一. 识别滑块缺口
- 使用ddddocr识别
该算法识别准确率为95%左右,测试三轮,每轮测试100次
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]
- 使用cv2识别
该算法识别准确率为95%左右,测试三轮,每轮测试100次
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]
二. 构造滑块轨迹
- 构造轨迹库
图片长度为300,理论上就300种轨迹,实际上应该是200+种,还要减去滑块图的长度80
手动滑他个几百次,并把距离和轨迹记录下来,识别出距离后直接查对应轨迹 - 算法构造轨迹track
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
三. 结语
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