output item list and scan
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Cole Deck
parent
129ad2a762
commit
e21628b608
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265
detect.py
265
detect.py
@ -76,13 +76,13 @@ ap.add_argument("-n", "--number", type=int, required=False,
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args = vars(ap.parse_args())
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args2 = ap.parse_args()"""
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def detect(calibration_width, img_file, show, quick):
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selected = 2
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list = []
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#if type(args["number"]) == type(selected):
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# selected = args["number"]
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# load the image, convert it to grayscale, and blur it slightly
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image = None
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print(str(type(img_file)))
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#print(str(type(img_file)))
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if str(type(img_file)) == "<class 'numpy.ndarray'>":
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image = img_file.copy()
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else:
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@ -91,7 +91,7 @@ def detect(calibration_width, img_file, show, quick):
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#image = img_file.copy()
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#image = cv2.resize(image, (int(image.shape[1]*1), int(image.shape[0]*1)))
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image = cv2.resize(image, (1000, int(
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image.shape[0]/image.shape[1] * 1000)), interpolation=cv2.INTER_NEAREST)
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image.shape[0]/image.shape[1] * 1000)), interpolation=cv2.INTER_NEAREST)
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if show and not quick:
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cv2.namedWindow("Item Sorter")
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@ -232,139 +232,134 @@ def detect(calibration_width, img_file, show, quick):
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dimA = dA / pixelsPerMetric
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dimB = dB / pixelsPerMetric
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if num == num or show:
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area_box = dA * dB
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(x, y), radius = cv2.minEnclosingCircle(c)
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area_contour = cv2.contourArea(c)
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area_circle = math.pi * pow(radius, 2)
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boxiness = area_contour / area_box
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circleness = area_contour / area_circle
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circular = False
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rectangular = False
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if boxiness > circleness:
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rectangular = True
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#cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
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# Item detection
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area_box = dA * dB
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(x, y), radius = cv2.minEnclosingCircle(c)
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area_contour = cv2.contourArea(c)
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area_circle = math.pi * pow(radius, 2)
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boxiness = area_contour / area_box
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circleness = area_contour / area_circle
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circular = False
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rectangular = False
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if boxiness > circleness:
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rectangular = True
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#cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
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else:
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circular = True
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cv2.circle(orig, (int(x), int(y)), int(radius), (255, 0, 0), 2)
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objtype = "Unknown"
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itemw = larger(dimA, dimB)
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itemwr = itemw
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itemwr *= 8
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itemwr = round(itemwr)
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itemwr /= 8
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itemh = smaller(dimA, dimB)
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itemhr = itemh
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itemhr *= 16
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itemhr = round(itemhr)
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itemhr /= 16
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if circular and itemwr == 0.75:
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objtype = "Penny"
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iteml = 0
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else:
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if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03):
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# Keps nut or spacer
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objtype = "Spacer"
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mask = np.zeros(gray.shape, np.uint8)
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cv2.drawContours(mask, [c], 0, 255, -1)
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#pixelpoints = np.transpose(np.nonzero(mask))
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hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV)
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mean_val = cv2.mean(hsv, mask=mask)
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#print(str(mean_val[0]))
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if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5):
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objtype = "Keps Nut"
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if circular and near(radius / pixelsPerMetric, 0.23, 0.02):
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objtype = "Washer"
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epsilon = 3 # 0.02*cv2.arcLength(c,True)
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# print(str(epsilon))
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approx = cv2.approxPolyDP(c, epsilon, True)
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hull = cv2.convexHull(approx, returnPoints=False)
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hull2 = cv2.convexHull(c)
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defects = cv2.convexityDefects(c, hull)
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#print(str(defects.size) + " match")
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cv2.drawContours(orig, (hull2), -1, (0, 0, 255), 3)
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cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3)
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convexness = area_contour / cv2.contourArea(hull2)
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#print(str(convexness) + " % fill")
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# if not cv2.isContourConvex(approx):
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# if cv2.matchShapes(hull, c, 1, 0.0) > 1:
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if defects is not None and defects.size > 5 and (convexness < 0.9 or boxiness < 0.75) and rectangular:
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objtype = "Screw"
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iteml = larger(dimA, dimB)
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#print("Screw Length (RAW): " + str(iteml))
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iteml = sizeVexScrew(radius * 2 / pixelsPerMetric)
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#print("Rounded Length: " + str(iteml))
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else:
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circular = True
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cv2.circle(orig, (int(x), int(y)), int(radius), (255, 0, 0), 2)
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objtype = "Unknown"
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itemw = larger(dimA, dimB)
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itemwr = itemw
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itemwr *= 8
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itemwr = round(itemwr)
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itemwr /= 8
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itemh = smaller(dimA, dimB)
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itemhr = itemh
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itemhr *= 16
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itemhr = round(itemhr)
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itemhr /= 16
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if circular and itemwr == 0.75:
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objtype = "Penny"
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iteml = 0
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else:
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if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03):
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# Keps nut or spacer
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objtype = "Spacer"
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mask = np.zeros(gray.shape, np.uint8)
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cv2.drawContours(mask, [c], 0, 255, -1)
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#pixelpoints = np.transpose(np.nonzero(mask))
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hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV)
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mean_val = cv2.mean(hsv, mask=mask)
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#print(str(mean_val[0]))
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if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5):
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objtype = "Keps Nut"
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if circular and near(radius / pixelsPerMetric, 0.23, 0.02):
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objtype = "Washer"
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epsilon = 3 # 0.02*cv2.arcLength(c,True)
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# print(str(epsilon))
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approx = cv2.approxPolyDP(c, epsilon, True)
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hull = cv2.convexHull(approx, returnPoints=False)
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hull2 = cv2.convexHull(c)
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defects = cv2.convexityDefects(c, hull)
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#print(str(defects.size) + " match")
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cv2.drawContours(orig, (hull2), -1, (0, 0, 255), 3)
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cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3)
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convexness = area_contour / cv2.contourArea(hull2)
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#print(str(convexness) + " % fill")
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# if not cv2.isContourConvex(approx):
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# if cv2.matchShapes(hull, c, 1, 0.0) > 1:
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if defects is not None and defects.size > 5 and (convexness < 0.9 or boxiness < 0.75) and rectangular:
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objtype = "Screw"
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iteml = larger(dimA, dimB)
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#print("Screw Length (RAW): " + str(iteml))
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iteml = sizeVexScrew(radius * 2 / pixelsPerMetric)
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#print("Rounded Length: " + str(iteml))
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else:
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if itemhr == 0.3125 and rectangular:
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objtype = "Standoff"
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iteml = sizeStandoff(itemw)
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if itemhr == 0.1875 and rectangular:
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objtype = "Axle"
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iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
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rows, cols = orig.shape[:2]
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[vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
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lefty = int((-xx*vy/vx) + yy)
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righty = int(((cols-xx)*vy/vx)+yy)
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# cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2)
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slope = (lefty - righty) / (1 - cols)
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angle = math.atan(slope)
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xpos = x - math.cos(angle) * radius
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ypos = y - math.sin(angle) * radius
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xpos2 = x + math.cos(angle) * radius
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ypos2 = y + math.sin(angle) * radius
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if xpos > xpos2:
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swap(xpos, xpos2)
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swap(ypos, ypos2)
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if rectangular:
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cv2.line(orig, (int(xpos), int(ypos)),
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(int(xpos2), int(ypos2)), (255, 127, 0), 2)
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# print(str(iteml))
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# draw the object sizes on the image
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if show or True:
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# cv2.putText(orig, "{:.5f}in".format(itemhr),
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# (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
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# 0.65, (255, 255, 255), 2)
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if circular:
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cv2.putText(orig, str(objtype),
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(int(x - 25), int(y + radius + 20)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.6, (50, 50, 220), 2)
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else:
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cv2.putText(orig, str(objtype),
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(int(xpos2 + 10), int(ypos2 + 20)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.6, (50, 50, 220), 2)
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output = ""
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objname = objtype;
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if objtype == "Unknown":
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output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
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if objtype == "Screw" or objtype == "Standoff":
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output = str(iteml) + "in"
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objname += str(iteml)
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if objtype == "Axle":
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output = "{:.2f}in".format(iteml)
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objname += str(itemwr)
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print(objname)
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if circular:
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cv2.putText(orig, output, # print data
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(int(x - 25), int(y + radius + 40)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.5, (50, 50, 220), 1)
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else:
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cv2.putText(orig, output, # print data
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(int(xpos2 + 10), int(ypos2 + 40)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.5, (50, 50, 220), 1)
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# show the output image
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if show:
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cv2.imshow("Item Sorter", orig)
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#cv2.waitKey(1)
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if itemhr == 0.3125 and rectangular:
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objtype = "Standoff"
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iteml = sizeStandoff(itemw)
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if itemhr == 0.1875 and rectangular:
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objtype = "Axle"
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iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
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rows, cols = orig.shape[:2]
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[vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
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lefty = int((-xx*vy/vx) + yy)
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righty = int(((cols-xx)*vy/vx)+yy)
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# cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2)
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slope = (lefty - righty) / (1 - cols)
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angle = math.atan(slope)
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xpos = x - math.cos(angle) * radius
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ypos = y - math.sin(angle) * radius
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xpos2 = x + math.cos(angle) * radius
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ypos2 = y + math.sin(angle) * radius
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if xpos > xpos2:
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swap(xpos, xpos2)
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swap(ypos, ypos2)
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if rectangular:
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cv2.line(orig, (int(xpos), int(ypos)),
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(int(xpos2), int(ypos2)), (255, 127, 0), 2)
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# print(str(iteml))
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# draw the object sizes on the image
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# cv2.putText(orig, "{:.5f}in".format(itemhr),
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# (int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
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# 0.65, (255, 255, 255), 2)
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if circular:
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cv2.putText(orig, str(objtype),
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(int(x - 25), int(y + radius + 20)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.6, (50, 50, 220), 2)
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else:
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cv2.putText(orig, str(objtype),
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(int(xpos2 + 10), int(ypos2 + 20)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.6, (50, 50, 220), 2)
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output = ""
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objname = objtype;
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if objtype == "Unknown":
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output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
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if objtype == "Screw" or objtype == "Standoff":
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output = str(iteml) + "in"
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objname += str(iteml)
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if objtype == "Axle":
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output = "{:.2f}in".format(iteml)
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objname += str(itemwr)
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#print(objname)
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list.append(objname)
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if circular:
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cv2.putText(orig, output, # print data
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(int(x - 25), int(y + radius + 40)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.5, (50, 50, 220), 1)
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else:
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cv2.putText(orig, output, # print data
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(int(xpos2 + 10), int(ypos2 + 40)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.5, (50, 50, 220), 1)
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# show the output image
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if show and not quick:
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cv2.imshow("Item Sorter", orig)
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#cv2.waitKey(1)
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if quick:
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return orig
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return (list, orig)
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else:
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cv2.waitKey(0)
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@ -5,14 +5,28 @@ from imutils.video import FPS
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calibration_width = 0.75
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image = "img7.jpg"
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images = ("img.jpg", "img2.jpg", "img3.jpg", "img4.jpg", "img5.jpg", "img6.jpg", "img7.jpg", "img8.jpg")
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#images = ("img.jpg", "img2.jpg")
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video = False
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def go():
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for file in images:
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detect.detect(calibration_width, file, True, False)
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items,output = detect.detect(calibration_width, file, True, True)
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print(str(items))
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if "Penny" in items:
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items.remove("Penny")
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itema = items[0]
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valid = True
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for item in items:
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if item != itema:
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print("Too many items!")
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valid = False
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break
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if valid:
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print("Found " + itema)
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if not video:
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elapsed_time = timeit.timeit(go, number=1)/1
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print(elapsed_time)
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else :
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#tcp capture = cv2.VideoCapture('udpsrc port=5001 ! gdpdepay ! rtph264depay ! avdec_h264 ! videoconvert ! videorate ! video/x-raw,framerate=5/1 ! appsink', cv2.CAP_GSTREAMER)
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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)
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@ -32,7 +46,8 @@ else :
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#print('frame')
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if x > 1:
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ret,frame = capture.retrieve()
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cv2.imshow('Item Sorter', detect.detect(calibration_width, frame, True, True))
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list,output = detect.detect(calibration_width, frame, True, True)
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cv2.imshow('Item Sorter', output)
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x = 0
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if cv2.waitKey(1)&0xFF == ord('q'):
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break
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