def convert_to_line_segs(track): line_segs = [] last_pt = track[-1].copy() for pt in track: line_segs.append(line_seg.line_seg(last_pt, pt)) last_pt = pt return line_segs
def get_sensor(self): sensor_mid = [self.x + self.sensor_offset*cos(self.orientation), self.y + self.sensor_offset*sin(self.orientation)] sensor_left = [sensor_mid[0] - self.sensor_width*sin(self.orientation), sensor_mid[1] + self.sensor_width*cos(self.orientation)] sensor_right = [sensor_mid[0] + self.sensor_width*sin(self.orientation), sensor_mid[1] - self.sensor_width*cos(self.orientation)] return line_seg.line_seg(sensor_left, sensor_right)
# to change the contrast on the full-sized image
img2 = cv2.imread(depth_im, -1)
img2 = util.normalize_depth(img2)
img2 = util.clahe(img2, iter=2)
# crops the image
img2 = img2[mouse_Y[0]:mouse_Y[1], mouse_X[0]:mouse_X[1]]
P["img2"] = img2
# *********************************** SECTION 1 *****************************************
# FIND DEPTH / CURVATURE DISCONTINUITIES.
curve_disc, depth_disc, edgelist = edge_detect(P)
#CREATES LINE SEGMENTS
seglist = line_seg(edgelist, tol=5)
if context.ShowEdgeListWnd:
draw.draw_edge_list(seglist, P)
line_pairs = []
cntr_pairs = []
img_size = copy.deepcopy(P["img_size"])
height = img_size[0]
width = img_size[1]
blank_im = np.zeros((height, width, 3), np.uint8)
print("img size", img_size)
window_size = 3
def roipoly(src, poly):
import numpy as np
import cv2
import line_seg
#im = cv2.imread('images/text.png')
im1 = cv2.imread('images/text3.jpg')
im1 = cv2.resize(im1, None, fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
#im = cv2.resize(im,None,fx=4,fy=4,interpolation=cv2.INTER_CUBIC)
#im = im[19:110,:]
lines = line_seg.line_seg()
j = 0
for i in range(len(lines)):
if i < len(lines) - 1:
im = im1[lines[i] - 2:lines[i + 1] + 2, 0:im1.shape[1]]
height = im.shape[0]
width = im.shape[1]
imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
#thresh = cv2.adaptiveThreshold(imgray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,21,20)
thresh = cv2.threshold(imgray, 0, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
thresh_real = cv2.threshold(imgray, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
#thresh_inv = cv2.adaptiveThreshold(imgray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,21,20)
kernel = np.ones((5, 5), np.uint8)
blob = cv2.dilate(thresh, kernel, iterations=4)
blob = cv2.erode(blob, kernel, iterations=4)
count = np.zeros(shape=(height, ))
for y in range(height):
for x in range(width):
if blob[y][x] == 255:
