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155 lines
5.0 KiB
Python
155 lines
5.0 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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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 = 0.09
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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 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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# 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("-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, (image.shape[1]*2, image.shape[0]*2), interpolation = cv2.INTER_NEAREST)
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if args2.show:
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cv2.imshow("Screw Length Detection", 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("Screw Length Detection", 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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# loop over the contours individually
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for c in cnts:
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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:
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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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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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cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
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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-righ 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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cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
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(255, 0, 255), 2)
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cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
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(255, 0, 255), 2)
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# compute the Euclidean distance between the midpoints
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dA = np.linalg.norm(np.array((tltrX, tltrY, 0)) - np.array((blbrX, blbrY, 0)))
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dB = np.linalg.norm(np.array((tlblX, tlblY, 0)) - np.array((trbrX, trbrY, 0)))
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# if the pixels per metric has not been initialized, then
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# compute it as the ratio of pixels to supplied metric
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# (in this case, inches)
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if pixelsPerMetric is None:
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pixelsPerMetric = dB / args["width"]
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# compute the size of the object
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dimA = dA / pixelsPerMetric
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dimB = dB / pixelsPerMetric
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if num == num:
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iteml = larger(dimA, dimB)
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print("Screw Length (RAW): " + str(iteml))
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iteml = sizeVexScrew(iteml)
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print("Rounded Length: " + 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(larger(dimA, dimB)),
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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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if num > 1:
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cv2.putText(orig, "{:.3f}in screw".format(iteml), # print screw length
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(int(trbrX + 20), int(trbrY + 20)), 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("Screw Length Detection", orig)
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cv2.waitKey(0) |