# 图像轮廓最大内接矩形的求法

img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.blur(gray, (9, 9))
_, thresh = cv2.threshold(blurred, 155, 255, cv2.THRESH_BINARY)
_, cnts, _ = cv2.findContours( thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
rect = cv2.minAreaRect(c)
#box即为最小外接矩形坐标
box = np.int0(cv2.boxPoints(rect))
cv2.drawContours(img, [box], -1, (0, 255, 0), 3)
cv2.imshow("Image", img)
cv2.imwrite("pic.jpg", img)
cv2.waitKey(0)


def order_points(pts):
# pts为轮廓坐标
# 列表中存储元素分别为左上角，右上角，右下角和左下角
rect = np.zeros((4, 2), dtype = "float32")
# 左上角的点具有最小的和，而右下角的点具有最大的和
s = pts.sum(axis = 1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
# 计算点之间的差值
# 右上角的点具有最小的差值,
# 左下角的点具有最大的差值
diff = np.diff(pts, axis = 1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
# 返回排序坐标(依次为左上右上右下左下)
return rect


img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.blur(gray, (9, 9))
_, thresh = cv2.threshold(blurred, 155, 255, cv2.THRESH_BINARY)
_, cnts, _ = cv2.findContours( thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]先找出轮廓点
rect = order_points(c.reshape(c.shape[0], 2))
print(rect)
xs = [i[0] for i in rect]
ys = [i[1] for i in rect]
xs.sort()
ys.sort()
#内接矩形的坐标为
print(xs[1],xs[2],ys[1],ys[2])


posted @ 2019-11-15 14:55  奥布莱恩  阅读(6792)  评论(0编辑  收藏  举报