# 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):
    selected = 2
    #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, (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 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, (5, 5), 0)
    if show and not quick:
        cv2.imshow("Item Sorter", gray)
        cv2.waitKey(0)

    # perform edge detection, then perform a dilation + erosion to
    # close gaps in between object edges
    edged = cv2.Canny(gray, 50, 100)
    if show and not quick:
        cv2.imshow("Item Sorter", edged)
        cv2.waitKey(0)

    edged = cv2.dilate(edged, None, iterations=1)
    edged = cv2.erode(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 = "Unknown"
    objname = ""
    # 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 == num or show:
            area_box = dA * dB
            (x, y), radius = cv2.minEnclosingCircle(c)
            area_contour = cv2.contourArea(c)
            area_circle = math.pi * pow(radius, 2)
            boxiness = area_contour / area_box
            circleness = area_contour / area_circle
            circular = False
            rectangular = False
            if boxiness > circleness:
                rectangular = True
                #cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
            else:
                circular = True
                cv2.circle(orig, (int(x), int(y)), int(radius), (255, 0, 0), 2)

            objtype = "Unknown"
            itemw = larger(dimA, dimB)
            itemwr = itemw
            itemwr *= 8
            itemwr = round(itemwr)
            itemwr /= 8

            itemh = smaller(dimA, dimB)
            itemhr = itemh
            itemhr *= 16
            itemhr = round(itemhr)
            itemhr /= 16
            if circular and itemwr == 0.75:
                objtype = "Penny"
                iteml = 0
            else:
                if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03):
                    # Keps nut or spacer
                    objtype = "Spacer"
                    mask = np.zeros(gray.shape, np.uint8)
                    cv2.drawContours(mask, [c], 0, 255, -1)
                    #pixelpoints = np.transpose(np.nonzero(mask))
                    hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV)
                    mean_val = cv2.mean(hsv, mask=mask)
                    #print(str(mean_val[0]))
                    if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5):
                        objtype = "Keps Nut"
                if circular and near(radius / pixelsPerMetric, 0.23, 0.02):
                    objtype = "Washer"
                epsilon = 3  # 0.02*cv2.arcLength(c,True)
                # print(str(epsilon))
                approx = cv2.approxPolyDP(c, epsilon, True)
                hull = cv2.convexHull(approx, returnPoints=False)
                hull2 = cv2.convexHull(c)
                defects = cv2.convexityDefects(c, hull)
                #print(str(defects.size) + " match")
                cv2.drawContours(orig, (hull2), -1, (0, 0, 255), 3)
                cv2.drawContours(orig, (approx), -1, (255, 0, 0), 3)
                convexness = area_contour / cv2.contourArea(hull2)
                #print(str(convexness) + " % fill")
                # if not cv2.isContourConvex(approx):
                # if cv2.matchShapes(hull, c, 1, 0.0) > 1:
                if defects is not None and defects.size > 5 and (convexness < 0.9 or boxiness < 0.75) and rectangular:
                    objtype = "Screw"
                    iteml = larger(dimA, dimB)
                    #print("Screw Length (RAW): " + str(iteml))
                    iteml = sizeVexScrew(radius * 2 / pixelsPerMetric)
                    #print("Rounded Length: " + str(iteml))
                else:
                    if itemhr == 0.3125 and rectangular:
                        objtype = "Standoff"
                        iteml = sizeStandoff(itemw)

                    if itemhr == 0.1875 and rectangular:
                        objtype = "Axle"
                        iteml = (radius * 2 / pixelsPerMetric + itemw) / 2

            rows, cols = orig.shape[:2]
            [vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
            lefty = int((-xx*vy/vx) + yy)
            righty = int(((cols-xx)*vy/vx)+yy)
            # cv2.line(orig,(cols-1,righty),(0,lefty),(0,255,0),2)
            slope = (lefty - righty) / (1 - cols)
            angle = math.atan(slope)
            xpos = x - math.cos(angle) * radius
            ypos = y - math.sin(angle) * radius
            xpos2 = x + math.cos(angle) * radius
            ypos2 = y + math.sin(angle) * radius
            if xpos > xpos2:
                swap(xpos, xpos2)
                swap(ypos, ypos2)
            if rectangular:
                cv2.line(orig, (int(xpos), int(ypos)),
                         (int(xpos2), int(ypos2)), (255, 127, 0), 2)
            # print(str(iteml))
            # draw the object sizes on the image
            if show or True:
                # cv2.putText(orig, "{:.5f}in".format(itemhr),
                #	(int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
                #	0.65, (255, 255, 255), 2)
                if circular:
                    cv2.putText(orig, str(objtype),
                                (int(x - 25), int(y + radius + 20)
                                 ), cv2.FONT_HERSHEY_SIMPLEX,
                                0.6, (50, 50, 220), 2)
                else:
                    cv2.putText(orig, str(objtype),
                                (int(xpos2 + 10), int(ypos2 + 20)
                                 ), cv2.FONT_HERSHEY_SIMPLEX,
                                0.6, (50, 50, 220), 2)
                output = ""
                objname = objtype;
                if objtype == "Unknown":
                    output = "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh)
                if objtype == "Screw" or objtype == "Standoff":
                    output = str(iteml) + "in"
                    objname += str(iteml)
                if objtype == "Axle":
                    output = "{:.2f}in".format(iteml)
                    objname += str(itemwr)
                print(objname)
                if circular:
                    cv2.putText(orig, output,  # print data
                                (int(x - 25), int(y + radius + 40)
                                 ), cv2.FONT_HERSHEY_SIMPLEX,
                                0.5, (50, 50, 220), 1)
                else:
                    cv2.putText(orig, output,  # print data
                                (int(xpos2 + 10), int(ypos2 + 40)
                                 ), cv2.FONT_HERSHEY_SIMPLEX,
                                0.5, (50, 50, 220), 1)

        # show the output image
                if show:
                    cv2.imshow("Item Sorter", orig)
                #cv2.waitKey(1)
    if quick:
        return orig
    else:
        cv2.waitKey(0)