效果
分割前
分割后
代码
图像预处理
#
equ = cv2.equalizeHist(gray)
#
gaussian = cv2.GaussianBlur(gray, (3, 3), 0, 0, cv2.BORDER_DEFAULT)
#
median = cv2.medianBlur(gaussian, 5)
#
sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize = 3)
#
ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)
#
element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))
element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 7))
#
dilation = cv2.dilate(binary, element2, iterations = 1)
#
erosion = cv2.erode(dilation, element1, iterations = 1)
#
dilation2 = cv2.dilate(erosion, element2,iterations = 2)
查找轮廓 计算矩形
region = []
# 查找轮廓
_,contours,hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 筛选面积小的
for i in range(len(contours)):
cnt = contours[i]
# # 计算该轮廓的面积
# area = cv2.contourArea(cnt)
# 面积小的都筛选掉
# if (area < 2000):
# continue
# 轮廓近似,作用很小
epsilon = 0.001 * cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
# 找到最小的矩形,该矩形可能有方向
rect = cv2.minAreaRect(cnt)
# box是四个点的座标
box = cv2.boxPoints(rect)
box = np.int0(box)
#
height = abs(box[0][1] - box[2][1])
width = abs(box[0][0] - box[2][0])
region.append(box)
保存公式
for box in region:
i += 1
# cv2.drawContours(img, [box], 0, (0, 255, 0), 1)
ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]
xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]
ys_sorted_index = np.argsort(ys)
xs_sorted_index = np.argsort(xs)
x1 = box[xs_sorted_index[0], 0]
x2 = box[xs_sorted_index[3], 0]
y1 = box[ys_sorted_index[0], 1]
y2 = box[ys_sorted_index[3], 1]
img_org2 = img.copy()
img_plate = img_org2[y1:y2, x1:x2]
cv2.imwrite('temp/number_plate'+str(i)+'.jpg',img_plate)
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