import cv2

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def show(name):
    '''展示圈出來的位置'''
    cv2.imshow('Show', name)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


def _tran_canny(image):
    """消除噪聲"""
    image = cv2.GaussianBlur(image, (3, 3), 0)
    return cv2.Canny(image, 50, 150)


def detect_displacement(img_slider_path, image_background_path):
    """detect displacement"""
    # # 參數(shù)0是灰度模式
    image = cv2.imread(img_slider_path, 0)
    template = cv2.imread(image_background_path, 0)

    # 尋找最佳匹配
    res = cv2.matchTemplate(_tran_canny(image), _tran_canny(template), cv2.TM_CCOEFF_NORMED)
    # 最小值，最大值，并得到最小值, 最大值的索引
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    top_left = max_loc[0]  # 橫坐標(biāo)
    # 展示圈出來的區(qū)域
    x, y = max_loc  # 獲取x,y位置坐標(biāo)

    w, h = image.shape[::-1]  # 寬高
    cv2.rectangle(template, (x, y), (x + w, y + h), (7, 249, 151), 2)
    show(template)
    return top_left

if __name__ == '__main__':
    top_left = detect_displacement("target.png", "background.png")
    print(top_left)

# 展示圈出來的區(qū)域
  #  x, y = max_loc  # 獲取x,y位置坐標(biāo)

  #  w, h = image.shape[::-1]  # 寬高
  #  cv2.rectangle(template, (x, y), (x + w, y + h), (7, 249, 151), 2)
  #  show(template)

#coding=utf-8
import cv2
import numpy as np
from PIL import Image
 
def get_element_slide_distance():
 
 otemp = 'captcha2.png'
 oblk = 'captcha1.png'
 target = cv2.imread(otemp, 0) # 讀取進(jìn)行色度圖片，轉(zhuǎn)換為numpy中的數(shù)組類型數(shù)據(jù)
 template = cv2.imread(oblk, 0)
 width, height = target.shape[::-1] # 獲取缺口圖數(shù)組的形狀 -->缺口圖的寬和高
 temp = 'temp.jpg' # 將處理之后的圖片另存
 targ = 'targ.jpg'
 cv2.imwrite(temp, template)
 cv2.imwrite(targ, target)
 target = cv2.imread(targ) # 讀取另存的滑塊圖
 target = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY) # 進(jìn)行色彩轉(zhuǎn)換
 # 去除白色部分 獲取滑塊正常大小
 target = target[target.any(1)]
 
 target = abs(255 - target) # 獲取色差的絕對(duì)值
 cv2.imwrite(targ, target) # 保存圖片
 target = cv2.imread(targ) # 讀取滑塊
 template = cv2.imread(temp) # 讀取背景圖
 result = cv2.matchTemplate(target, template, cv2.TM_CCOEFF_NORMED) # 比較兩張圖的重疊區(qū)域
 top, left = np.unravel_index(result.argmax(), result.shape) # 獲取圖片的缺口位置
 #缺口位置
 print((left, top, left + width, top + height)) # 背景圖中的圖片缺口坐標(biāo)位置
 
 #調(diào)用PIL Image 做測(cè)試
 image = Image.open("captcha1.png")
 
 rectangle = (left + 3, top + 3, left + width - 3, top + height - 3) #去掉白色塊的影響(上面去掉白色部分的功能并沒有真的起作用)
 #切割
 imagecrop = image.crop(rectangle)
 #保存切割的缺口
 imagecrop.save("new_image.jpg")
 
 return left+3
 
distance = get_element_slide_distance()
# 滑動(dòng)距離誤差校正，滑動(dòng)距離*圖片在網(wǎng)頁上顯示的縮放比-滑塊相對(duì)的初始位置
distance = distance*(280/680) - 22

def generate_tracks1(XCoordinates):
 element = browser.find_element_by_xpath("http://div[@class='secsdk-captcha-drag-icon sc-jKJlTe fsBatO']")
 ActionChains(browser).click_and_hold(on_element = element).perform()
 #
 # ActionChains(browser).move_by_offset(xoffset=0, yoffset=y - 445).perform()
 #
 # time.sleep(0.15)
 # print("第二步，拖動(dòng)元素")
 distance = XCoordinates - 60
 # 初速度
 v = 0
 # 單位時(shí)間為0.2s來統(tǒng)計(jì)軌跡，軌跡即0.2內(nèi)的位移,越低看起來越絲滑！！
 t = 0.08
 # 位移/軌跡列表，列表內(nèi)的一個(gè)元素代表0.2s的位移
 tracks = []
 # 當(dāng)前的位移
 current = 0
 # 到達(dá)mid值開始減速
 mid = distance * 5 / 8
 
 distance += 10  # 先滑過一點(diǎn)，最后再反著滑動(dòng)回來
 # a = random.randint(1,3)
 while current < distance:
  if current < mid:
   # 加速度越小，單位時(shí)間的位移越小,模擬的軌跡就越多越詳細(xì)
   a = random.randint(100, 200)  # 加速運(yùn)動(dòng)
  else:
   a = -random.randint(2, 10)  # 減速運(yùn)動(dòng)
 
  # 初速度
  v0 = v
  # 0.2秒時(shí)間內(nèi)的位移
  s = v0 * t + 0.5 * a * (t ** 2)
  # 當(dāng)前的位置
  current += s
  # 添加到軌跡列表
  tracks.append(round(s))
 
  # 速度已經(jīng)達(dá)到v,該速度作為下次的初速度
  v = v0 + a * t
  if current > distance:
   break
 
 # 反著滑動(dòng)到大概準(zhǔn)確位置
 # for i in range(4):
 #     tracks.append(-random.randint(1, 3))
 # for i in range(4):
 #    tracks.append(-random.randint(1,3))
 random.shuffle(tracks)
 count = 0
 for item in tracks:
  print(item)
  count += item
  ActionChains(browser).move_by_offset(xoffset = item, yoffset = random.randint(-2, 2)).perform()
 
 # ActionChains(browser).move_to_element_with_offset(to_element=element, xoffset=XCoordinates-18,yoffset=y - 445).perform()
 # time.sleep(2)
 # # 釋放鼠標(biāo)
 print(count)
 ActionChains(browser).release(on_element = element).perform()