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output item list and scan

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Cole Deck
2019-12-13 10:57:02 -06:00
parent 129ad2a762
commit e21628b608
4 changed files with 147 additions and 137 deletions
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__pycache__/detect.cpython-38.pyc
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detect.cpython-38-x86_64-linux-gnu.so
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265
detect.py
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@@ -76,13 +76,13 @@ ap.add_argument("-n", "--number", type=int, required=False,
args = vars(ap.parse_args())
args2 = ap.parse_args()"""
def detect(calibration_width, img_file, show, quick):
selected = 2
list = []
#if type(args["number"]) == type(selected):
# selected = args["number"]
# load the image, convert it to grayscale, and blur it slightly
image = None
print(str(type(img_file)))
#print(str(type(img_file)))
if str(type(img_file)) == "<class 'numpy.ndarray'>":
image = img_file.copy()
else:
@@ -91,7 +91,7 @@ def detect(calibration_width, img_file, show, quick):
#image = img_file.copy()
#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.shape[0]/image.shape[1] * 1000)), interpolation=cv2.INTER_NEAREST)
if show and not quick:
cv2.namedWindow("Item Sorter")
@@ -232,139 +232,134 @@ def detect(calibration_width, img_file, show, quick):
dimA = dA / pixelsPerMetric
dimB = dB / pixelsPerMetric
if num == num or 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)
# Item detection
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), (255, 0, 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:
if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03):
# Keps nut or spacer
objtype = "Spacer"
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]))
if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5):
objtype = "Keps Nut"
if circular and near(radius / pixelsPerMetric, 0.23, 0.02):
objtype = "Washer"
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), 3)
cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3)
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 is not None and defects.size > 5 and (convexness < 0.9 or boxiness < 0.75) and rectangular:
objtype = "Screw"
iteml = larger(dimA, dimB)
#print("Screw Length (RAW): " + str(iteml))
iteml = sizeVexScrew(radius * 2 / pixelsPerMetric)
#print("Rounded Length: " + str(iteml))
else:
circular = True
cv2.circle(orig, (int(x), int(y)), int(radius), (255, 0, 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:
if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03):
# Keps nut or spacer
objtype = "Spacer"
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]))
if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5):
objtype = "Keps Nut"
if circular and near(radius / pixelsPerMetric, 0.23, 0.02):
objtype = "Washer"
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), 3)
cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3)
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 is not None and defects.size > 5 and (convexness < 0.9 or boxiness < 0.75) and rectangular:
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 and rectangular:
objtype = "Standoff"
iteml = sizeStandoff(itemw)
if itemhr == 0.1875 and rectangular:
objtype = "Axle"
iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
rows, cols = orig.shape[:2]
[vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
lefty = int((-xx*vy/vx) + yy)
righty = int(((cols-xx)*vy/vx)+yy)
# cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2)
slope = (lefty - righty) / (1 - cols)
angle = math.atan(slope)
xpos = x - math.cos(angle) * radius
ypos = y - math.sin(angle) * radius
xpos2 = x + math.cos(angle) * radius
ypos2 = y + math.sin(angle) * radius
if xpos > xpos2:
swap(xpos, xpos2)
swap(ypos, ypos2)
if rectangular:
cv2.line(orig, (int(xpos), int(ypos)),
(int(xpos2), int(ypos2)), (255, 127, 0), 2)
# print(str(iteml))
# draw the object sizes on the image
if show or True:
# cv2.putText(orig, "{:.5f}in".format(itemhr),
# (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
# 0.65, (255, 255, 255), 2)
if circular:
cv2.putText(orig, str(objtype),
(int(x - 25), int(y + radius + 20)
), cv2.FONT_HERSHEY_SIMPLEX,
0.6, (50, 50, 220), 2)
else:
cv2.putText(orig, str(objtype),
(int(xpos2 + 10), int(ypos2 + 20)
), cv2.FONT_HERSHEY_SIMPLEX,
0.6, (50, 50, 220), 2)
output = ""
objname = objtype;
if objtype == "Unknown":
output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
if objtype == "Screw" or objtype == "Standoff":
output = str(iteml) + "in"
objname += str(iteml)
if objtype == "Axle":
output = "{:.2f}in".format(iteml)
objname += str(itemwr)
print(objname)
if circular:
cv2.putText(orig, output, # print data
(int(x - 25), int(y + radius + 40)
), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (50, 50, 220), 1)
else:
cv2.putText(orig, output, # print data
(int(xpos2 + 10), int(ypos2 + 40)
), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (50, 50, 220), 1)
# show the output image
if show:
cv2.imshow("Item Sorter", orig)
#cv2.waitKey(1)
if itemhr == 0.3125 and rectangular:
objtype = "Standoff"
iteml = sizeStandoff(itemw)
if itemhr == 0.1875 and rectangular:
objtype = "Axle"
iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
rows, cols = orig.shape[:2]
[vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
lefty = int((-xx*vy/vx) + yy)
righty = int(((cols-xx)*vy/vx)+yy)
# cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2)
slope = (lefty - righty) / (1 - cols)
angle = math.atan(slope)
xpos = x - math.cos(angle) * radius
ypos = y - math.sin(angle) * radius
xpos2 = x + math.cos(angle) * radius
ypos2 = y + math.sin(angle) * radius
if xpos > xpos2:
swap(xpos, xpos2)
swap(ypos, ypos2)
if rectangular:
cv2.line(orig, (int(xpos), int(ypos)),
(int(xpos2), int(ypos2)), (255, 127, 0), 2)
# print(str(iteml))
# draw the object sizes on the image
# cv2.putText(orig, "{:.5f}in".format(itemhr),
# (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
# 0.65, (255, 255, 255), 2)
if circular:
cv2.putText(orig, str(objtype),
(int(x - 25), int(y + radius + 20)
), cv2.FONT_HERSHEY_SIMPLEX,
0.6, (50, 50, 220), 2)
else:
cv2.putText(orig, str(objtype),
(int(xpos2 + 10), int(ypos2 + 20)
), cv2.FONT_HERSHEY_SIMPLEX,
0.6, (50, 50, 220), 2)
output = ""
objname = objtype;
if objtype == "Unknown":
output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
if objtype == "Screw" or objtype == "Standoff":
output = str(iteml) + "in"
objname += str(iteml)
if objtype == "Axle":
output = "{:.2f}in".format(iteml)
objname += str(itemwr)
#print(objname)
list.append(objname)
if circular:
cv2.putText(orig, output, # print data
(int(x - 25), int(y + radius + 40)
), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (50, 50, 220), 1)
else:
cv2.putText(orig, output, # print data
(int(xpos2 + 10), int(ypos2 + 40)
), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (50, 50, 220), 1)
# show the output image
if show and not quick:
cv2.imshow("Item Sorter", orig)
#cv2.waitKey(1)
if quick:
return orig
return (list, orig)
else:
cv2.waitKey(0)

19
run_detect.py
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@@ -5,14 +5,28 @@ from imutils.video import FPS
calibration_width = 0.75
image = "img7.jpg"
images = ("img.jpg", "img2.jpg", "img3.jpg", "img4.jpg", "img5.jpg", "img6.jpg", "img7.jpg", "img8.jpg")
#images = ("img.jpg", "img2.jpg")
video = False
def go():
for file in images:
detect.detect(calibration_width, file, True, False)
items,output = detect.detect(calibration_width, file, True, True)
print(str(items))
if "Penny" in items:
items.remove("Penny")
itema = items[0]
valid = True
for item in items:
if item != itema:
print("Too many items!")
valid = False
break
if valid:
print("Found " + itema)
if not video:
elapsed_time = timeit.timeit(go, number=1)/1
print(elapsed_time)
else :
#tcp capture = cv2.VideoCapture('udpsrc port=5001 ! gdpdepay ! rtph264depay ! avdec_h264 ! videoconvert ! videorate ! video/x-raw,framerate=5/1 ! appsink', cv2.CAP_GSTREAMER)
capture = cv2.VideoCapture('udpsrc port=9000 caps="application/x-rtp, media=(string)video, clock-rate=(int)90000, encoding-name=(string)H264" ! rtph264depay ! avdec_h264 ! videoconvert ! appsink sync=false', cv2.CAP_GSTREAMER)
@@ -32,7 +46,8 @@ else :
#print('frame')
if x > 1:
ret,frame = capture.retrieve()
cv2.imshow('Item Sorter', detect.detect(calibration_width, frame, True, True))
list,output = detect.detect(calibration_width, frame, True, True)
cv2.imshow('Item Sorter', output)
x = 0
if cv2.waitKey(1)&0xFF == ord('q'):
break