Use the largest item instead of quitting when there is more than one

camera.service

@@ -0,0 +1,13 @@
+[Unit]
+Description=UDP Camera Stream
+After=network.target auditd.service media-writable.mount 
+Requires=media-writable.mount
+
+[Service]
+ExecStart=/media/writable/run.sh
+Restart=always
+Type=idle
+User=pi
+
+[Install]
+WantedBy=multi-user.target

client.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=Python Sorting Client
+After=network.target auditd.service media-writable.mount 
+Requires=media-writable.mount
+
+[Service]
+ExecStart=/media/writable/item-sort/pi_client.py
+Restart=always
+Type=idle
+User=root
+StartLimitIntervalSec=0
+RestartSec=5
+WorkingDirectory=/media/writable/item-sort
+[Install]
+WantedBy=multi-user.target

compile.py

@@ -0,0 +1,13 @@
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+ext_modules = [
+    Extension("detect",  ["detect.py"]),
+    #Extension("mymodule2",  ["mymodule2.py"]),
+    #   ... all your modules that need be compiled ...
+    ]
+setup(
+    name = 'Item Sorter',
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = ext_modules
+)

control_motor.py

@@ -0,0 +1,101 @@
+import math
+import serial
+import RPi.GPIO as gpio
+import time
+
+ser = serial.Serial('/dev/ttyUSB0', 115200)
+gpio.setmode(gpio.BCM)
+gpio.setup(13, gpio.OUT)
+pwm = gpio.PWM(13, 100)
+pwm.start(13)
+verbose = True
+
+print("[ INFO ] Initializing Grbl...")
+ser.write(b'\r\n\r\n')
+time.sleep(2)
+ser.write(b'$RST=#\n')
+time.sleep(1)
+ser.flushInput()
+time.sleep(0.25)
+ser.write(b'$X\n')
+print("[ INFO ] Grbl is ready.")
+
+def goToBin(bin):
+    print("[ INFO ] Delivering item to bin: " + str(bin))
+    adjustedBin = math.floor(bin / 2)
+    if adjustedBin > 11:
+        print("[ INFO ] All bins full! Using overflow bin.")
+        bin = 0;
+        adjustedBin = 0;
+    distance = adjustedBin * 18
+    delay = 0.5 + 0.93 * adjustedBin
+    command = '$J=X-'
+    command += str(distance)
+    command += ' F2000'
+    print("[ INFO ] Sending command to Grbl: " + command)
+    command += '\n'
+    time.sleep(0.25)
+    ser.write(command.encode('utf-8'))
+    # s.write("$C\n")
+    while True:
+        grbl_out = str(ser.readline().strip()) # Wait for grbl response with carriage return
+        print(grbl_out)
+        if int(grbl_out.find('error')) >= 0 :
+            print("[ EXIT ] Grbl reported an error.")
+            quit()
+        elif int(grbl_out.find('ok')) >= 0 :
+            if verbose: print('[ INFO ] Grbl message: ',grbl_out)
+            break
+    print("[ INFO ] Waiting for " + str(delay) + " seconds.")
+    time.sleep(delay)
+    if bin % 2 == 0: # tilt to left
+        print("[ INFO ] Titling motor to left side.")
+        pwm.ChangeDutyCycle(5)
+        time.sleep(1)
+        pwm.ChangeDutyCycle(14)
+    else:
+        print("[ INFO ] Titling motor to right side.")
+        pwm.ChangeDutyCycle(25)
+        time.sleep(1)
+        pwm.ChangeDutyCycle(14)
+    time.sleep(1)
+    print("[ INFO ] Sending command to Grbl: G0 X0")
+    ser.write(b'$j=X0 F2000\n')
+    while True:
+        grbl_out = str(ser.readline().strip()) # Wait for grbl response with carriage return
+        if int(grbl_out.find('error')) >= 0 :
+            print("[ EXIT ] Grbl reported an error.")
+            quit()
+        elif int(grbl_out.find('ok')) >= 0 :
+            if verbose: print('[ INFO ] Grbl message: ',grbl_out)
+            break
+    print("[ INFO ] Waiting for " + str(delay) + " seconds.")
+    time.sleep(delay)
+def stopInput():
+    command = '!'
+    command += '\n'
+    ser.write(command.encode('utf-8'))
+    print(command)
+    #command2 = str(0x85)
+    #ser.write(command2.encode('utf-8'))
+    #while True:
+    #    grbl_out = str(ser.readline().strip()) # Wait for grbl response with carriage return
+    #    if int(grbl_out.find('error')) >= 0 :
+    #        print("[ EXIT ] Grbl reported an error.")
+    #        quit()
+    #    elif int(grbl_out.find('ok')) >= 0 :
+    #        if verbose: print('[ INFO ] Grbl message: ',grbl_out)
+    #        break
+def startInput():
+    ser.write(b'$J=G91 Y-5000 F400\n')
+    print("intake")
+    #x = 0
+    #while True and x < 10:
+        #x += 1
+        #grbl_out = str(ser.readline().strip()) # Wait for grbl response with carriage return
+        #if int(grbl_out.find('error')) >= 0 :
+        #    print("[ EXIT ] Grbl reported an error.")
+        #    quit()
+        #elif int(grbl_out.find('ok')) >= 0 :
+        #    if verbose: print('[ INFO ] Grbl message: ',grbl_out)
+        #    break

control_pixel.py

@@ -0,0 +1,7 @@
+import board
+import neopixel
+pixels = neopixel.NeoPixel(board.D18, 23)
+def ledOff():
+        pixels.fill((0,0,0))
+def ledOn():
+        pixels.fill((2,2,2))

detect.py

@@ -0,0 +1,387 @@
+# 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
+
+
+def swap(a, b):
+    tmp = a
+    a = b
+    b = tmp
+"""
+# 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("-c", "--cascade", required=True,
+#                help="path to the cascade")
+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()"""
+def detect(calibration_width, img_file, show, quick):
+    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)))
+    if str(type(img_file)) == "<class 'numpy.ndarray'>":
+        image = img_file.copy()
+    else:
+        image = cv2.imread(img_file)
+    
+    #image = img_file.copy()
+    image = cv2.resize(image, (math.floor(image.shape[1]*0.5), math.floor(image.shape[0]*0.5)))
+    #image = cv2.resize(image, (1000, int(image.shape[0]/image.shape[1] * 1000)), interpolation=cv2.INTER_NEAREST)
+
+    if show and not quick:
+        cv2.namedWindow("Item Sorter")
+        cv2.imshow("Item Sorter", image)
+        cv2.waitKey(0)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gray = cv2.GaussianBlur(gray, (7, 7), 0)
+    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=2)
+    edged = cv2.erode(edged, None, iterations=2)
+    edged = cv2.dilate(edged, None, iterations=1)
+    edged = cv2.erode(edged, None, iterations=1)
+    edged = cv2.dilate(edged, None, iterations=2)
+    #edged = cv2.erode(edged, None, iterations=1)
+    #edged = cv2.dilate(edged, None, iterations=1)
+    if show and not quick:
+        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)
+        # xpos,ypos,w,h = cv2.boundingRect(c)
+        # crop_img = orig[ypos:ypos+h, xpos:xpos+w]
+        # cv2.imwrite("object_images/IMG_" + str(w*h) + ".jpg", crop_img) # create training images
+        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), 3)
+            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) / calibration_width
+            continue
+            """
+    pixelsPerMetric = 25
+    orig = image.copy()
+    objtype = "Object"
+    objname = ""
+    c = None
+    # loop over the contours individually
+    if len(cnts) == 0:
+        return ((),edged)
+    area = cv2.contourArea(cnts[0])
+    if area < 400:
+        area = 0
+    for contour in cnts:
+        if cv2.contourArea(contour) >= area and cv2.contourArea(contour) > 400:
+            area = cv2.contourArea(contour)
+            c = contour
+    if c is not None:
+        #orig = image.copy()
+        num += 1
+        # if the contour is not sufficiently large, ignore it
+        #pixelsPerMetric = 75
+        #if cv2.contourArea(c) < 300 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")
+        # 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-right and bottom-right
+        (tlblX, tlblY) = midpoint(tl, bl)
+        (trbrX, trbrY) = midpoint(tr, br)
+    # 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
+
+        # 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 = "Object"
+        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, 0.4, 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)
+                mean_rgb = cv2.mean(orig, mask=mask)
+                if near(mean_rgb[2], 59, 3) and near(mean_val[1], 85, 5): #and near(mean_val[2], 78, 5):
+                    objtype = "Keps Nut"
+                print(str(mean_rgb[2]) + objtype + str(mean_val[1]))
+            elif circular and near(radius / pixelsPerMetric, 0.23, 0.02):
+                objtype = "Washer"
+            #print(str(radius * 2 / pixelsPerMetric) + objtype)
+            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
+        # cv2.putText(orig, "{:.5f}in".format(itemhr),
+        #	(int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
+        #	0.65, (255, 255, 255), 2)
+        if objtype != "Penny":
+            objtype = magicSort(c)
+        if objtype == "Object":
+            objtype = magicSort(c)
+            output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
+        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 == "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 not quick:
+        cv2.waitKey(0)
+    return (list, edged)
+        
+def magicSort(contour):
+    moments = cv2.moments(contour)
+    humoments = cv2.HuMoments(moments)
+    #humoments[6] = abs(humoments[6]) #it's possible for the last number to change sign if item is mirrored
+    #magicNumber1 = 0
+    #magicNumber2 = 0
+    name = "Object"
+    for i in range(0,7):
+        if humoments[i] == 0:
+            humoments[i] = 0.1;
+        humoments[i] = -1 * math.copysign(1.0, humoments[i]) * math.log10(abs(humoments[i]))
+        if i > 1:
+            humoments[i] = int(round(humoments[i][0] / 8) * 8)
+            if i != 4 and i != 6:
+                name += ", " + str(abs(int(humoments[i][0])))
+            #magicNumber1 += abs(humoments[i][0])
+        else:
+            humoments[i] = int(round(humoments[i][0] * 4) * 16)
+            name += ", " + str(abs(int(humoments[i][0])))
+            #magicNumber2 += abs(humoments[i][0])
+        #magicNumber += humoments[i][0]           
+    #print(str(humoments))
+    #print(magicNumber)
+    #name = "Unknown: " + str(int(magicNumber1)) + ", " + str(int(magicNumber2))
+    #print(name)
+    return name

main.py

@@ -1,320 +1,2 @@
-# 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
-
-
-def swap(a, b):
-    tmp = a
-    a = b
-    b = tmp
-
-
-# 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.namedWindow("Item Sorter")
-    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)
-    # xpos,ypos,w,h = cv2.boundingRect(c)
-    # crop_img = orig[ypos:ypos+h, xpos:xpos+w]
-    # cv2.imwrite("object_images/IMG_" + str(w*h) + ".jpg", crop_img) # create training images
-    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-right 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), 1)
-
-        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), 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.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
-
-        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)), (0, 255, 0), 1)
-        # 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(xpos2 + 10), int(ypos2 + 20)
-                         ), cv2.FONT_HERSHEY_SIMPLEX,
-                        0.65, (255, 255, 255), 2)
-            output = ""
-            if objtype == "Unknown":
-                output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
-            if objtype == "Screw" or objtype == "Standoff":
-                output = str(iteml) + "in"
-            if objtype == "Axle":
-                output = "{:.2f}in".format(iteml)
-            cv2.putText(orig, output,  # print data
-                        (int(xpos2 + 10), int(ypos2 + 40)
-                         ), cv2.FONT_HERSHEY_SIMPLEX,
-                        0.65, (255, 255, 255), 2)
-
-    # show the output image
-            cv2.imshow("Item Sorter", orig)
-            cv2.waitKey(25)
-
-cv2.waitKey(0)
+from logic import main      # this comes from a compiled binary
+main ()