164 lines
5.5 KiB
Python
164 lines
5.5 KiB
Python
# -*- coding: utf-8 -*-
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# @Author : relakkes@gmail.com
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# @Time : 2023/12/2 12:55
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# @Desc : 滑块相关的工具包
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import os
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from typing import List
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from urllib.parse import urlparse
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import cv2
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import httpx
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import numpy as np
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class Slide:
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"""
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copy from https://blog.csdn.net/weixin_43582101 thanks for author
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update: relakkes
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"""
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def __init__(self, gap, bg, gap_size=None, bg_size=None, out=None):
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"""
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:param gap: 缺口图片链接或者url
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:param bg: 带缺口的图片链接或者url
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"""
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self.img_dir = os.path.join(os.getcwd(), 'temp_image')
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if not os.path.exists(self.img_dir):
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os.makedirs(self.img_dir)
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bg_resize = bg_size if bg_size else (340, 212)
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gap_size = gap_size if gap_size else (68, 68)
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self.bg = self.check_is_img_path(bg, 'bg', resize=bg_resize)
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self.gap = self.check_is_img_path(gap, 'gap', resize=gap_size)
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self.out = out if out else os.path.join(self.img_dir, 'out.jpg')
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@staticmethod
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def check_is_img_path(img, img_type, resize):
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if img.startswith('http'):
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headers = {
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"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;"
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"q=0.8,application/signed-exchange;v=b3;q=0.9",
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"Accept-Encoding": "gzip, deflate, br",
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"Accept-Language": "zh-CN,zh;q=0.9,en-GB;q=0.8,en;q=0.7,ja;q=0.6",
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"AbstractCache-Control": "max-age=0",
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"Connection": "keep-alive",
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"Host": urlparse(img).hostname,
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"Upgrade-Insecure-Requests": "1",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/91.0.4472.164 Safari/537.36",
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}
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img_res = httpx.get(img, headers=headers)
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if img_res.status_code == 200:
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img_path = f'./temp_image/{img_type}.jpg'
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image = np.asarray(bytearray(img_res.content), dtype="uint8")
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image = cv2.imdecode(image, cv2.IMREAD_COLOR)
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if resize:
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image = cv2.resize(image, dsize=resize)
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cv2.imwrite(img_path, image)
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return img_path
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else:
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raise Exception(f"保存{img_type}图片失败")
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else:
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return img
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@staticmethod
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def clear_white(img):
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"""清除图片的空白区域，这里主要清除滑块的空白"""
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img = cv2.imread(img)
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rows, cols, channel = img.shape
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min_x = 255
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min_y = 255
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max_x = 0
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max_y = 0
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for x in range(1, rows):
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for y in range(1, cols):
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t = set(img[x, y])
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if len(t) >= 2:
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if x <= min_x:
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min_x = x
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elif x >= max_x:
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max_x = x
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if y <= min_y:
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min_y = y
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elif y >= max_y:
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max_y = y
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img1 = img[min_x:max_x, min_y: max_y]
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return img1
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def template_match(self, tpl, target):
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th, tw = tpl.shape[:2]
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result = cv2.matchTemplate(target, tpl, cv2.TM_CCOEFF_NORMED)
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# 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
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
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tl = max_loc
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br = (tl[0] + tw, tl[1] + th)
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# 绘制矩形边框，将匹配区域标注出来
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# target：目标图像
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# tl：矩形定点
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# br：矩形的宽高
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# (0,0,255)：矩形边框颜色
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# 1：矩形边框大小
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cv2.rectangle(target, tl, br, (0, 0, 255), 2)
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cv2.imwrite(self.out, target)
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return tl[0]
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@staticmethod
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def image_edge_detection(img):
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edges = cv2.Canny(img, 100, 200)
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return edges
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def discern(self):
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img1 = self.clear_white(self.gap)
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img1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
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slide = self.image_edge_detection(img1)
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back = cv2.imread(self.bg, cv2.COLOR_RGB2GRAY)
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back = self.image_edge_detection(back)
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slide_pic = cv2.cvtColor(slide, cv2.COLOR_GRAY2RGB)
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back_pic = cv2.cvtColor(back, cv2.COLOR_GRAY2RGB)
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x = self.template_match(slide_pic, back_pic)
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# 输出横坐标, 即 滑块在图片上的位置
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return x
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def get_track_simple(distance) -> List[int]:
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# 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
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# distance为传入的总距离
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# 移动轨迹
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track: List[int] = []
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# 当前位移
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current = 0
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# 减速阈值
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mid = distance * 4 / 5
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# 计算间隔
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t = 0.2
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# 初速度
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v = 1
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while current < distance:
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if current < mid:
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# 加速度为2
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a = 4
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else:
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# 加速度为-2
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a = -3
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v0 = v
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# 当前速度
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v = v0 + a * t # type: ignore
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# 移动距离
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move = v0 * t + 1 / 2 * a * t * t
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# 当前位移
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current += move # type: ignore
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# 加入轨迹
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track.append(round(move))
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return track
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def get_tracks(distance: int, level: str = "easy") -> List[int]:
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if level == "easy":
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return get_track_simple(distance)
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else:
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from . import easing
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_, tricks = easing.get_tracks(distance, seconds=2, ease_func="ease_out_expo")
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return tricks
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