def compute_distance_image(self, input_map): #Create necessary matrices dist_matrix=cv.CreateMat(input_map.height, input_map.width, cv.CV_32FC1) dist_img_gray=cv.CreateMat(input_map.height, input_map.width, cv.CV_8UC1) #Get the Euclidean distance from every free point in the map to the closest obstacle cv.DistTransform(input_map, dist_matrix) #To make the pikes of the distance function we use the square of the resulted value square_mat=cv.CreateMat(input_map.height, input_map.width, cv.CV_32FC1) cv.Pow(dist_matrix,square_mat, 3) #We normalize the values of all pixels to print the image. matrix = dist_matrix max_val = np.max(matrix) m=255/max_val for r in range(0, matrix.height): for c in range(0, matrix.width): dist_img_gray[r,c]=np.int(m*matrix[r,c]) return(dist_img_gray, dist_matrix)
def on_trackbar(edge_thresh): cv.Threshold(gray, edge, float(edge_thresh), float(edge_thresh), cv.CV_THRESH_BINARY) #Distance transform cv.DistTransform(edge, dist, cv.CV_DIST_L2, cv.CV_DIST_MASK_5) cv.ConvertScale(dist, dist, 5000.0, 0) cv.Pow(dist, dist, 0.5) cv.ConvertScale(dist, dist32s, 1.0, 0.5) cv.AndS(dist32s, cv.ScalarAll(255), dist32s, None) cv.ConvertScale(dist32s, dist8u1, 1, 0) cv.ConvertScale(dist32s, dist32s, -1, 0) cv.AddS(dist32s, cv.ScalarAll(255), dist32s, None) cv.ConvertScale(dist32s, dist8u2, 1, 0) cv.Merge(dist8u1, dist8u2, dist8u2, None, dist8u) cv.ShowImage(wndname, dist8u)