从轮廓

Question

我正在尝试使用OpenCV从图像中识别并提取一个相当明显的区域。到目前为止，通过使用阈值和一系列扩张和糜烂，我可以成功找到我需要的区域的轮廓。

但是，我尝试使用minAreaRect作为旋转和裁剪的前体，无法生成包含输入轮廓的矩形。

contours, hierarchy = cv2.findContours(morph.copy() ,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contour = contours[0]

draw = cv2.cvtColor(morph, cv2.COLOR_GRAY2BGR)
cv2.drawContours(draw, [contour], 0, (0,255,0), 2)

rotrect = cv2.minAreaRect(contour)
box = cv2.cv.BoxPoints(rotrect)
box = numpy.int0(box)
cv2.drawContours(draw, [box], 0, (0,0,255), 2)

cv2.imshow('image', draw); cv2.waitKey(0)

以下是输出的例子：

红色笔划为rect，绿色为contour。我原本预计红色中风会包含绿色中风。

不幸的是我无法提供输入图像。

Answer 1

我最终通过实现自己的旋转卡尺程序来找到最小矩形来解决这个问题。它使用凸包来确定候选旋转。

def p2abs(point):
    return math.sqrt(point[0] ** 2 + point[1] ** 2)

def rotatePoint(point, angle):
    s, c = math.sin(angle), math.cos(angle)
    return (p[0] * c - p[1] * s, p[0] * s + p[1] * c)

def rotatePoints(points, angle):
    return [rotatePoint(point, angle) for point in points]

points = map(lambda x: tuple(x[0]), contour)
convexHull = map(lambda x: points[x], scipy.spatial.ConvexHull(numpy.array(points)).vertices)

minArea = float("inf")
minRect = None

for i in range(len(hull)):
    a, b = convexHull[i], convexHull[i - 1]
    ang = math.atan2(b[0] - a[0], b[1] - a[1])

    rotatedHull = rotatePoints(convexHull, ang)

    minX = min(map(lambda p: p[0], rotatedHull))
    maxX = max(map(lambda p: p[0], rotatedHull))
    minY = min(map(lambda p: p[1], rotatedHull))
    maxY = max(map(lambda p: p[1], rotatedHull))

    area = (maxX - minX) * (maxY - minY)

    if area < minArea:
        minArea = area

        rotatedRect = [(minX, minY), (minX, maxY), (maxX, maxY), (maxX, minY)]
        minRect = rotatePoints(rotatedRect, -ang)

_, topLeft = min([(p2abs(p), i) for p, i in zip(range(4), minRect)])
rect = minrect[topLeft:] + minrect[:topLeft]