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format code

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cascade
Cole Deck 5 years ago
parent 57abd697ba
commit 0e00ba7fca
1 changed files with 243 additions and 237 deletions
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474
main.py
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@ -13,75 +13,81 @@ itemh = 0
def midpoint(ptA, ptB): def midpoint(ptA, ptB):
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5) return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
def sizeVexScrew(iteml): def sizeVexScrew(iteml):
# Screw Sizing code # Screw Sizing code
# subtract screw head size to find thread length # subtract screw head size to find thread length
shead = 0.1 shead = 0.1
iteml -= shead iteml -= shead
#print("Thread Length: " + str(iteml)) #print("Thread Length: " + str(iteml))
iteml *= 8 iteml *= 8
iteml = round(iteml) iteml = round(iteml)
iteml /= 8 iteml /= 8
return iteml return iteml
def sizeStandoff(iteml): def sizeStandoff(iteml):
# Standoff Sizing code # Standoff Sizing code
iteml *= 2 iteml *= 2
iteml = round(iteml) iteml = round(iteml)
iteml /= 2 iteml /= 2
return iteml return iteml
def larger(a, b): def larger(a, b):
if a >= b: if a >= b:
return a return a
else: else:
return b return b
def smaller(a, b): def smaller(a, b):
if a < b: if a < b:
return a return a
else: else:
return b return b
def near(a, b, close): def near(a, b, close):
if abs(a-b) < close: if abs(a-b) < close:
return True return True
return False return False
def swap(a, b): def swap(a, b):
tmp = a tmp = a
a = b a = b
b = tmp b = tmp
# construct the argument parse and parse the arguments # construct the argument parse and parse the arguments
ap = argparse.ArgumentParser() ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, ap.add_argument("-i", "--image", required=True,
help="path to the input image") help="path to the input image")
ap.add_argument("-w", "--width", type=float, required=True, ap.add_argument("-w", "--width", type=float, required=True,
help="width of the left-most object in the image (in inches)") help="width of the left-most object in the image (in inches)")
ap.add_argument("-n", "--number", type=int, required=False, ap.add_argument("-n", "--number", type=int, required=False,
help="object # to measure (from left to right)") help="object # to measure (from left to right)")
ap.add_argument("-s", "--show", action="store_true", ap.add_argument("-s", "--show", action="store_true",
help="show on the screen") help="show on the screen")
args = vars(ap.parse_args()) args = vars(ap.parse_args())
args2 = ap.parse_args() args2 = ap.parse_args()
selected = 2 selected = 2
if type(args["number"]) == type(selected): if type(args["number"]) == type(selected):
selected = args["number"] selected = args["number"]
# load the image, convert it to grayscale, and blur it slightly # load the image, convert it to grayscale, and blur it slightly
image = cv2.imread(args["image"]) image = cv2.imread(args["image"])
#image = cv2.resize(image, (int(image.shape[1]*1), int(image.shape[0]*1))) #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) image = cv2.resize(image, (1000, int(
image.shape[0]/image.shape[1] * 1000)), interpolation=cv2.INTER_NEAREST)
if args2.show: if args2.show:
cv2.namedWindow("Item Sorter") cv2.namedWindow("Item Sorter")
cv2.imshow("Item Sorter", image) cv2.imshow("Item Sorter", image)
cv2.waitKey(0) cv2.waitKey(0)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0) gray = cv2.GaussianBlur(gray, (7, 7), 0)
@ -93,11 +99,11 @@ edged = cv2.dilate(edged, None, iterations=1)
#edged = cv2.erode(edged, None, iterations=1) #edged = cv2.erode(edged, None, iterations=1)
if args2.show: if args2.show:
cv2.imshow("Item Sorter", edged) cv2.imshow("Item Sorter", edged)
cv2.waitKey(0) cv2.waitKey(0)
# find contours in the edge map # find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts) cnts = imutils.grab_contours(cnts)
# sort the contours from left-to-right and initialize the # sort the contours from left-to-right and initialize the
@ -107,205 +113,205 @@ pixelsPerMetric = None
num = 0 num = 0
# Calibration loop # Calibration loop
for c in cnts: for c in cnts:
# if the contour is not sufficiently large, ignore it # if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < 100: if cv2.contourArea(c) < 100:
continue continue
# compute the rotated bounding box of the contour # compute the rotated bounding box of the contour
orig = image.copy() orig = image.copy()
box = cv2.minAreaRect(c) box = cv2.minAreaRect(c)
box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box) box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
box = np.array(box, dtype="int") box = np.array(box, dtype="int")
#box = perspective.order_points(box) #box = perspective.order_points(box)
(tl, tr, br, bl) = box (tl, tr, br, bl) = box
(tltrX, tltrY) = midpoint(tl, tr) (tltrX, tltrY) = midpoint(tl, tr)
(blbrX, blbrY) = midpoint(bl, br) (blbrX, blbrY) = midpoint(bl, br)
(tlblX, tlblY) = midpoint(tl, bl) (tlblX, tlblY) = midpoint(tl, bl)
(trbrX, trbrY) = midpoint(tr, br) (trbrX, trbrY) = midpoint(tr, br)
dA = np.linalg.norm(np.array((tltrX, tltrY, 0)) - np.array((blbrX, blbrY, 0))) dA = np.linalg.norm(np.array((tltrX, tltrY, 0)) -
dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) - np.array((trbrX, trbrY, 0))) np.array((blbrX, blbrY, 0)))
area_box = dA * dB dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) -
(x,y),radius = cv2.minEnclosingCircle(c) np.array((trbrX, trbrY, 0)))
area_contour = cv2.contourArea(c) area_box = dA * dB
area_circle = math.pi * pow(radius, 2) (x, y), radius = cv2.minEnclosingCircle(c)
boxiness = area_contour / area_box area_contour = cv2.contourArea(c)
circleness = area_contour / area_circle area_circle = math.pi * pow(radius, 2)
circular = False boxiness = area_contour / area_box
rectangular = False circleness = area_contour / area_circle
if boxiness > circleness: circular = False
rectangular = True rectangular = False
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2) if boxiness > circleness:
else: rectangular = True
circular = True cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
cv2.circle(orig,(int(x),int(y)),int(radius),(0,255,0),2) else:
mask = np.zeros(gray.shape,np.uint8) circular = True
cv2.drawContours(mask,[c],0,255,-1) cv2.circle(orig, (int(x), int(y)), int(radius), (0, 255, 0), 2)
#pixelpoints = np.transpose(np.nonzero(mask)) mask = np.zeros(gray.shape, np.uint8)
hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV) cv2.drawContours(mask, [c], 0, 255, -1)
mean_val = cv2.mean(hsv,mask = mask) #pixelpoints = np.transpose(np.nonzero(mask))
print(str(mean_val[0])) hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV)
#print(", " + str(mean_val[0]/mean_val[2])) mean_val = cv2.mean(hsv, mask=mask)
#print(", " + str(mean_val[2]/mean_val[1])) print(str(mean_val[0]))
if pixelsPerMetric is None and circular is True and near(mean_val[0], 16, 4.5): #print(", " + str(mean_val[0]/mean_val[2]))
# 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: #print(", " + str(mean_val[2]/mean_val[1]))
pixelsPerMetric = smaller(dA, dB) / args["width"] 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() orig = image.copy()
# loop over the contours individually # loop over the contours individually
for c in cnts: for c in cnts:
#orig = image.copy() #orig = image.copy()
num += 1 num += 1
# if the contour is not sufficiently large, ignore it # if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < 100 or pixelsPerMetric is None: if cv2.contourArea(c) < 100 or pixelsPerMetric is None:
continue continue
# compute the rotated bounding box of the contour # compute the rotated bounding box of the contour
box = cv2.minAreaRect(c)
box = cv2.minAreaRect(c) box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box) box = np.array(box, dtype="int")
box = np.array(box, dtype="int")
# order the points in the contour such that they appear
# order the points in the contour such that they appear # in top-left, top-right, bottom-right, and bottom-left
# in top-left, top-right, bottom-right, and bottom-left # order, then draw the outline of the rotated bounding
# order, then draw the outline of the rotated bounding # box
# box #box = perspective.order_points(box)
#box = perspective.order_points(box)
# loop over the original points and draw them
# loop over the original points and draw them # for (x, y) in box:
#for (x, y) in box: #cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
#cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
# unpack the ordered bounding box, then compute the midpoint
# unpack the ordered bounding box, then compute the midpoint # between the top-left and top-right coordinates, followed by
# between the top-left and top-right coordinates, followed by # the midpoint between bottom-left and bottom-right coordinates
# the midpoint between bottom-left and bottom-right coordinates (tl, tr, br, bl) = box
(tl, tr, br, bl) = box (tltrX, tltrY) = midpoint(tl, tr)
(tltrX, tltrY) = midpoint(tl, tr) (blbrX, blbrY) = midpoint(bl, br)
(blbrX, blbrY) = midpoint(bl, br)
# compute the midpoint between the top-left and top-right points,
# compute the midpoint between the top-left and top-right points, # followed by the midpoint between the top-right and bottom-right
# followed by the midpoint between the top-right and bottom-right (tlblX, tlblY) = midpoint(tl, bl)
(tlblX, tlblY) = midpoint(tl, bl) (trbrX, trbrY) = midpoint(tr, br)
(trbrX, trbrY) = midpoint(tr, br) # draw the midpoints on the image
# draw the midpoints on the image #cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
#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(blbrX), int(blbrY)), 5, (255, 0, 0), -1) #cv2.circle(orig, (int(tlblX), int(tlblY)), 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)
#cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
# draw lines between the midpoints
# draw lines between the midpoints # compute the Euclidean distance between the midpoints
# compute the Euclidean distance between the midpoints dA = np.linalg.norm(np.array((tltrX, tltrY, 0)) -
dA = np.linalg.norm(np.array((tltrX, tltrY, 0)) - np.array((blbrX, blbrY, 0))) np.array((blbrX, blbrY, 0)))
dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) - np.array((trbrX, trbrY, 0))) dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) -
np.array((trbrX, trbrY, 0)))
dimA = dA / pixelsPerMetric dimA = dA / pixelsPerMetric
dimB = dB / pixelsPerMetric dimB = dB / pixelsPerMetric
if num == selected or args2.show: if num == selected or args2.show:
area_box = dA * dB area_box = dA * dB
(x,y),radius = cv2.minEnclosingCircle(c) (x, y), radius = cv2.minEnclosingCircle(c)
area_contour = cv2.contourArea(c) area_contour = cv2.contourArea(c)
area_circle = math.pi * pow(radius, 2) area_circle = math.pi * pow(radius, 2)
boxiness = area_contour / area_box boxiness = area_contour / area_box
circleness = area_contour / area_circle circleness = area_contour / area_circle
circular = False circular = False
rectangular = False rectangular = False
if boxiness > circleness: if boxiness > circleness:
rectangular = True rectangular = True
#cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2) #cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
else: else:
circular = True circular = True
cv2.circle(orig,(int(x),int(y)),int(radius),(0,255,0),1) cv2.circle(orig, (int(x), int(y)), int(radius), (0, 255, 0), 1)
objtype = "Unknown"
objtype = "Unknown" itemw = larger(dimA, dimB)
itemw = larger(dimA, dimB) itemwr = itemw
itemwr = itemw itemwr *= 8
itemwr *= 8 itemwr = round(itemwr)
itemwr = round(itemwr) itemwr /= 8
itemwr /= 8
itemh = smaller(dimA, dimB)
itemh = smaller(dimA, dimB) itemhr = itemh
itemhr = itemh itemhr *= 16
itemhr *= 16 itemhr = round(itemhr)
itemhr = round(itemhr) itemhr /= 16
itemhr /= 16 if circular and itemwr == 0.75:
if circular and itemwr == 0.75: objtype = "Penny"
objtype = "Penny" iteml = 0
iteml = 0 else:
else: epsilon = 3 # 0.02*cv2.arcLength(c,True)
epsilon = 3#0.02*cv2.arcLength(c,True) # print(str(epsilon))
#print(str(epsilon)) approx = cv2.approxPolyDP(c, epsilon, True)
approx = cv2.approxPolyDP(c,epsilon,True) hull = cv2.convexHull(approx, returnPoints=False)
hull = cv2.convexHull(approx, returnPoints=False) hull2 = cv2.convexHull(c)
hull2 = cv2.convexHull(c) defects = cv2.convexityDefects(c, hull)
defects = cv2.convexityDefects(c,hull) #print(str(defects.size) + " match")
#print(str(defects.size) + " match") cv2.drawContours(orig, (hull2), -1, (0, 0, 255), 3)
cv2.drawContours(orig, (hull2), -1, (0, 0, 255), 3) cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3)
cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3) convexness = area_contour / cv2.contourArea(hull2)
convexness = area_contour / cv2.contourArea(hull2) #print(str(convexness) + " % fill")
#print(str(convexness) + " % fill") # if not cv2.isContourConvex(approx):
#if not cv2.isContourConvex(approx): # if cv2.matchShapes(hull, c, 1, 0.0) > 1:
#if cv2.matchShapes(hull, c, 1, 0.0) > 1: if defects.size > 5 and (convexness < 0.9 or boxiness < 0.75):
if defects.size > 5 and (convexness < 0.9 or boxiness < 0.75): objtype = "Screw"
objtype = "Screw" iteml = larger(dimA, dimB)
iteml = larger(dimA, dimB) #print("Screw Length (RAW): " + str(iteml))
#print("Screw Length (RAW): " + str(iteml)) iteml = sizeVexScrew(radius * 2 / pixelsPerMetric)
iteml = sizeVexScrew(radius * 2 / pixelsPerMetric) #print("Rounded Length: " + str(iteml))
#print("Rounded Length: " + str(iteml)) else:
else: if itemhr == 0.3125:
if itemhr == 0.3125: objtype = "Standoff"
objtype = "Standoff" iteml = sizeStandoff(itemw)
iteml = sizeStandoff(itemw)
if itemhr == 0.1875:
if itemhr == 0.1875: objtype = "Axle"
objtype = "Axle" iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
rows, cols = orig.shape[:2]
rows,cols = orig.shape[:2] [vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
[vx,vy,xx,yy] = cv2.fitLine(c, cv2.DIST_L2,0,0.01,0.01) lefty = int((-xx*vy/vx) + yy)
lefty = int((-xx*vy/vx) + yy) righty = int(((cols-xx)*vy/vx)+yy)
righty = int(((cols-xx)*vy/vx)+yy) # cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2)
#cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2) slope = (lefty - righty) / (1 - cols)
slope = (lefty - righty) / (1 - cols) angle = math.atan(slope)
angle = math.atan(slope) xpos = x - math.cos(angle) * radius
xpos = x - math.cos(angle) * radius ypos = y - math.sin(angle) * radius
ypos = y - math.sin(angle) * radius xpos2 = x + math.cos(angle) * radius
xpos2 = x + math.cos(angle) * radius ypos2 = y + math.sin(angle) * radius
ypos2 = y + math.sin(angle) * radius if xpos > xpos2:
if xpos > xpos2: swap(xpos, xpos2)
swap(xpos, xpos2) swap(ypos, ypos2)
swap(ypos, ypos2) if rectangular:
if rectangular: cv2.line(orig, (int(xpos), int(ypos)),
cv2.line(orig,(int(xpos),int(ypos)),(int(xpos2), int(ypos2)),(0,255,0),1) (int(xpos2), int(ypos2)), (0, 255, 0), 1)
#print(str(iteml)) # print(str(iteml))
# draw the object sizes on the image # draw the object sizes on the image
if args2.show: if args2.show:
#cv2.putText(orig, "{:.5f}in".format(itemhr), # cv2.putText(orig, "{:.5f}in".format(itemhr),
# (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX, # (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
# 0.65, (255, 255, 255), 2) # 0.65, (255, 255, 255), 2)
cv2.putText(orig, str(objtype), cv2.putText(orig, str(objtype),
(int(xpos2 + 10), int(ypos2 + 20)), cv2.FONT_HERSHEY_SIMPLEX, (int(xpos2 + 10), int(ypos2 + 20)
0.65, (255, 255, 255), 2) ), cv2.FONT_HERSHEY_SIMPLEX,
output = "" 0.65, (255, 255, 255), 2)
if objtype == "Unknown": output = ""
output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh) if objtype == "Unknown":
if objtype == "Screw" or objtype == "Standoff": output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
output = str(iteml) + "in" if objtype == "Screw" or objtype == "Standoff":
if objtype == "Axle": output = str(iteml) + "in"
output = "{:.2f}in".format(iteml) if objtype == "Axle":
cv2.putText(orig, output, # print data output = "{:.2f}in".format(iteml)
(int(xpos2 + 10), int(ypos2 + 40)), cv2.FONT_HERSHEY_SIMPLEX, cv2.putText(orig, output, # print data
0.65, (255, 255, 255), 2) (int(xpos2 + 10), int(ypos2 + 40)
), cv2.FONT_HERSHEY_SIMPLEX,
# show the output image 0.65, (255, 255, 255), 2)
cv2.imshow("Item Sorter", orig)
cv2.waitKey(25) # show the output image
cv2.imshow("Item Sorter", orig)
cv2.waitKey(25)
cv2.waitKey(0) cv2.waitKey(0)
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