add another scan

anno.txt

@@ -0,0 +1,24 @@
+/home/cole/item-sort/positives/IMG_10208.jpg 1 4 3 80 110
+/home/cole/item-sort/positives/IMG_1155.jpg 1 1 1 30 32
+/home/cole/item-sort/positives/IMG_1184.jpg 1 1 1 34 30
+/home/cole/item-sort/positives/IMG_1287.jpg 1 2 2 35 29
+/home/cole/item-sort/positives/IMG_13351.jpg 1 2 2 165 75
+/home/cole/item-sort/positives/IMG_13857.jpg 1 2 5 90 142
+/home/cole/item-sort/positives/IMG_1472.jpg 1 1 1 43 29
+/home/cole/item-sort/positives/IMG_1584.jpg 1 1 1 45 31
+/home/cole/item-sort/positives/IMG_1683.jpg 1 1 0 48 32
+/home/cole/item-sort/positives/IMG_1764.jpg 1 2 1 38 39
+/home/cole/item-sort/positives/IMG_1776.jpg 1 1 0 45 36
+/home/cole/item-sort/positives/IMG_19530.jpg 1 4 4 120 149
+/home/cole/item-sort/positives/IMG_2200.jpg 1 1 0 46 42
+/home/cole/item-sort/positives/IMG_2772.jpg 1 0 0 83 32
+/home/cole/item-sort/positives/IMG_2784.jpg 1 1 1 46 55
+/home/cole/item-sort/positives/IMG_2800.jpg 1 1 1 53 48
+/home/cole/item-sort/positives/IMG_3285.jpg 1 1 1 71 43
+/home/cole/item-sort/positives/IMG_3604.jpg 1 1 0 104 33
+/home/cole/item-sort/positives/IMG_4550.jpg 1 1 1 127 32
+
+/home/cole/item-sort/positives/IMG_4879.jpg 1 2 2 37 114
+/home/cole/item-sort/positives/IMG_7412.jpg 1 2 2 105 65
+/home/cole/item-sort/positives/IMG_7654.jpg 1 4 2 80 84
+/home/cole/item-sort/positives/IMG_7740.jpg 1 1 2 126 57

bg.txt

@@ -0,0 +1,38 @@
+negatives/IMG_10285.jpg
+negatives/IMG_1190.jpg
+negatives/IMG_1225.jpg
+negatives/IMG_1443.jpg
+negatives/IMG_1482.jpg
+negatives/IMG_17202.jpg
+negatives/IMG_18939.jpg
+negatives/IMG_19860.jpg
+negatives/IMG_2116.jpg
+negatives/IMG_2408.jpg
+negatives/IMG_2475.jpg
+negatives/IMG_2548.jpg
+negatives/IMG_2597.jpg
+negatives/IMG_2700.jpg
+negatives/IMG_27048.jpg
+negatives/IMG_2805.jpg
+negatives/IMG_28583.jpg
+negatives/IMG_30940.jpg
+negatives/IMG_35235.jpg
+negatives/IMG_3599.jpg
+negatives/IMG_36040.jpg
+negatives/IMG_36400.jpg
+negatives/IMG_3666.jpg
+negatives/IMG_3840.jpg
+negatives/IMG_3844.jpg
+negatives/IMG_40176.jpg
+negatives/IMG_42840.jpg
+negatives/IMG_5041.jpg
+negatives/IMG_5184.jpg
+negatives/IMG_5402.jpg
+negatives/IMG_5700.jpg
+negatives/IMG_5820.jpg
+negatives/IMG_6014.jpg
+negatives/IMG_6016.jpg
+negatives/IMG_6240.jpg
+negatives/IMG_6435.jpg
+negatives/IMG_7482.jpg
+negatives/IMG_9800.jpg

camera.service

@@ -1,13 +0,0 @@
-[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

@@ -1,15 +0,0 @@
-[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

@@ -1,13 +0,0 @@
-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

@@ -1,101 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,387 +0,0 @@
-# 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

@@ -1,2 +1,333 @@
-from logic import main      # this comes from a compiled binary
+# import the necessary packages
-main ()
+#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()
+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
+
+cascade = cv2.CascadeClassifier()
+if not cascade.load(cv2.samples.findFile(args2.cascade)):
+    print('--(!)Error loading face cascade')
+    exit(0)
+screws = cascade.detectMultiScale(image)
+frame = image.copy()
+for (x,y,w,h) in screws:
+    center = (x + w//2, y + h//2)
+    frame = cv2.ellipse(frame, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)
+if args2.show:
+    cv2.imshow("Item Sorter", frame)
+    cv2.waitKey(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)