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slider_util.py
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slider_util.py
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# -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 12:55
# @Desc : 滑块相关的工具包
import os
from typing import List
from urllib.parse import urlparse
import cv2
import httpx
import numpy as np
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) -> List[int]:
# 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: List[int] = []
# 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 # type: ignore
# 移动距离
move = v0 * t + 1 / 2 * a * t * t
# 当前位移
current += move # type: ignore
# 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