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How to calculate the distance between the objects in the image with Python

stokry profile image Stokry ・2 min read

Today I want to show you how to calculate the distance between the objects in the image. We will write an awesome algorithm that you can modify and extend to your needs.

This is our test image:

enter image description here

Let's jump to the code!

First, we need to 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
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Then we construct the argument parse and parse the arguments

def midpoint(ptA, ptB):
    return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
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after that we load the image, convert it to grayscale:

image = cv2.imread('images/test.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
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then we perform edge detection and 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)
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find contours in the edge map

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
                        cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
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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
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then we loop over the contours individually:

for c in cnts:
    if cv2.contourArea(c) < 100:
        continue
    box = cv2.minAreaRect(c)
    box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
    box = np.array(box, dtype="int")
    box = perspective.order_points(box)
    cX = np.average(box[:, 0])
    cY = np.average(box[:, 1])
    if refObj is None:
        (tl, tr, br, bl) = box
        (tlblX, tlblY) = midpoint(tl, bl)
        (trbrX, trbrY) = midpoint(tr, br)
        D = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
        refObj = (box, (cX, cY), D / 70)
        continue
    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)
    refCoords = np.vstack([refObj[0], refObj[1]])
    objCoords = np.vstack([box, (cX, cY)])
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then we loop over the original points:

for ((xA, yA), (xB, yB), color) in zip(refCoords, objCoords, colors):
        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)
        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)
        cv2.imshow("Image", orig)
        cv2.waitKey(0)
cv2.destroyAllWindows()
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This is our final result:

enter image description here

Thank you all.

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