import base64
import random
import time
from io import BytesIO
import cv2
import requests
try:
import matplotlib.pyplot as plt
except:
plt = None
import numpy as np
from PIL import Image
from selenium.webdriver import ActionChains
from scipy import signal
def ease_out_quad(x):
return 1 - (1 - x) * (1 - x)
def ease_out_quart(x):
return 1 - pow(1 - x, 4)
def ease_out_expo(x):
if x == 1:
return 1
else:
return 1 - pow(2, -10 * x)
def ease_out_bounce(x):
n1 = 7.5625
d1 = 2.75
if x < 1 / d1:
return n1 * x * x
elif x < 2 / d1:
x -= 1.5 / d1
return n1 * x * x + 0.75
elif x < 2.5 / d1:
x -= 2.25 / d1
return n1 * x * x + 0.9375
else:
x -= 2.625 / d1
return n1 * x * x + 0.984375
def get_tracks_by_ease_func(distance, ease_func=ease_out_quart, seconds=None, steps=50):
if not seconds:
seconds = random.random() + random.randint(2, 5)
tracks = [0]
offsets = [0]
for t in np.arange(0.0, seconds, seconds / steps):
offset = round(ease_func(t / seconds) * distance)
tracks.append(offset - offsets[-1])
offsets.append(offset)
return tracks
#######################
def get_tracks_by_random(distance):
# 移动轨迹
tracks = []
current = 0
while current < distance:
move = random.randint(-3, 5)
current += move
if current > 0:
tracks.append(move)
return tracks
#########################
def get_random(min, max):
return random.choice(list(range(min, max)))
def get_tracks_by_a(distance):
"""
根据加速度获取移动轨迹
:param distance:
:return: 移动轨迹
"""
# 移动轨迹
tracks = []
# 当前位移
current = 0
# 减速阈值
# mid = distance * 4 / 5
# mid = distance * 3 / 5
mid = distance * 7 / 10
# 计算间隔
# t = 0.2
t = 0.1
# 初速度
v = 4
while current < distance:
if current < mid:
# 加速度为正2
a = 4 + get_random(-2, 2)
else:
# 加速度为负3
a = -6 + get_random(-2, 2)
# 初速度v0
v0 = v
# 当前速度 v = v0 + at
v = v0 + a * t
# 移动距离x = v0t + 1/2 * a * t^2
move = v0 * t + 1 / 2 * a * t * t
# 当前位移
current += move
# 加入轨迹
tracks.append(round(move))
return tracks
def get_tracks_by_a2(distance):
"""
:param S: 缺口距离Px
:return:
"""
distance += 20
v = 0
t = 0.2
tracks = []
current = 0
mid = distance * 3 / 5 # 减速阀值
while current < distance:
if current < mid:
a = 2 + random.random()
else:
a = -3 + random.random()
s = v * t + 0.5 * a * (t ** 2)
v = v + a * t
current += s
tracks.append(round(s))
back_tracks = [-3, -3, -2, -2, -2, -2, -2, -1, -1, -1]
tracks.extend(back_tracks)
return tracks
def get_tracks(distance):
tracks_funcs = [
lambda distance: get_tracks_by_ease_func(
distance, ease_func=ease_out_quad, steps=get_random(30, 50)
),
lambda distance: get_tracks_by_ease_func(
distance, ease_func=ease_out_quart, steps=get_random(30, 50)
),
lambda distance: get_tracks_by_ease_func(
distance, ease_func=ease_out_expo, steps=get_random(30, 50)
),
lambda distance: get_tracks_by_ease_func(
distance, ease_func=ease_out_bounce, steps=get_random(30, 50)
),
lambda distance: get_tracks_by_random(distance),
lambda distance: get_tracks_by_a(distance),
lambda distance: get_tracks_by_a2(distance),
]
tracks_func = random.choice(tracks_funcs)
return tracks_func(distance)
def drag_and_drop(browser, slider, tracks, seconds=None):
if not seconds:
seconds = random.random() + random.randint(2, 5)
unit_times = seconds / len(tracks)
ActionChains(browser).click_and_hold(slider).perform()
for x in tracks:
ActionChains(browser).move_by_offset(x, 0).perform()
time.sleep(unit_times)
ActionChains(browser).pause(0.5).release().perform()
def draw_tracks(tracks, seconds):
distances = []
distance = 0
for track in tracks:
distance += track
distances.append(distance)
times = []
unit_times = seconds / len(tracks)
time = 0
for i in range(len(tracks)):
time += unit_times
times.append(time)
print("tracks: ", tracks)
print("times:", times)
plt.plot(times, distances)
plt.xlabel("time")
plt.ylabel("distance")
plt.show()
########### 获取滑块偏移量 ##############
def get_image(pic_path):
return Image.open(pic_path)
def pic2gray(pic_path, save=False) -> np.ndarray:
"""
图像转为灰度图像
@param pic_path: 图片地址
@return: np.ndarray
"""
pic_path_rgb = cv2.imread(pic_path)
pic_path_gray = cv2.cvtColor(pic_path_rgb, cv2.COLOR_BGR2GRAY)
if save:
cv2.imwrite(pic_path, pic_path_gray)
return pic_path_gray
def img2array(image: Image) -> np.ndarray:
"""
图片转 ndarray
@param image:
@return:
"""
img_array = np.array(image)
return img_array
def array2img(image_array: np.ndarray) -> Image:
"""
array 转 Image
@param image_array:
@return: Image， 可以调用img.show()查看图片
"""
img = Image.fromarray(image_array.astype("uint8")).convert("RGBA")
return img
# def get_gap_edge_position(image: Image, gap_edge_rgb: tuple, show=False, rgb_range=10) -> np.ndarray:
# '''
# 获取缺口 边界
# @param image: 背景图
# @param gap_edge_rgb: 缺口边界rgb
# @param show: 是否画图 方便调试
# @param rgb_range: rgb 上下浮动范围
# @return: numpy.ndarray 二维矩阵，缺口边界为1，其他为0。 横向为列，纵向为行。左上角为原点
# '''
#
# pix = image.load()
# width = image.size[0]
# height = image.size[1]
#
# positions = []
# position_arrays = np.zeros((height, width), dtype=np.int) # 高度为行 宽度为列
#
# for x in range(width):
# for y in range(height):
# try:
# rgb1 = pix[x, y][:3]
# except:
# rgb1 = (pix[x, y], pix[x, y], pix[x, y]) # 灰度图像，只有一个值
#
# if abs(rgb1[0] - gap_edge_rgb[0]) < rgb_range and abs(rgb1[1] - gap_edge_rgb[1]) < rgb_range and abs(rgb1[2] - gap_edge_rgb[2]) < rgb_range:
# positions.append([x, y])
# position_arrays[y, x] = 1
#
# if show:
# x = [position[0] for position in positions]
# y = [height - position[1] for position in positions]
#
# plt.xlim(right=width, left=0)
# plt.ylim(top=height, bottom=0)
# plt.plot(x, y, 'ro')
# plt.show()
#
# return position_arrays
def get_slider_edge_position_by_alpha(
image: Image, alpha=(80, 254), show=False
) -> np.ndarray:
"""
根据边界透明度获取小滑块 边界
@param image: 闭
@param alpha: 透明度 开区间
@param show: 是否画图 方便调试
@return: numpy.ndarray 二维矩阵，缺口边界为1，其他为0。 横向为列，纵向为行。左上角为原点
"""
pix = image.load()
width = image.size[0]
height = image.size[1]
positions = []
position_arrays = np.zeros((height, width), dtype=np.int) # 高度为行 宽度为列
for x in range(width):
for y in range(height):
r, g, b, a = pix[x, y]
if a >= alpha[0] and a <= alpha[1]: # 阴影为半透明 0 为全透明 255为不透明
positions.append([x, y])
position_arrays[y, x] = 1
if show:
x = [position[0] for position in positions]
y = [height - position[1] for position in positions]
plt.xlim(right=width, left=0)
plt.ylim(top=height, bottom=0)
plt.plot(x, y, "ro")
plt.show()
return position_arrays
def get_slider_edge_by_convolve2d(image: Image) -> np.ndarray:
slider_fillter = get_slider_edge_position_by_alpha(
image, show=False, alpha=(255, 255)
)
slider_fillter = delete_blank_edge(slider_fillter)
slider_fillter = expand_edges(slider_fillter, count=1, axis=0, pos=0) # 首行
slider_fillter = expand_edges(slider_fillter, count=1, axis=1, pos=0) # 首列
slider_fillter = expand_edges(slider_fillter, count=1, axis=0, pos=-1) # 末行
slider_fillter = expand_edges(slider_fillter, count=1, axis=1, pos=-1) # 末列
# 做卷积 将中心为1的点去掉
fillter = asarray([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
slider_fillter = convolve2d(slider_fillter, fillter)
# 4 周为负数，非边界，只有大于0的才是边界
slider_fillter = cover_number(
slider_fillter != 0, 1, 0
) # slider_fillter != 0 则边缘粗度为2
# slider_fillter = delete_blank_edge(slider_fillter)
return slider_fillter
def print_array(array: np.ndarray):
np.set_printoptions(threshold=np.inf, linewidth=np.inf)
print(array)
def asarray(array: list) -> np.ndarray:
"""
数组转np.ndarray
@param array: 二维的list
@return:
"""
return np.asarray(array)
def delete_blank_edge(array: np.ndarray):
"""
删除空白的边界（整行 或 整列都为0 则删除）
@param array:
@return:
"""
rows, cols = array.shape[:2]
for row in range(rows): # 上到下遍历, 删除水平为0的行，到不为0的行时结束
if sum(array[0,]) == 0:
array = np.delete(array, 0, 0)
else:
break
rows, cols = array.shape[:2]
for row in reversed(range(rows)): # 下到上遍历, 删除水平为0的行，到不为0的行时结束
if sum(array[-1,]) == 0:
array = np.delete(array, -1, 0)
else:
break
rows, cols = array.shape[:2]
for col in range(cols): # 上到下遍历, 删除垂直为0的列，到不为0的列时结束
if sum(array[:, 0]) == 0:
array = np.delete(array, 0, 1)
else:
break
rows, cols = array.shape[:2]
for col in reversed(range(cols)): # 下到上遍历, 删除垂直为0的列，到不为0的列时结束
if sum(array[:, -1]) == 0:
array = np.delete(array, -1, 1)
else:
break
return array
def cover_number(condition, x, y):
"""
转换数组中的值为指定的值 条件为真时转换为x，为假时转为y
eg:
np.where(a > 0, a, 0) # 将 类型为np.ndarray的a值大于零的不动，小于零的改为0
@return:
"""
return np.where(condition, x, y)
def expand_edges(array: np.ndarray, *, count, axis, pos=0) -> np.ndarray:
"""
扩大边缘
@param array:
@param count: 边缘数量
@param axis: 0/1 (0 为行 1 为列)
@param pos: 插入的位置 (0为首行或首列 -1为末行或末列)
@return:
"""
if axis == 0:
zero_array = np.zeros((array.shape[1]), dtype=np.int)
else:
zero_array = np.zeros((array.shape[0]), dtype=np.int)
if pos == -1:
pos = array.shape[0] if axis == 0 else array.shape[1]
array = np.insert(array, pos, values=zero_array, axis=axis)
return array
def convolve2d(bg_array: np.ndarray, fillter: np.ndarray) -> np.ndarray:
"""
same 2d卷积 参考：https://blog.csdn.net/m0_38007695/article/details/82794454
@param bg_array: 背景二维二维矩阵
@param fillter: 小窗口二维矩阵, 行列最好为奇数
@return:
"""
bg_h, bg_w = bg_array.shape[:2]
# K的高和宽
fillter_h, fillter_w = fillter.shape[:2]
# 计算full卷积
c_full = signal.convolve2d(bg_array, fillter, mode="full")
# 指定锚点的位置
kr, kc = fillter_h // 2, fillter_w // 2
# 根据锚点的位置，从full卷积中截取得到same卷积
c_same = c_full[
fillter_h - kr - 1 : bg_h + fillter_h - kr - 1,
fillter_w - kc - 1 : bg_w + fillter_w - kc - 1,
]
return c_same
def canny_edge(image_array: np.ndarray, show=False) -> np.ndarray:
"""
锐化边缘
@param image_array:
@return:
"""
can = cv2.Canny(image_array, threshold1=200, threshold2=300)
if show:
cv2.imshow("candy", can)
cv2.waitKey()
cv2.destroyAllWindows()
return can
def draw_pic_from_array(array: np.ndarray) -> None:
"""
根据array 画图
@param array:
@return:
"""
plt.figure() # 打开matplotlib的可视化figure
plt.imshow(array)
plt.gray() # 灰阶图
plt.title("filter image") # 标题
plt.show()
def find_gap_edge_center_point(
bg_arrays: np.ndarray, slide_arrags: np.ndarray
) -> tuple:
"""
获取缺口中心点
@param bg_arrays: 背景图缺口边界矩阵
@param slide_arrags: 滑块边界矩阵
@return: 中心点 (x,y) 横向为x， 纵向y，左上角为坐标原点
"""
slide_window_row, slide_window_cols = slide_arrags.shape # 滑块窗口的行列数
bg_rows, bg_cols = bg_arrays.shape # 背景图的行列数
bg_vertical_center = bg_rows // 2 # 滑块缺口一般在垂直的中间位置 及 中间行
slider_window_center_vertical_padding = slide_window_row // 2 # 滑动窗口中心点到上下边距 及 垂直边距
slider_window_center_horizontal_padding = (
slide_window_cols // 2
) # 滑动窗口中心点到左右边距 及 水平边距
max_point_sum = 0
center_point = None
for col in range(
slider_window_center_horizontal_padding,
bg_cols - slider_window_center_horizontal_padding + 1,
): # 横向移动滑块窗口，求滑块窗口内的矩阵乘积
if (
bg_vertical_center + slider_window_center_vertical_padding > bg_rows
or col + slider_window_center_horizontal_padding > bg_cols
):
break
current_window = bg_arrays[
bg_vertical_center
- slider_window_center_vertical_padding : bg_vertical_center
+ slider_window_center_vertical_padding,
col
- slider_window_center_horizontal_padding : col
+ slider_window_center_horizontal_padding,
] # 当前被遮罩的窗口
try:
point_sum = (slide_arrags * current_window).sum()
except:
continue
if point_sum > max_point_sum:
max_point_sum = point_sum
center_point = (col, bg_vertical_center)
return center_point
def find_max_point(arrays: np.ndarray, search_on_horizontal_center=False) -> tuple:
"""
找二维数组中最大的点
@param arrays:
@param search_on_horizontal_center: 只在水平居中处找
@return: 中心点 (x,y) 横向为x， 纵向y，左上角为坐标原点
"""
max_point = 0
max_point_pos = None
array_rows, array_cols = arrays.shape
if search_on_horizontal_center:
for col in range(array_cols):
if arrays[array_rows // 2, col] > max_point:
max_point = arrays[array_rows // 2, col]
max_point_pos = col, array_rows // 2
else:
for row in range(array_rows):
for col in range(array_cols):
if arrays[row, col] > max_point:
max_point = arrays[row, col]
max_point_pos = col, row
return max_point_pos
def draw_pos(pic: str, pos: tuple, rect_range=None):
"""
画点 画点时以左下角为原点
@param pic: 图片
@param pos: 点的位置
@return:
"""
img = cv2.imread(pic)
if rect_range:
left_top_pos = pos[0] - rect_range[1] // 2, pos[1] - rect_range[0] // 2
right_bottom_pos = pos[0] + rect_range[1] // 2, pos[1] + rect_range[0] // 2
else:
left_top_pos = pos
right_bottom_pos = pos
cv2.rectangle(
img, left_top_pos, right_bottom_pos, (0, 0, 255), 2
) # 图片 左上角位置 右下角位置 矩形框颜色 边框像素
if rect_range:
cv2.rectangle(img, pos, pos, (0, 0, 255), 2) # 图片 左上角位置 右下角位置 矩形框颜色 边框像素
cv2.imshow(pic, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def get_offset_x(slide_arrags: np.ndarray, gap_edge_center_point: tuple) -> int:
"""
获取滑块的偏移量
@param slide_arrags: 滑块数组
@param gap_edge_center_point: 缺口坐标
@return:
"""
return gap_edge_center_point[0] - slide_arrags.shape[1] // 2
def clear_white(img: str) -> np.ndarray:
"""
清除图片的空白区域，这里主要清除滑块的空白
Args:
img: 图片地址
Returns:
"""
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 get_gap_center_point(bg_pic, slider_pic, show=False) -> tuple:
"""
获取缺口位置, 为上面函数的汇总调度
@param bg_pic:
@param slider_pic:
@return: (x, y)
"""
# slider_image = get_image(slider_pic)
bg_array = pic2gray(bg_pic) # 图像灰度画
# 锐化边缘
edge_detection_array = canny_edge(bg_array, show=show)
# # # 获取滑块 filter
# # slider_edge_fillter = get_slider_edge_position(slider_image, show=False, alpha=(30, 254)) # 阴影边界 根据透明度
# slider_content_fillter = get_slider_edge_position_by_alpha(slider_image, show=False, alpha=(255, 255)) # 内部图形
# #
# # # 删除空白边界
# # slider_edge_fillter = delete_blank_edge(slider_edge_fillter)
# # slider_content_fillter = delete_blank_edge(slider_content_fillter)
# #
# # # 内缩一些隐形边界，当实际边界也落入到阴影里，因为上面得到的边界为阴影边界，实际匹配时有可能是匹配轮廓，而非阴影
# # slider_edge_content_difference = (slider_edge_fillter.shape[0] - slider_content_fillter.shape[0], slider_edge_fillter.shape[1] - slider_content_fillter.shape[1])
# # slider_fillter = cv2.resize(slider_edge_fillter, (slider_edge_fillter.shape[0] - slider_edge_content_difference[0] // 2, slider_edge_fillter.shape[1] - slider_edge_content_difference[1] // 2),
# # interpolation=cv2.INTER_AREA) # https://blog.csdn.net/zh_jessica/article/details/77946346
#
# slider_content_fillter = delete_blank_edge(slider_content_fillter)
# print_array(slider_content_fillter)
#
# fillter = asarray([
# [-1, -1, -1],
# [-1, 8, -1],
# [-1, -1, -1],
# ])
#
# slider_content_fillter = convolve2d(slider_content_fillter, fillter)
#
# slider_fillter = cover_number(slider_content_fillter != 0, 1, 0)
# print_array(slider_fillter)
# slider_fillter = get_slider_edge_by_convolve2d(slider_image)
# draw_pic_from_array(slider_fillter)
slider_fillter = clear_white(slider_pic)
slider_fillter = canny_edge(slider_fillter, show=show)
slider_fillter = np.rot90(slider_fillter, 2) # 逆时针旋转90度2次 即180度, 卷积的定义就是要旋转180度...
# 将为0 的点设置为负数，防止匹配到噪声
# total_point = slider_fillter.size
# no_zero_point = sum(sum(slider_fillter))
# zero_point = total_point - no_zero_point
# slider_fillter = cover_number(
# slider_fillter == 0, -no_zero_point / zero_point, 1
# ) # 为0点转换为 -1 * 不为零点数/为零点数。这样矩阵和为0
# print_array(slider_fillter)
# draw_pic_from_array(slider_fillter)
# 找缺口中心点
# center_point = find_gap_edge_center_point(edge_detection_array, slider_fillter)
# draw_pic_from_array(edge_detection_array)
edge_filltered = convolve2d(
edge_detection_array, slider_fillter
) # 用fillter 与 蜕化后的背景做卷积
# draw_pic_from_array(edge_filltered)
center_point = find_max_point(edge_filltered)
center_point = center_point[0], center_point[1]
if show:
draw_pos(bg_pic, center_point, rect_range=slider_fillter.shape)
return center_point
if __name__ == "__main__":
# seconds = random.randint(2, 10) + random.random()
# distinct = 300 + random.randint(2, 10) + random.random()
# tracks, seconds = get_tracks_by_ease_func(distinct, ease_out_quart, seconds)
# tracks = get_tracks(distinct)
# tracks = get_tracks_by_random(300)
# tracks = get_tracks_by_a(300)
#
# draw_tracks(tracks, seconds)
# 获取滑块 filter
# slider_image = get_image('slide.png')
#
# # slider_edge_fillter = get_slider_edge_position(slider_image, show=False, alpha=(30, 254))
# slider_content_fillter = get_slider_edge_position_by_alpha(slider_image, show=False, alpha=(255, 255))
#
# # 处理滑块fillter
# # slider_edge_fillter = delete_blank_edge(slider_edge_fillter)
# # print_array(slider_edge_fillter)
#
# slider_content_fillter = delete_blank_edge(slider_content_fillter)
# print_array(slider_content_fillter)
#
# # 四周插入一圈为0的点, 为了方便找边缘
# zero_array_row = np.zeros((slider_content_fillter.shape[1]), dtype=np.int)
# zero_array_col = np.zeros((slider_content_fillter.shape[0] + 2), dtype=np.int) # 插完行后 列多了两列
#
# slider_content_fillter = np.insert(slider_content_fillter, 0, values=zero_array_row, axis=0) # 首行
# slider_content_fillter = np.insert(slider_content_fillter, slider_content_fillter.shape[0], values=zero_array_row, axis=0) # 末行
# slider_content_fillter = np.insert(slider_content_fillter, 0, values=zero_array_col, axis=1) # 首列
# slider_content_fillter = np.insert(slider_content_fillter, slider_content_fillter.shape[1], values=zero_array_col, axis=1) # 首列
#
# # print_array(slider_content_fillter)
#
# fillter = asarray([
# [-1, -1, -1],
# [-1, 8, -1],
# [-1, -1, -1],
# ])
#
# slider_content_fillter = convolve2d(slider_content_fillter, fillter)
# print_array(slider_content_fillter)
#
# slider_content_fillter = cover_number(slider_content_fillter != 0, 1, 0)
# slider_content_fillter = delete_blank_edge(slider_content_fillter)
# print_array(slider_content_fillter)
# slider_edge_content_difference = (slider_edge_fillter.shape[0] - slider_content_fillter.shape[0], slider_edge_fillter.shape[1] - slider_content_fillter.shape[1])
# # print(slider_edge_content_difference)
#
# slider_fillter = cv2.resize(slider_edge_fillter, (slider_edge_fillter.shape[0] - slider_edge_content_difference[0] // 2, slider_edge_fillter.shape[1] - slider_edge_content_difference[1] // 2),
# interpolation=cv2.INTER_NEAREST)
#
# print_array(slider_fillter)
# array = asarray([
# [1, 2],
# [3, 4]
# ])
#
# array = expand_edges(array, count=1, axis=0, pos=0)
# array = expand_edges(array, count=1, axis=1, pos=0)
# array = expand_edges(array, count=1, axis=0, pos=-1)
# array = expand_edges(array, count=1, axis=1, pos=-1)
#
# print(array)
# img = 'WechatIMG1282.jpeg'
#
# img = pic2gray(img)
#
# canny_edge(img, show=True)
# get_gap_center_point("test_files/bg2.png", "test_files/slider2.png", show=True)
# a = pic2gray("test_files/bg3.png", True)
# print_array(a)
# bg = asarray(
# [
# [0, 0, 0, 0, 0, 0, 0],
# [0, 0, 1, 1, 1, 0, 0],
# [0, 0, 1, 1, 1, 0, 0],
# [0, 0, 1, 1, 1, 0, 0],
# [0, 0, 0, 0, 0, 0, 0],
# ]
# )
#
# # fillter = np.rot90(fillter, 2) # 逆时针旋转90度2次 即180度
# # print(fillter)
#
# filte2 = asarray([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
#
# result = convolve2d(bg, filte2)
# print(result)
image = Image.open("download.jpg")
start = time.time()
print(new_get_gap_point(image, type=1, size="280x158"))
print("耗时: ", time.time() - start)
标签：轨迹,滑块,fillter,验证码,slider,tracks,edge,np,array From： https://www.cnblogs.com/yoyo1216/p/17502974.html