Object Measuring Size OpenCV Python With Source Code

Object Measuring Size OpenCV Python With Source Code

The Object Measuring Size OpenCV Python was developed using Python OpenCV, the project provides a script to read an image and based on the dimensions of a reference object find the dimensions of other objects in a scene.

A Object Measuring we will learn how to perform object measurement using OpenCV and Python. We will use an image as our guide and find the measurement size of objects placed in this region.

In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way, if you are new to Python programming and you don’t know what Python IDE to use, I have here a list of the Best Python IDE for Windows, Linux, and Mac OS that will suit you.

I also have here How to Download and Install the Latest Version of Python on Windows.

To start executing Object Measuring Size With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.

Object Measuring Size OpenCV Python With Source Code: Steps on how to run the project

Installed Libraries

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

Complete Source Code

# USAGE
# python object_size.py --image images/example_01.png --width 0.955
# python object_size.py --image images/example_02.png --width 0.955
# python object_size.py --image images/example_03.png --width 3.5

# 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 initialize the
# 'pixels per metric' calibration variable
(cnts, _) = contours.sort_contours(cnts)
pixelsPerMetric = 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
	orig = image.copy()
	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)
	cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)

	# loop over the original points and draw them
	for (x, y) in box:
		cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)

	# unpack the ordered bounding box, then compute the midpoint
	# between the top-left and top-right coordinates, followed by
	# the midpoint between bottom-left and bottom-right coordinates
	(tl, tr, br, bl) = box
	(tltrX, tltrY) = midpoint(tl, tr)
	(blbrX, blbrY) = midpoint(bl, br)

	# compute the midpoint between the top-left and top-right points,
	# followed by the midpoint between the top-righ and bottom-right
	(tlblX, tlblY) = midpoint(tl, bl)
	(trbrX, trbrY) = midpoint(tr, br)

	# draw the midpoints on the image
	cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
	cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
	cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
	cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)

	# draw lines between the midpoints
	cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
		(255, 0, 255), 2)
	cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
		(255, 0, 255), 2)

	# compute the Euclidean distance between the midpoints
	dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
	dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))

	# if the pixels per metric has not been initialized, then
	# compute it as the ratio of pixels to supplied metric
	# (in this case, inches)
	if pixelsPerMetric is None:
		pixelsPerMetric = dB / args["width"]

	# compute the size of the object
	dimA = dA / pixelsPerMetric
	dimB = dB / pixelsPerMetric

	# draw the object sizes on the image
	cv2.putText(orig, "{:.1f}in".format(dimA),
		(int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,
		0.65, (255, 255, 255), 2)
	cv2.putText(orig, "{:.1f}in".format(dimB),
		(int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
		0.65, (255, 255, 255), 2)

	# show the output image
	cv2.imshow("Image", orig)
	cv2.waitKey(0)

Output:

Download the Source Code below

Click the button below to download the complete source code:

Summary

The Object Measuring Size was developed using Python OpenCV, the project provides a script to read an image and based on the dimensions of a reference object find the dimensions of other objects in a scene.

It Also Includes a downloadable project with source code for free.

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Angel Jude Suarez

Full-Stack Developer at PIES IT Solution

Focuses on Python development, machine learning, and AI integration. Has built production AI systems including OpenAI Whisper integration for medical transcription and GPT-4o-powered diagnosis assistance. Strong background in pandas, scikit-learn, and TensorFlow.

Expertise: Python · PHP · Java · VB.NET · ASP.NET · Machine Learning · AI Integration · OpenCV · Django · CodeIgniter  · View all posts by Angel Jude Suarez →