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 packages
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)
# construct the argument parse and parse the arguments
ap = 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 slightly
image = 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 edges
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)
# find contours in the edge map
cnts = 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 individually
for c in cnts:
  # if the contour is not sufficiently large, ignore it
  if cv2.contourArea(c) < 100:
    continue
  # compute the rotated bounding box of the contour
  box = 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
  # box
  box = perspective.order_points(box)
  # compute the center of the bounding box
  cX = 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 object
  if 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 image
  orig = 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 points
  for ((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)