This content originally appeared on DEV Community and was authored by Stokry
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:
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
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)
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)
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)
find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
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
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)])
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()
This is our final result:
Thank you all.
This content originally appeared on DEV Community and was authored by Stokry
Stokry | Sciencx (2021-05-12T18:09:57+00:00) How to calculate the distance between the objects in the image with Python. Retrieved from https://www.scien.cx/2021/05/12/how-to-calculate-the-distance-between-the-objects-in-the-image-with-python/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.