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334 lines
11 KiB
Python
334 lines
11 KiB
Python
# import the necessary packages
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#from imutils import perspective
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from imutils import contours
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import numpy as np
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import argparse
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import imutils
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import cv2
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import math
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import time
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itemw = 0
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itemh = 0
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def midpoint(ptA, ptB):
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return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
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def sizeVexScrew(iteml):
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# Screw Sizing code
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# subtract screw head size to find thread length
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shead = 0.1
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iteml -= shead
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#print("Thread Length: " + str(iteml))
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iteml *= 8
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iteml = round(iteml)
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iteml /= 8
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return iteml
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def sizeStandoff(iteml):
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# Standoff Sizing code
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iteml *= 2
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iteml = round(iteml)
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iteml /= 2
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return iteml
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def larger(a, b):
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if a >= b:
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return a
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else:
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return b
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def smaller(a, b):
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if a < b:
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return a
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else:
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return b
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def near(a, b, close):
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if abs(a-b) < close:
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return True
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return False
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def swap(a, b):
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tmp = a
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a = b
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b = tmp
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# construct the argument parse and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-i", "--image", required=True,
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help="path to the input image")
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ap.add_argument("-c", "--cascade", required=True,
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help="path to the cascade")
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ap.add_argument("-w", "--width", type=float, required=True,
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help="width of the left-most object in the image (in inches)")
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ap.add_argument("-n", "--number", type=int, required=False,
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help="object # to measure (from left to right)")
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ap.add_argument("-s", "--show", action="store_true",
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help="show on the screen")
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args = vars(ap.parse_args())
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args2 = ap.parse_args()
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selected = 2
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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 = cv2.imread(args["image"])
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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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if args2.show:
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cv2.namedWindow("Item Sorter")
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cv2.imshow("Item Sorter", image)
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cv2.waitKey(0)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (7, 7), 0)
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# perform edge detection, then perform a dilation + erosion to
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# close gaps in between object edges
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edged = cv2.Canny(gray, 50, 100)
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edged = cv2.dilate(edged, None, iterations=1)
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#edged = cv2.erode(edged, None, iterations=1)
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if args2.show:
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cv2.imshow("Item Sorter", edged)
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cv2.waitKey(0)
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# find contours in the edge map
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cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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# sort the contours from left-to-right and initialize the
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# 'pixels per metric' calibration variable
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#(cnts, _) = contours.sort_contours(cnts)
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pixelsPerMetric = None
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num = 0
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cascade = cv2.CascadeClassifier()
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if not cascade.load(cv2.samples.findFile(args2.cascade)):
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print('--(!)Error loading face cascade')
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exit(0)
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screws = cascade.detectMultiScale(image)
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frame = image.copy()
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for (x,y,w,h) in screws:
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center = (x + w//2, y + h//2)
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frame = cv2.ellipse(frame, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)
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if args2.show:
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cv2.imshow("Item Sorter", frame)
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cv2.waitKey(0)
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# Calibration loop
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for c in cnts:
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# if the contour is not sufficiently large, ignore it
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if cv2.contourArea(c) < 100:
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continue
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# compute the rotated bounding box of the contour
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orig = image.copy()
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box = cv2.minAreaRect(c)
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# xpos,ypos,w,h = cv2.boundingRect(c)
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# crop_img = orig[ypos:ypos+h, xpos:xpos+w]
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# cv2.imwrite("object_images/IMG_" + str(w*h) + ".jpg", crop_img) # create training images
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box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
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box = np.array(box, dtype="int")
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#box = perspective.order_points(box)
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(tl, tr, br, bl) = box
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(tltrX, tltrY) = midpoint(tl, tr)
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(blbrX, blbrY) = midpoint(bl, br)
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(tlblX, tlblY) = midpoint(tl, bl)
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(trbrX, trbrY) = midpoint(tr, br)
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dA = np.linalg.norm(np.array((tltrX, tltrY, 0)) -
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np.array((blbrX, blbrY, 0)))
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dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) -
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np.array((trbrX, trbrY, 0)))
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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), (0, 255, 0), 2)
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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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#print(", " + str(mean_val[0]/mean_val[2]))
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#print(", " + str(mean_val[2]/mean_val[1]))
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if pixelsPerMetric is None and circular is True and near(mean_val[0], 16, 4.5):
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# 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:
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pixelsPerMetric = smaller(dA, dB) / args["width"]
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orig = image.copy()
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# loop over the contours individually
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for c in cnts:
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#orig = image.copy()
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num += 1
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# if the contour is not sufficiently large, ignore it
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if cv2.contourArea(c) < 100 or pixelsPerMetric is None:
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continue
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# compute the rotated bounding box of the contour
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box = cv2.minAreaRect(c)
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box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
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box = np.array(box, dtype="int")
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# order the points in the contour such that they appear
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# in top-left, top-right, bottom-right, and bottom-left
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# order, then draw the outline of the rotated bounding
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# box
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#box = perspective.order_points(box)
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# loop over the original points and draw them
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# for (x, y) in box:
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#cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
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# unpack the ordered bounding box, then compute the midpoint
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# between the top-left and top-right coordinates, followed by
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# the midpoint between bottom-left and bottom-right coordinates
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(tl, tr, br, bl) = box
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(tltrX, tltrY) = midpoint(tl, tr)
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(blbrX, blbrY) = midpoint(bl, br)
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# compute the midpoint between the top-left and top-right points,
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# followed by the midpoint between the top-right and bottom-right
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(tlblX, tlblY) = midpoint(tl, bl)
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(trbrX, trbrY) = midpoint(tr, br)
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# draw the midpoints on the image
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#cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
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#cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
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#cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
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#cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
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# draw lines between the midpoints
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# compute the Euclidean distance between the midpoints
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dA = np.linalg.norm(np.array((tltrX, tltrY, 0)) -
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np.array((blbrX, blbrY, 0)))
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dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) -
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np.array((trbrX, trbrY, 0)))
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dimA = dA / pixelsPerMetric
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dimB = dB / pixelsPerMetric
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if num == selected or args2.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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else:
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circular = True
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cv2.circle(orig, (int(x), int(y)), int(radius), (0, 255, 0), 1)
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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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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.size > 5 and (convexness < 0.9 or boxiness < 0.75):
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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:
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objtype = "Standoff"
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iteml = sizeStandoff(itemw)
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if itemhr == 0.1875:
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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)), (0, 255, 0), 1)
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# print(str(iteml))
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# draw the object sizes on the image
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if args2.show:
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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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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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0.65, (255, 255, 255), 2)
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output = ""
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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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if objtype == "Axle":
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output = "{:.2f}in".format(iteml)
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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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0.65, (255, 255, 255), 2)
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# show the output image
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cv2.imshow("Item Sorter", orig)
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cv2.waitKey(25)
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cv2.waitKey(0)
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