compile to code, runner script
This commit is contained in:
Cole Deck
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__pycache__/detect.cpython-38.pyc
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__pycache__/detect.cpython-38.pyc
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build/temp.linux-x86_64-3.8/detect.o
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build/temp.linux-x86_64-3.8/detect.o
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compile.py
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compile.py
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from distutils.core import setup
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from distutils.extension import Extension
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from Cython.Distutils import build_ext
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ext_modules = [
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Extension("detect", ["detect.py"]),
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#Extension("mymodule2", ["mymodule2.py"]),
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# ... all your modules that need be compiled ...
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]
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setup(
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name = 'Item Sorter',
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cmdclass = {'build_ext': build_ext},
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ext_modules = ext_modules
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)
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detect.cpython-38-x86_64-linux-gnu.so
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detect.cpython-38-x86_64-linux-gnu.so
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detect.py
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361
detect.py
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# 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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"""
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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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def detect(calibration_width, img_file, show):
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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(img_file)
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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 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, (5, 5), 0)
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if show:
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cv2.imshow("Item Sorter", gray)
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cv2.waitKey(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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if show:
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cv2.imshow("Item Sorter", edged)
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cv2.waitKey(0)
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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 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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# 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) / calibration_width
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continue
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orig = image.copy()
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objtype = "Unknown"
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objname = ""
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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 == num or 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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if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03):
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# Keps nut or spacer
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objtype = "Spacer"
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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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if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5):
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objtype = "Keps Nut"
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if circular and near(radius / pixelsPerMetric, 0.23, 0.02):
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objtype = "Washer"
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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 is not None and defects.size > 5 and (convexness < 0.9 or boxiness < 0.75) and rectangular:
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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 and rectangular:
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objtype = "Standoff"
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iteml = sizeStandoff(itemw)
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if itemhr == 0.1875 and rectangular:
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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 show or True:
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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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if circular:
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cv2.putText(orig, str(objtype),
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(int(x - 25), int(y + radius + 20)
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), cv2.FONT_HERSHEY_SIMPLEX,
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0.55, (255, 255, 255), 2)
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else:
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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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0.55, (255, 255, 255), 2)
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output = ""
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objname = objtype;
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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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objname += str(iteml)
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if objtype == "Axle":
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output = "{:.2f}in".format(iteml)
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objname += str(itemwr)
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print(objname)
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if circular:
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cv2.putText(orig, output, # print data
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||||
(int(x - 25), int(y + radius + 35)
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), cv2.FONT_HERSHEY_SIMPLEX,
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||||
0.5, (255, 255, 255), 1)
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||||
else:
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cv2.putText(orig, output, # print data
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||||
(int(xpos2 + 10), int(ypos2 + 35)
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||||
), cv2.FONT_HERSHEY_SIMPLEX,
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||||
0.5, (255, 255, 255), 1)
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||||
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||||
# show the output image
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if show:
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cv2.imshow("Item Sorter", orig)
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||||
#cv2.waitKey(1)
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||||
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||||
cv2.waitKey(0)
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||||
362
main.py
362
main.py
@@ -1,360 +1,2 @@
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||||
# 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()
|
||||
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, (5, 5), 0)
|
||||
if args2.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 args2.show:
|
||||
cv2.imshow("Item Sorter", edged)
|
||||
cv2.waitKey(0)
|
||||
|
||||
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
|
||||
|
||||
|
||||
# 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"]
|
||||
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 == 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:
|
||||
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 args2.show:
|
||||
# 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
|
||||
cv2.imshow("Item Sorter", orig)
|
||||
#cv2.waitKey(1)
|
||||
|
||||
cv2.waitKey(0)
|
||||
from logic import main # this comes from a compiled binary
|
||||
main ()
|
||||
12
run_detect.py
Normal file
12
run_detect.py
Normal file
@@ -0,0 +1,12 @@
|
||||
import detect
|
||||
import timeit
|
||||
calibration_width = 0.75
|
||||
image = "img7.jpg"
|
||||
images = ("img.jpg", "img2.jpg", "img3.jpg", "img4.jpg", "img5.jpg", "img6.jpg", "img7.jpg", "img8.jpg")
|
||||
show = False
|
||||
def go():
|
||||
#for file in images:
|
||||
detect.detect(calibration_width, "img7.jpg", show)
|
||||
|
||||
elapsed_time = timeit.timeit(go, number=100)/100
|
||||
print(elapsed_time)
|
||||
Reference in New Issue
Block a user