CaptchaCv2.py
3.95 KB
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import random
import cv2
import numpy as np
import pandas as pd
import math
import os
# x方向一阶导中值
def get_dx_median(dx, x, y, w, h):
return np.median(dx[y:(y + h), x])
# 预处理
def pre_process(img_path):
img = cv2.imread(img_path, 1)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转成灰度图像
_, binary = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY) # 将灰度图像转成二值图像
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) # 查找轮廓
rect_area = []
rect_arc_length = []
cnt_infos = {}
for i, cnt in enumerate(contours):
if cv2.contourArea(cnt) < 5000 or cv2.contourArea(cnt) > 25000:
continue
x, y, w, h = cv2.boundingRect(cnt)
cnt_infos[i] = {'rect_area': w * h, # 矩形面积
'rect_arclength': 2 * (w + h), # 矩形周长
'cnt_area': cv2.contourArea(cnt), # 轮廓面积
'cnt_arclength': cv2.arcLength(cnt, True), # 轮廓周长
'cnt': cnt, # 轮廓
'w': w,
'h': h,
'x': x,
'y': y,
'mean': np.mean(np.min(img[y:(y + h), x:(x + w)], axis=2)), # 矩形内像素平均
}
rect_area.append(w * h)
rect_arc_length.append(2 * (w + h))
dx = cv2.Sobel(img, -1, 1, 0, ksize=5)
return img, dx, cnt_infos
def qq_mark_pos(img_path):
if not os.path.exists(img_path):
print("文件不存在")
return 0
img, dx, cnt_infos = pre_process(img_path)
h, w = img.shape[:2]
df = pd.DataFrame(cnt_infos).T
df.head()
df['dx_mean'] = df.apply(lambda x: get_dx_median(dx, x['x'], x['y'], x['w'], x['h']), axis=1)
df['rect_ratio'] = df.apply(lambda v: v['rect_arclength'] / 4 / math.sqrt(v['rect_area'] + 1), axis=1)
df['area_ratio'] = df.apply(lambda v: v['rect_area'] / v['cnt_area'], axis=1)
df['score'] = df.apply(lambda x: abs(x['rect_ratio'] - 1), axis=1)
result = df.query('x>0').query('area_ratio<2').query('rect_area>5000').query('rect_area<20000').sort_values(
['mean', 'score', 'dx_mean']).head(2)
if len(result):
x_left = result.x.values[0]
# cv2.line(img, (x_left, 0), (x_left, h), color=(255, 0, 255))
# plt.imshow(img)
# plt.show()
return result
def get_track_list(distance):
"""
模拟轨迹 假装是人在操作
"""
# 初速度
v = 0
# 单位时间为0.2s来统计轨迹，轨迹即0.2内的位移
t = 0.2
# 位移/轨迹列表，列表内的一个元素代表0.2s的位移
tracks = []
# 当前的位移
current = 0
# 到达mid值开始减速
mid = distance * 7 / 8
distance += 10 # 先滑过一点，最后再反着滑动回来
# a = random.randint(1,3)
while current < distance:
if current < mid:
# 加速度越小，单位时间的位移越小,模拟的轨迹就越多越详细
a = random.randint(2, 4) # 加速运动
else:
a = -random.randint(3, 5) # 减速运动
# 初速度
v0 = v
# 0.2秒时间内的位移
s = v0 * t + 0.5 * a * (t ** 2)
# 当前的位置
current += s
# 添加到轨迹列表
tracks.append(round(s))
# 速度已经达到v,该速度作为下次的初速度
v = v0 + a * t
# 反着滑动到大概准确位置
for i in range(4):
tracks.append(-random.randint(2, 3))
for i in range(4):
tracks.append(-random.randint(1, 3))
return tracks
# if __name__ == "__main__":
# img_path = "bg.png"
# if not os.path.exists(img_path):
# print("文件不存在")
# res = qq_mark_pos(img_path)
# x = res.x.values[0]
# x_r = 344 * x / 699
# track = get_track_list(res.x.values[0])
# print(f'x:{x},xr:{x_r}, track:{track}')