img_path, image)
+                return img_path
+            else:
+                raise Exception(f"保存{img_type}图片失败")
+        else:
+            return img
+
+    @staticmethod
+    def clear_white(img):
+        """清除图片的空白区域，这里主要清除滑块的空白"""
+        img = cv2.imread(img)
+        rows, cols, channel = img.shape
+        min_x = 255
+        min_y = 255
+        max_x = 0
+        max_y = 0
+        for x in range(1, rows):
+            for y in range(1, cols):
+                t = set(img[x, y])
+                if len(t) >= 2:
+                    if x <= min_x:
+                        min_x = x
+                    elif x >= max_x:
+                        max_x = x
+
+                    if y <= min_y:
+                        min_y = y
+                    elif y >= max_y:
+                        max_y = y
+        img1 = img[min_x:max_x, min_y: max_y]
+        return img1
+
+    def template_match(self, tpl, target):
+        th, tw = tpl.shape[:2]
+        result = cv2.matchTemplate(target, tpl, cv2.TM_CCOEFF_NORMED)
+        # 寻找矩阵(一维数组当作向量,用Mat定义) 中最小值和最大值的位置
+        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
+        tl = max_loc
+        br = (tl[0] + tw, tl[1] + th)
+        # 绘制矩形边框，将匹配区域标注出来
+        # target：目标图像
+        # tl：矩形定点
+        # br：矩形的宽高
+        # (0,0,255)：矩形边框颜色
+        # 1：矩形边框大小
+        cv2.rectangle(target, tl, br, (0, 0, 255), 2)
+        cv2.imwrite(self.out, target)
+        return tl[0]
+
+    @staticmethod
+    def image_edge_detection(img):
+        edges = cv2.Canny(img, 100, 200)
+        return edges
+
+    def discern(self):
+        img1 = self.clear_white(self.gap)
+        img1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
+        slide = self.image_edge_detection(img1)
+
+        back = cv2.imread(self.bg, cv2.COLOR_RGB2GRAY)
+        back = self.image_edge_detection(back)
+
+        slide_pic = cv2.cvtColor(slide, cv2.COLOR_GRAY2RGB)
+        back_pic = cv2.cvtColor(back, cv2.COLOR_GRAY2RGB)
+        x = self.template_match(slide_pic, back_pic)
+        # 输出横坐标, 即 滑块在图片上的位置
+        return x
+
+
+def get_track_simple(distance):
+    # 有的检测移动速度的 如果匀速移动会被识别出来，来个简单点的 渐进
+    # distance为传入的总距离
+    # 移动轨迹
+    track = []
+    # 当前位移
+    current = 0
+    # 减速阈值
+    mid = distance * 4 / 5
+    # 计算间隔
+    t = 0.2
+    # 初速度
+    v = 1
+
+    while current < distance:
+        if current < mid:
+            # 加速度为2
+            a = 4
+        else:
+            # 加速度为-2
+            a = -3
+        v0 = v
+        # 当前速度
+        v = v0 + a * t
+        # 移动距离
+        move = v0 * t + 1 / 2 * a * t * t
+        # 当前位移
+        current += move
+        # 加入轨迹
+        track.append(round(move))
+    return track
+
+
+def get_tracks(distance: int, level: str = "easy") -> List[int]:
+    if level == "easy":
+        return get_track_simple(distance)
+    else:
+        from . import easing
+        _, tricks = easing.get_tracks(distance, seconds=2, ease_func="ease_out_expo")
+        return tricks