def Otsu_Detection(mypath):
#read image
for fn in glob(mypath + "\\*.jpg"):
image = cv2.imread(fn)
#grayscale image
gray = pp.grayImage(image)
#filter image
Filter = pp.Gfilter(gray)
#segment image
segment = seg.otsu(Filter)
#detect edges
edged = seg.CannySeg(segment)
#dilate segmented image
gradient = cv2.morphologyEx(edged, cv2.MORPH_GRADIENT, det.getKennel())
#get contours of the image
contours = det.getContours(gradient, image)
#calculate contours area
getArea = det.get_contour_areas(contours)
# Labeling Contours left to right
for (i, c) in enumerate(contours):
hull = cv2.convexHull(c)
M = cv2.moments(hull)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
#if shape looks like circle/pentagon, treat as pothole
shape_detector_start = ShapeDetector.detect(shape_detector, hull)
if (shape_detector_start == "circle"
or shape_detector_start == "pentagon" or
shape_detector_start == "rectangle") and getArea[i] > 3250:
draw = cv2.drawContours(image, [hull], 0, (0, 0, 255), 2)
cv2.putText(image, "Pothole", (cx, cy),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
else:
continue
cv2.imshow("result", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
import cv2 #OpenCv
import numpy as np #numpy
import pre_processing as pp # preprocessing module
import segmentation as seg # segmentation module
import detection as det #detection module
import ROI # get Region Of Interest
from glob import glob
#kennel size
getkennel = det.getKennel()
#Shape Detector
class ShapeDetector:
def __init__(self):
pass
def detect(self, c):
# initialize the shape name and approximate the contour
shape = "unidentified"
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.05 * peri, False)
# if the shape is a triangle, it will have 3 vertices
if len(approx) == 3:
shape = "triangle"
# if the shape has 4 vertices, it is either a square or
# a rectangle
elif len(approx) == 4:
# compute the bounding box of the contour and use the
# bounding box to compute the aspect ratio
(x, y, w, h) = cv2.boundingRect(approx)