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output item list and scan

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video
Cole Deck 5 years ago
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()) args = vars(ap.parse_args())
args2 = ap.parse_args()""" args2 = ap.parse_args()"""
def detect(calibration_width, img_file, show, quick): def detect(calibration_width, img_file, show, quick):
selected = 2 list = []
#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 = None image = None
print(str(type(img_file))) #print(str(type(img_file)))
if str(type(img_file)) == "<class 'numpy.ndarray'>": if str(type(img_file)) == "<class 'numpy.ndarray'>":
image = img_file.copy() image = img_file.copy()
else: else:
@ -91,7 +91,7 @@ def detect(calibration_width, img_file, show, quick):
#image = img_file.copy() #image = img_file.copy()
#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 = 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: if show and not quick:
cv2.namedWindow("Item Sorter") cv2.namedWindow("Item Sorter")
@ -232,139 +232,134 @@ def detect(calibration_width, img_file, show, quick):
dimA = dA / pixelsPerMetric dimA = dA / pixelsPerMetric
dimB = dB / pixelsPerMetric dimB = dB / pixelsPerMetric
if num == num or show: # Item detection
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), (255, 0, 0), 2) cv2.circle(orig, (int(x), int(y)), int(radius), (255, 0, 0), 2)
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)
itemhr = itemh
itemh = smaller(dimA, dimB) itemhr *= 16
itemhr = itemh itemhr = round(itemhr)
itemhr *= 16 itemhr /= 16
itemhr = round(itemhr) if circular and itemwr == 0.75:
itemhr /= 16 objtype = "Penny"
if circular and itemwr == 0.75: iteml = 0
objtype = "Penny" else:
iteml = 0 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: else:
if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03): if itemhr == 0.3125 and rectangular:
# Keps nut or spacer objtype = "Standoff"
objtype = "Spacer" iteml = sizeStandoff(itemw)
mask = np.zeros(gray.shape, np.uint8) if itemhr == 0.1875 and rectangular:
cv2.drawContours(mask, [c], 0, 255, -1) objtype = "Axle"
#pixelpoints = np.transpose(np.nonzero(mask)) iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV) rows, cols = orig.shape[:2]
mean_val = cv2.mean(hsv, mask=mask) [vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
#print(str(mean_val[0])) lefty = int((-xx*vy/vx) + yy)
if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5): righty = int(((cols-xx)*vy/vx)+yy)
objtype = "Keps Nut" # cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2)
if circular and near(radius / pixelsPerMetric, 0.23, 0.02): slope = (lefty - righty) / (1 - cols)
objtype = "Washer" angle = math.atan(slope)
epsilon = 3 # 0.02*cv2.arcLength(c,True) xpos = x - math.cos(angle) * radius
# print(str(epsilon)) ypos = y - math.sin(angle) * radius
approx = cv2.approxPolyDP(c, epsilon, True) xpos2 = x + math.cos(angle) * radius
hull = cv2.convexHull(approx, returnPoints=False) ypos2 = y + math.sin(angle) * radius
hull2 = cv2.convexHull(c) if xpos > xpos2:
defects = cv2.convexityDefects(c, hull) swap(xpos, xpos2)
#print(str(defects.size) + " match") swap(ypos, ypos2)
cv2.drawContours(orig, (hull2), -1, (0, 0, 255), 3) if rectangular:
cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3) cv2.line(orig, (int(xpos), int(ypos)),
convexness = area_contour / cv2.contourArea(hull2) (int(xpos2), int(ypos2)), (255, 127, 0), 2)
#print(str(convexness) + " % fill") # print(str(iteml))
# if not cv2.isContourConvex(approx): # draw the object sizes on the image
# if cv2.matchShapes(hull, c, 1, 0.0) > 1: # cv2.putText(orig, "{:.5f}in".format(itemhr),
if defects is not None and defects.size > 5 and (convexness < 0.9 or boxiness < 0.75) and rectangular: # (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
objtype = "Screw" # 0.65, (255, 255, 255), 2)
iteml = larger(dimA, dimB) if circular:
#print("Screw Length (RAW): " + str(iteml)) cv2.putText(orig, str(objtype),
iteml = sizeVexScrew(radius * 2 / pixelsPerMetric) (int(x - 25), int(y + radius + 20)
#print("Rounded Length: " + str(iteml)) ), cv2.FONT_HERSHEY_SIMPLEX,
else: 0.6, (50, 50, 220), 2)
if itemhr == 0.3125 and rectangular: else:
objtype = "Standoff" cv2.putText(orig, str(objtype),
iteml = sizeStandoff(itemw) (int(xpos2 + 10), int(ypos2 + 20)
), cv2.FONT_HERSHEY_SIMPLEX,
if itemhr == 0.1875 and rectangular: 0.6, (50, 50, 220), 2)
objtype = "Axle" output = ""
iteml = (radius * 2 / pixelsPerMetric + itemw) / 2 objname = objtype;
if objtype == "Unknown":
rows, cols = orig.shape[:2] output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
[vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01) if objtype == "Screw" or objtype == "Standoff":
lefty = int((-xx*vy/vx) + yy) output = str(iteml) + "in"
righty = int(((cols-xx)*vy/vx)+yy) objname += str(iteml)
# cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2) if objtype == "Axle":
slope = (lefty - righty) / (1 - cols) output = "{:.2f}in".format(iteml)
angle = math.atan(slope) objname += str(itemwr)
xpos = x - math.cos(angle) * radius #print(objname)
ypos = y - math.sin(angle) * radius list.append(objname)
xpos2 = x + math.cos(angle) * radius if circular:
ypos2 = y + math.sin(angle) * radius cv2.putText(orig, output, # print data
if xpos > xpos2: (int(x - 25), int(y + radius + 40)
swap(xpos, xpos2) ), cv2.FONT_HERSHEY_SIMPLEX,
swap(ypos, ypos2) 0.5, (50, 50, 220), 1)
if rectangular: else:
cv2.line(orig, (int(xpos), int(ypos)), cv2.putText(orig, output, # print data
(int(xpos2), int(ypos2)), (255, 127, 0), 2) (int(xpos2 + 10), int(ypos2 + 40)
# print(str(iteml)) ), cv2.FONT_HERSHEY_SIMPLEX,
# draw the object sizes on the image 0.5, (50, 50, 220), 1)
if show or True: # show the output image
# cv2.putText(orig, "{:.5f}in".format(itemhr), if show and not quick:
# (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX, cv2.imshow("Item Sorter", orig)
# 0.65, (255, 255, 255), 2) #cv2.waitKey(1)
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 quick: if quick:
return orig return (list, orig)
else: else:
cv2.waitKey(0) cv2.waitKey(0)

19
run_detect.py
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@ -5,14 +5,28 @@ from imutils.video import FPS
calibration_width = 0.75 calibration_width = 0.75
image = "img7.jpg" 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", "img3.jpg", "img4.jpg", "img5.jpg", "img6.jpg", "img7.jpg", "img8.jpg")
#images = ("img.jpg", "img2.jpg")
video = False video = False
def go(): def go():
for file in images: 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: if not video:
elapsed_time = timeit.timeit(go, number=1)/1 elapsed_time = timeit.timeit(go, number=1)/1
print(elapsed_time) print(elapsed_time)
else : else :
#tcp capture = cv2.VideoCapture('udpsrc port=5001 ! gdpdepay ! rtph264depay ! avdec_h264 ! videoconvert ! videorate ! video/x-raw,framerate=5/1 ! appsink', cv2.CAP_GSTREAMER) #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) 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') #print('frame')
if x > 1: if x > 1:
ret,frame = capture.retrieve() 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 x = 0
if cv2.waitKey(1)&0xFF == ord('q'): if cv2.waitKey(1)&0xFF == ord('q'):
break break

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