You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
295 lines
9.0 KiB
Python
295 lines
9.0 KiB
Python
# import the necessary packages
|
|
#from imutils import perspective
|
|
from imutils import contours
|
|
import numpy as np
|
|
import argparse
|
|
import imutils
|
|
import cv2
|
|
import math
|
|
import time
|
|
|
|
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 = 0.1
|
|
iteml -= shead
|
|
#print("Thread Length: " + str(iteml))
|
|
iteml *= 8
|
|
iteml = round(iteml)
|
|
iteml /= 8
|
|
return iteml
|
|
|
|
def sizeStandoff(iteml):
|
|
# Standoff Sizing code
|
|
iteml *= 2
|
|
iteml = round(iteml)
|
|
iteml /= 2
|
|
return iteml
|
|
|
|
|
|
def larger(a, b):
|
|
if a >= b:
|
|
return a
|
|
else:
|
|
return b
|
|
|
|
def smaller(a, b):
|
|
if a < b:
|
|
return a
|
|
else:
|
|
return b
|
|
|
|
def near(a, b, close):
|
|
if abs(a-b) < close:
|
|
return True
|
|
return False
|
|
# 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, (int(image.shape[1]*1), int(image.shape[0]*1)))
|
|
image = cv2.resize(image, (1000, int(image.shape[0]/image.shape[1] * 1000)), interpolation = cv2.INTER_NEAREST)
|
|
|
|
if args2.show:
|
|
cv2.imshow("Item Sorter", 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("Item Sorter", 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
|
|
|
|
|
|
|
|
|
|
|
|
# Calibration loop
|
|
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")
|
|
#box = perspective.order_points(box)
|
|
(tl, tr, br, bl) = box
|
|
(tltrX, tltrY) = midpoint(tl, tr)
|
|
(blbrX, blbrY) = midpoint(bl, br)
|
|
(tlblX, tlblY) = midpoint(tl, bl)
|
|
(trbrX, trbrY) = midpoint(tr, br)
|
|
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)))
|
|
area_box = dA * dB
|
|
(x,y),radius = cv2.minEnclosingCircle(c)
|
|
area_contour = cv2.contourArea(c)
|
|
area_circle = math.pi * pow(radius, 2)
|
|
boxiness = area_contour / area_box
|
|
circleness = area_contour / area_circle
|
|
circular = False
|
|
rectangular = False
|
|
if boxiness > circleness:
|
|
rectangular = True
|
|
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
|
|
else:
|
|
circular = True
|
|
cv2.circle(orig,(int(x),int(y)),int(radius),(0,255,0),2)
|
|
mask = np.zeros(gray.shape,np.uint8)
|
|
cv2.drawContours(mask,[c],0,255,-1)
|
|
#pixelpoints = np.transpose(np.nonzero(mask))
|
|
hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV)
|
|
mean_val = cv2.mean(hsv,mask = mask)
|
|
print(str(mean_val[0]))
|
|
#print(", " + str(mean_val[0]/mean_val[2]))
|
|
#print(", " + str(mean_val[2]/mean_val[1]))
|
|
if pixelsPerMetric is None and circular is True and near(mean_val[0], 16, 4.5):
|
|
# and near(mean_val[0], 63, 40) is True and near(mean_val[1], 108, 40) is True and near(mean_val[2], 104, 40) is True:
|
|
pixelsPerMetric = smaller(dA, dB) / args["width"]
|
|
|
|
|
|
|
|
orig = image.copy()
|
|
# loop over the contours individually
|
|
for c in cnts:
|
|
orig = image.copy()
|
|
num += 1
|
|
# if the contour is not sufficiently large, ignore it
|
|
if cv2.contourArea(c) < 100 or pixelsPerMetric is None:
|
|
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)
|
|
|
|
# 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
|
|
# 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)))
|
|
|
|
|
|
dimA = dA / pixelsPerMetric
|
|
dimB = dB / pixelsPerMetric
|
|
|
|
if num == selected or args2.show:
|
|
area_box = dA * dB
|
|
(x,y),radius = cv2.minEnclosingCircle(c)
|
|
area_contour = cv2.contourArea(c)
|
|
area_circle = math.pi * pow(radius, 2)
|
|
boxiness = area_contour / area_box
|
|
circleness = area_contour / area_circle
|
|
circular = False
|
|
rectangular = False
|
|
if boxiness > circleness:
|
|
rectangular = True
|
|
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
|
|
else:
|
|
circular = True
|
|
cv2.circle(orig,(int(x),int(y)),int(radius),(0,255,0),2)
|
|
|
|
|
|
objtype = "Unknown"
|
|
itemw = larger(dimA, dimB)
|
|
itemwr = itemw
|
|
itemwr *= 8
|
|
itemwr = round(itemwr)
|
|
itemwr /= 8
|
|
|
|
itemh = smaller(dimA, dimB)
|
|
itemhr = itemh
|
|
itemhr *= 16
|
|
itemhr = round(itemhr)
|
|
itemhr /= 16
|
|
if circular and itemwr == 0.75:
|
|
objtype = "Penny"
|
|
iteml = 0
|
|
else:
|
|
epsilon = 3#0.02*cv2.arcLength(c,True)
|
|
#print(str(epsilon))
|
|
approx = cv2.approxPolyDP(c,epsilon,True)
|
|
hull = cv2.convexHull(approx, returnPoints=False)
|
|
hull2 = cv2.convexHull(c)
|
|
defects = cv2.convexityDefects(c,hull)
|
|
#print(str(defects.size) + " match")
|
|
cv2.drawContours(orig, (hull2), -1, (0, 0, 255), 8)
|
|
cv2.drawContours(orig, (approx), -1, (255, 0, 0), 6)
|
|
convexness = area_contour / cv2.contourArea(hull2)
|
|
#print(str(convexness) + " % fill")
|
|
#if not cv2.isContourConvex(approx):
|
|
#if cv2.matchShapes(hull, c, 1, 0.0) > 1:
|
|
if defects.size > 5 and (convexness < 0.9 or boxiness < 0.75):
|
|
objtype = "Screw"
|
|
iteml = larger(dimA, dimB)
|
|
#print("Screw Length (RAW): " + str(iteml))
|
|
iteml = sizeVexScrew(radius * 2 / pixelsPerMetric)
|
|
#print("Rounded Length: " + str(iteml))
|
|
else:
|
|
if itemhr == 0.3125:
|
|
objtype = "Standoff"
|
|
iteml = sizeStandoff(itemw)
|
|
|
|
if itemhr == 0.1875:
|
|
objtype = "Axle"
|
|
iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
|
|
|
|
|
|
#print(str(iteml))
|
|
# draw the object sizes on the image
|
|
if args2.show:
|
|
#cv2.putText(orig, "{:.5f}in".format(itemhr),
|
|
# (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
|
|
# 0.65, (255, 255, 255), 2)
|
|
cv2.putText(orig, str(objtype),
|
|
(int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.65, (255, 255, 255), 2)
|
|
if objtype == "Unknown":
|
|
cv2.putText(orig, "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh), # print axle length
|
|
(int(trbrX + 20), int(trbrY + 20)), cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.65, (255, 255, 255), 2)
|
|
if objtype == "Screw":
|
|
cv2.putText(orig, str(iteml) + "in thread", # print screw length
|
|
(int(trbrX + 20), int(trbrY + 20)), cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.65, (255, 255, 255), 2)
|
|
if objtype == "Standoff":
|
|
cv2.putText(orig, str(iteml) + "in", # print standoff length
|
|
(int(trbrX + 20), int(trbrY + 20)), cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.65, (255, 255, 255), 2)
|
|
if objtype == "Axle":
|
|
cv2.putText(orig, "{:.2f}in".format(iteml), # print axle length
|
|
(int(trbrX + 20), int(trbrY + 20)), cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.65, (255, 255, 255), 2)
|
|
|
|
# show the output image
|
|
cv2.imshow("Item Sorter", orig)
|
|
cv2.waitKey(500)
|
|
|
|
cv2.waitKey(0) |