# 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): 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(img_file) #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: 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: 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: cv2.imshow("Item Sorter", edged) cv2.waitKey(0) edged = cv2.dilate(edged, None, iterations=1) edged = cv2.erode(edged, None, iterations=1) if 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) / calibration_width continue 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), (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: 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)), (0, 255, 0), 1) # 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, 0.55, (255, 255, 255), 2) else: cv2.putText(orig, str(objtype), (int(xpos2 + 10), int(ypos2 + 20) ), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (255, 255, 255), 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 + 35) ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) else: cv2.putText(orig, output, # print data (int(xpos2 + 10), int(ypos2 + 35) ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) # show the output image if show: cv2.imshow("Item Sorter", orig) #cv2.waitKey(1) cv2.waitKey(0)