在选择图像中的轮廓时如何避免对图像（条）进行分组？

Question

我正在从图表图像中提取单个柱的长度。它在大多数情况下工作正常，但在某些情况下，等高线将 2 个条形图分组为 1 个条形图，这对我的事业不利。我尝试了 canny、dilate、erode 和配色方案的不同组合。它仅略微改善了结果。如何避免分组？这是完整的代码和一张图片。您也可以运行使用此图像来查看问题。

from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2

def midpoint(ptA, ptB):
    return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)

image = cv2.imread("somefile.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)

#edged=cv2.Laplacian(gray, cv2.CV_8U, gray, ksize=7)

edged = cv2.Canny(gray, 30, 50)
cv2.imwrite("test00.png", edged)
edged = cv2.dilate(edged, None, iterations=1)
cv2.imwrite("test01.png", edged)
edged = cv2.erode(edged, None, iterations=1)
cv2.imwrite("test02.png", edged)

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)


pixelsPerMetric = 100

for c in cnts:
    orig = image.copy()
    box = cv2.minAreaRect(c)
    box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
    box = np.array(box, dtype="int")
    print(box)

    box = perspective.order_points(box)
    cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)

    for (x, y) in box:
        cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)

    (tl, tr, br, bl) = box
    (tltrX, tltrY) = midpoint(tl, tr)
    (blbrX, blbrY) = midpoint(bl, br)

    (tlblX, tlblY) = midpoint(tl, bl)
    (trbrX, trbrY) = midpoint(tr, br)

    cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)

    cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
             (255, 0, 255), 2)
    cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
             (255, 0, 255), 2)

    dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
    dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))


    dimA = dA / pixelsPerMetric
    dimB = dB / pixelsPerMetric

    cv2.putText(orig, "{:.1f}in".format(dimA),
                (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,
                0.65, (255, 255, 255), 2)
    cv2.putText(orig, "{:.1f}in".format(dimB),
                (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
                0.65, (255, 255, 255), 2)

    cv2.imshow("Image", orig)
    cv2.waitKey(0)

Answer 1

这张图片分割起来很简单。条形的颜色恰好是 RGB=(245,222,179)。您可以使用 OpenCV 的函数 inRange 来查找这种颜色的像素。在这个函数中，我们需要按 BGR 顺序给出颜色，因为这是 OpenCV 默认读取图像的方式。如果图像使用 JPEG 压缩（这是有损的，因此会稍微改变像素值），我在这里选择一个稍大的范围：

image = cv2.imread("somefile.png")
mask = cv2.inRange(image, (177, 220, 243), (181, 224, 247))

此图像 mask 现在具有完全分隔的条形：

在选择图像中的轮廓时如何避免对图像（条）进行分组？

How to avoid grouping images (bars) while selecting contours in an image?

python

opencv

computer-vision

cv2