object_size.py

# USAGE
# python main.py -i ./input/Straps1.jpg
# python main.py -i ./input/Generated/angle75.png

# import the necessary packages
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import imutils
import cv2


def midpoint(ptA, ptB):
	return (ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5


def objectsize(image, marker, pixelsPerMetric):
	print("WE RECIEVED:", marker)
	# convert image to grayscale, and blur it slightly
	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, 15, 100)
	edged = cv2.dilate(edged, None, iterations=1)
	edged = cv2.erode(edged, None, iterations=1)

	# cv2.imshow("Edges", edged)
	cv2.imwrite("./output/edges.jpg", edged)
	cv2.waitKey(0)

	# 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)
	boxes = []

	# 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.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)
		boxes.append(box)
	'''
	minDist = 99999.
	for box in boxes:  # loop through boxes to find the one closest to the aruco marker, set the PPM
		distance = abs(box[2][0]-marker[0])+abs(box[2][1]-marker[1])  # rough difference between marker and current box
		print("contour:", box[2])
		print("distance:", distance)
		if distance < minDist:  # Identifies potential markers:  the correct box will have the smallest distance
			print("CLOSEST contour:", box[2])
			minDist = distance
			pixelsPerMetric = dist.euclidean(box[2], box[1]) / 8  # calculates vertical length of the marker
	'''
	outName = 1
	for box in boxes:
		orig = image.copy()
		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))


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

		# draw the object sizes on the image
		cv2.putText(orig, "{:.1f}cm".format(dimB),
					(int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,
					0.65, (255, 64, 64), 2)
		cv2.putText(orig, "{:.1f}cm".format(dimA),
					(int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
					0.65, (255, 64, 64), 2)
		print("Object " + str(outName)+": " + "{:.1f}cm".format(dimB) + " by {:.1f}cm".format(dimA))
		# show the output image
		# cv2.imshow("Image", orig)
		cv2.imwrite("./output/object "+str(outName)+".jpg", orig)
		outName = outName+1
		cv2.waitKey(0)