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- def generate_distance(slice_url, bg_url):
- """
- :param bg_url: 背景图地址
- :param slice_url: 滑块图地址
- :return: distance
- :rtype: Integer
- """
- slide = ddddocr.DdddOcr(det=False, ocr=False, show_ad=False)
- slice_image = requests.get(slice_url).content
- bg_image = requests.get(bg_url).content
- result = slide.slide_match(target_bytes, bg_image, simple_target=True)
- return result['target'][0]
- def generate_distance(slice_url, bg_url):
- """
- :param bg_url: 背景图地址
- :param slice_url: 滑块图地址
- :return: distance
- :rtype: Integer
- """
- slice_image = np.asarray(bytearray(requests.get(slice_url).content), dtype=np.uint8)
- slice_image = cv2.imdecode(slice_image, 1)
- slice_image = cv2.Canny(slice_image, 255, 255)
-
- bg_image = np.asarray(bytearray(requests.get(bg_url).content), dtype=np.uint8)
- bg_image = cv2.imdecode(bg_image, 1)
- bg_image = cv2.pyrMeanShiftFiltering(bg_image, 5, 50)
- bg_image = cv2.Canny(bg_image, 255, 255)
-
- result = cv2.matchTemplate(bg_image, slice_image, cv2.TM_CCOEFF_NORMED)
-
- min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
-
- return max_loc[0]
- def generate_track(distance):
- def __ease_out_expo(step):
- return 1 if step == 1 else 1 - pow(2, -10 * step)
-
- tracks = [[random.randint(20, 60), random.randint(10, 40), 0]]
- count = 30 + int(distance / 2)
- _x, _y = 0, 0
- for item in range(count):
- x = round(__ease_out_expo(item / count) * distance)
- t = random.randint(10, 20)
- if x == _x:
- continue
- tracks.append([x - _x, _y, t])
- _x = x
- tracks.append([0, 0, random.randint(200, 300)])
- times = sum([track[2] for track in tracks])
- return tracks, times
本篇文章篇幅不长,主要也没啥好说的,验证码研究多了,识别和轨迹就那几套方法,换汤不换药
函数a(e, t)中的重头戏:c.guid()、_.encrypt()、i.encrypt()、c.arrayToHex()四个函数我们放到浩瀚篇再说吧,不然我这紫极魔瞳四大境界变成三大境界了,哈哈哈
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