class SlideCrack(object):
def __init__(self, gap, bg):
"""
init code
:param gap: 缺口图片
:param bg: 背景图片
:param out: 输出图片
"""
self.front = gap
self.bg = bg
@staticmethod
def clear_white(img):
# 清除图片的空白区域，这里主要清除滑块的空白
img_data = base64.b64decode(img)
img_array = np.fromstring(img_data, np.uint8)
img = cv2.imdecode(img_array, cv2.COLOR_RGB2BGR)
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 template_match(self, tpl, target):
th, tw = tpl.shape[:2]
result = cv2.matchTemplate(target, tpl, cv2.TM_CCOEFF_NORMED)
# 寻找矩阵(一维数组当作向量,用Mat定义) 中最小值和最大值的位置
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
tl = max_loc
br = (tl[0] + tw, tl[1] + th)
# 绘制矩形边框，将匹配区域标注出来
# target：目标图像
# tl：矩形定点
# br：矩形的宽高
# (0,0,255)：矩形边框颜色
# 1：矩形边框大小
cv2.rectangle(target, tl, br, (0, 0, 255), 2)
return tl
@staticmethod
def image_edge_detection(img):
edges = cv2.Canny(img, 100, 200)
return edges
def discern(self):
img1 = self.clear_white(self.front)
img1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
slide = self.image_edge_detection(img1)
img_data = base64.b64decode(self.bg)
img_array = np.fromstring(img_data, np.uint8)
back = cv2.imdecode(img_array, cv2.COLOR_RGB2BGR)
back = self.image_edge_detection(back)
slide_pic = cv2.cvtColor(slide, cv2.COLOR_GRAY2RGB)
back_pic = cv2.cvtColor(back, cv2.COLOR_GRAY2RGB)
pos = self.template_match(slide_pic, back_pic)
# 输出横坐标, 即 滑块在图片上的位置
return pos
