img_center_distance_between.py

# 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
import os

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
# path
img_dir = './images/'
img_path = os.path.join(img_dir,'DSC09561_c.jpg')

# object
width = 2.000 # (inch)

# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread(img_path)
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 / 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)])

	# coordinates for center point of bounding box
	xA = refCoords[4][0]
	yA = refCoords[4][1]
	xB = objCoords[4][0]
	yB = objCoords[4][1]
	color = colors[4]

	# draw circles corresponding to the current points and connect them with a line
	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)