如何使用OpenCV测量图像中物体之间的距离

原文链接：https://www.pyimagesearch.com/2016/04/04/measuring-distance-between-objects-in-an-image-with-opencv/

# import the necessary packagesfrom scipy.spatial import distance as distfrom imutils import perspectivefrom imutils import contoursimport numpy as npimport argparseimport imutilsimport cv2def midpoint(ptA, ptB):return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)# construct the argument parse and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-i", "--image", required=True,  help="path to the input image")ap.add_argument("-w", "--width", type=float, required=True,  help="width of the left-most object in the image (in inches)")args = vars(ap.parse_args())

# load the image, convert it to grayscale, and blur it slightlyimage = cv2.imread(args["image"])gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)gray = cv2.GaussianBlur(gray, (7, 7), 0)# perform edge detection, then perform a dilation + erosion to# close gaps in between object edgesedged = cv2.Canny(gray, 50, 100)edged = cv2.dilate(edged, None, iterations=1)edged = cv2.erode(edged, None, iterations=1)# find contours in the edge mapcnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,  cv2.CHAIN_APPROX_SIMPLE)cnts = imutils.grab_contours(cnts)# sort the contours from left-to-right and, then initialize the# distance colors and reference object(cnts, _) = contours.sort_contours(cnts)colors = ((0, 0, 255), (240, 0, 159), (0, 165, 255), (255, 255, 0),  (255, 0, 255))refObj = None

# loop over the contours individuallyfor c in cnts:  # if the contour is not sufficiently large, ignore itif cv2.contourArea(c) < 100:continue  # compute the rotated bounding box of the contourbox = cv2.minAreaRect(c)box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)box = np.array(box, dtype="int")  # order the points in the contour such that they appear  # in top-left, top-right, bottom-right, and bottom-left  # order, then draw the outline of the rotated bounding  # boxbox = perspective.order_points(box)  # compute the center of the bounding boxcX = np.average(box[:, 0])cY = np.average(box[:, 1])

# if this is the first contour we are examining (i.e.,# the left-most contour), we presume this is the# reference objectif refObj is None:# unpack the ordered bounding box, then compute the# midpoint between the top-left and top-right points,# followed by the midpoint between the top-right and# bottom-right    (tl, tr, br, bl) = box    (tlblX, tlblY) = midpoint(tl, bl)    (trbrX, trbrY) = midpoint(tr, br)# compute the Euclidean distance between the midpoints,# then construct the reference object    D = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))    refObj = (box, (cX, cY), D / args["width"])continue

# draw the contours on the imageorig = image.copy()  cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)  cv2.drawContours(orig, [refObj[0].astype("int")], -1, (0, 255, 0), 2)# stack the reference coordinates and the object coordinates# to include the object center  refCoords = np.vstack([refObj[0], refObj[1]])  objCoords = np.vstack([box, (cX, cY)])

# loop over the original pointsfor ((xA, yA), (xB, yB), color) in zip(refCoords, objCoords, colors):# draw circles corresponding to the current points and# connect them with a line    cv2.circle(orig, (int(xA), int(yA)), 5, color, -1)    cv2.circle(orig, (int(xB), int(yB)), 5, color, -1)    cv2.line(orig, (int(xA), int(yA)), (int(xB), int(yB)),      color, 2)# compute the Euclidean distance between the coordinates,# and then convert the distance in pixels to distance in# units    D = dist.euclidean((xA, yA), (xB, yB)) / refObj[2]    (mX, mY) = midpoint((xA, yA), (xB, yB))    cv2.putText(orig, "{:.1f}in".format(D), (int(mX), int(mY - 10)),      cv2.FONT_HERSHEY_SIMPLEX, 0.55, color, 2)# show the output image    cv2.imshow("Image", orig)    cv2.waitKey(0)