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一、参考资料

二、代码

distance_between.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# ================================================================
#   Copyright (C) 2019 * Ltd. All rights reserved.
#   Project ：pythonTest 
#   File name   : distance_between.py
#   Author      : yoyo
#   Contact     : cs_jxau@163.com
#   Created date: 2021-03-08 10:03:26
#   Editor      : yoyo
#   Modify Time : 2021-03-08 10:03:26
#   Version     : 1.0
#   IDE         : PyCharm2020
#   License     : Copyright (C) 2017-2021
#   Description : 使用OpenCV测量图像中物体之间的距离
# https://mp.weixin.qq.com/s/XvbkQm6x_zrnT8kU8i0iew
# python distance_between.py --image ./images/example_01.png --width 0.955
# ================================================================

# 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    W
        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)

三、素材图片

编辑

四、效果图

终端执行
>>> python distance_between.py --image ./images/example_01.png --width 0.955

编辑

五、结论

不足：用OpenCV传统图像处理测量距离，图像容易受到光照和复杂背景环境的影响，影响物体的检测，进而影响物体之间的距离计算

改进：用深度学习方法，精确检测出物体，然后计算物体间的距离