opencv 36 measure distance by contour

 Let’s say we have a marker or object with a known width W. We then place this marker some distance D from our camera. We take a picture of our object using our camera and then measure the apparent width in pixels P. This allows us to derive the perceived focal length F of our camera:

F = (P x D) / W

As I continue to move my camera both closer and farther away from the object/marker, I can apply the triangle similarity to determine the distance of the object to the camera:

D’ = (W x F) / P

card has known width 0.28ft, camera is also 0.28ft above

apparent width of card on image is 397

focal length is also 397 from fomular 1

canny edge find contour

apparent width is 208 now when camera is lifted

distance is calculated from fouler 2

#main.py

from imutils import paths

import numpy as np

import imutils

import cv2

def find_marker(image):

    # convert the image to grayscale, blur it, and detect edges

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    gray = cv2.GaussianBlur(gray, (5, 5), 0)

    edged = cv2.Canny(gray, 30, 100)

    cv2.imshow("canny edge", edged)

    # find the contours in the edged image and keep the largest one;

    # we'll assume that this is our piece of paper in the image

    cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

    cnts = imutils.grab_contours(cnts)

    c = max(cnts, key=cv2.contourArea)

    # compute the bounding box of the of the paper region and return it

    return cv2.minAreaRect(c)

def distance_to_camera(knownWidth, focalLength, perWidth):

    # compute and return the distance from the maker to the camera

    return (knownWidth * focalLength) / perWidth

# initialize the known distance from the camera to the object, which

# in this case is 3.4 inches

KNOWN_DISTANCE = 3.4

# initialize the known object width, which in this case, the piece of

# card is 3.4 inches wide

KNOWN_WIDTH = 3.4

# load the furst image that contains an object that is KNOWN TO BE 2 feet

# from our camera, then find the paper marker in the image, and initialize

# the focal length

image = cv2.imread("assets/distance/initial.jpg")

marker = find_marker(image)

print(marker)

focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH

print("focal length is " + str(focalLength))

# loop over the images

for imagePath in sorted(paths.list_images("assets/distance")):

    # load the image, find the marker in the image, then compute the

    # distance to the marker from the camera

    image = cv2.imread(imagePath)

    marker = find_marker(image)

    print(marker)

    inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])

    # draw a bounding box around the image and display it

    box = cv2.cv.BoxPoints(marker) if imutils.is_cv2() else cv2.boxPoints(marker)

    box = np.int0(box)

    cv2.drawContours(image, [box], -1, (0, 255, 0), 2)

    cv2.putText(image, "%.2fft" % (inches / 12),

                (image.shape[1] - 200, image.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,

                2.0, (0, 255, 0), 3)

    cv2.imshow("image", image)

    cv2.waitKey(0)

reference:

https://www.pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/