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@ -75,38 +75,45 @@ ap.add_argument("-n", "--number", type=int, required=False,
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help="show on the screen")
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help="show on the screen")
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args = vars(ap.parse_args())
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args = vars(ap.parse_args())
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args2 = 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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def detect(calibration_width, img_file, show, quick):
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selected = 2
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selected = 2
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#if type(args["number"]) == type(selected):
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#if type(args["number"]) == type(selected):
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# selected = args["number"]
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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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# load the image, convert it to grayscale, and blur it slightly
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image = None
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print(str(type(img_file)))
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if str(type(img_file)) == "<class 'numpy.ndarray'>":
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image = img_file.copy()
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else:
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image = cv2.imread(img_file)
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image = cv2.imread(img_file)
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#image = img_file.copy()
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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, (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 = cv2.resize(image, (1000, int(
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image.shape[0]/image.shape[1] * 1000)), interpolation=cv2.INTER_NEAREST)
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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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if show and not quick:
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cv2.namedWindow("Item Sorter")
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cv2.namedWindow("Item Sorter")
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cv2.imshow("Item Sorter", image)
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cv2.imshow("Item Sorter", image)
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cv2.waitKey(0)
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cv2.waitKey(0)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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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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gray = cv2.GaussianBlur(gray, (5, 5), 0)
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if show:
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if show and not quick:
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cv2.imshow("Item Sorter", gray)
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cv2.imshow("Item Sorter", gray)
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cv2.waitKey(0)
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cv2.waitKey(0)
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# perform edge detection, then perform a dilation + erosion to
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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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# close gaps in between object edges
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edged = cv2.Canny(gray, 50, 100)
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edged = cv2.Canny(gray, 50, 100)
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if show:
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if show and not quick:
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cv2.imshow("Item Sorter", edged)
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cv2.imshow("Item Sorter", edged)
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cv2.waitKey(0)
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cv2.waitKey(0)
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edged = cv2.dilate(edged, None, iterations=1)
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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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edged = cv2.erode(edged, None, iterations=1)
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if show:
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if show and not quick:
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cv2.imshow("Item Sorter", edged)
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cv2.imshow("Item Sorter", edged)
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cv2.waitKey(0)
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cv2.waitKey(0)
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# find contours in the edge map
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# find contours in the edge map
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@ -171,7 +178,7 @@ def detect(calibration_width, img_file, show):
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pixelsPerMetric = smaller(dA, dB) / calibration_width
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pixelsPerMetric = smaller(dA, dB) / calibration_width
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continue
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continue
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pixelsPerMetric = 25
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orig = image.copy()
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orig = image.copy()
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objtype = "Unknown"
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objtype = "Unknown"
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objname = ""
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objname = ""
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@ -180,7 +187,7 @@ def detect(calibration_width, img_file, show):
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#orig = image.copy()
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#orig = image.copy()
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num += 1
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num += 1
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# if the contour is not sufficiently large, ignore it
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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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if cv2.contourArea(c) < 100: # or pixelsPerMetric is None:
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continue
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continue
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# compute the rotated bounding box of the contour
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# compute the rotated bounding box of the contour
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@ -357,5 +364,7 @@ def detect(calibration_width, img_file, show):
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if show:
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if show:
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cv2.imshow("Item Sorter", orig)
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cv2.imshow("Item Sorter", orig)
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#cv2.waitKey(1)
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#cv2.waitKey(1)
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if quick:
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return orig
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else:
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cv2.waitKey(0)
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cv2.waitKey(0)
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