item-sort/main.py

# import the necessary packages
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import math

itemw = 0
itemh = 0


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

def sizeVexScrew(iteml):
	# Screw Sizing code
	# subtract screw head size to find thread length
	shead = 0.09
	iteml -= shead
	#print("Thread Length: " + str(iteml))
	iteml *= 8
	iteml = round(iteml)
	iteml /= 8
	return iteml
	
def larger(a, b):
	if a >= b:
		return a
	else: 
		return b

# 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)")
ap.add_argument("-n", "--number", type=int, required=False,
	help="object # to measure (from left to right)")
ap.add_argument("-s", "--show", action="store_true",
	help="show on the screen")
args = vars(ap.parse_args())
args2 = ap.parse_args()
selected = 2
if type(args["number"]) == type(selected):
	selected = args["number"]

# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread(args["image"])
image = cv2.resize(image, (image.shape[1]*2, image.shape[0]*2), interpolation = cv2.INTER_NEAREST)
if args2.show:
	cv2.imshow("Screw Length Detection", image)
	cv2.waitKey(0)
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)
if args2.show:
	cv2.imshow("Screw Length Detection", 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)
pixelsPerMetric = None
num = 0
# loop over the contours individually
for c in cnts:
	num += 1
	# 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 = np.linalg.norm(np.array((tltrX, tltrY, 0)) - np.array((blbrX, blbrY, 0)))
	dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) - np.array((trbrX, trbrY, 0)))

	# 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
	
	if num == num:
		iteml = larger(dimA, dimB)
		print("Screw Length (RAW): " + str(iteml))
		iteml = sizeVexScrew(iteml)
		print("Rounded Length: " + str(iteml))
	# draw the object sizes on the image
		if args2.show:
			cv2.putText(orig, "{:.5f}in".format(larger(dimA, dimB)),
				(int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
				0.65, (255, 255, 255), 2)
			if num > 1:
				cv2.putText(orig, "{:.3f}in screw".format(iteml), # print screw length
					(int(trbrX + 20), int(trbrY + 20)), cv2.FONT_HERSHEY_SIMPLEX,
					0.65, (255, 255, 255), 2)
 
	# show the output image
			cv2.imshow("Screw Length Detection", orig)
			cv2.waitKey(0)