[[-1 -1  1]
 [-1 -1  1]
 [ 1  1  1]]

mask = cv2.morphologyEx(threshed, cv2.MORPH_HITMISS, kernel, anchor=(-1,-1))

import cv2
import numpy as np
from itertools import tee

def pairwise(iterable):
    a, b = tee(iterable)
    next(b, None)
    return zip(a, b)

# read image as grayscale
img = cv2.imread('example.png', 0)

# get corner points, remove duplicate/nearby lines
contours = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[1]
contour = contours[0]
pts = np.array([contour[0]] + [pt1 for pt0, pt1 in pairwise(contour) if not (abs(pt0 - pt1) <= 1).all()])
x, y = pts[:, -1, 0], pts[:, -1, 1]

# get the kernel that you will sum around your corner points
kernel = np.float64(cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (13, 13)))
kernel /= np.sum(kernel)

# convole the image with the kernel, and pull out the sums at the corner points
conv = cv2.filter2D(img/255, cv2.CV_64F, kernel)
neighborhood_sums = conv[y, x]

# concave indices have more white than black around them, so convolution will be >= 1/2
concave_indices = neighborhood_sums >= 0.5

# draw markers
marked = cv2.merge([img, img, img])
for pt, concave in zip(pts, concave_indices):
    color = (255, 0, 255) if concave else (0, 255, 0)
    marker = cv2.MARKER_TRIANGLE_UP if concave else cv2.MARKER_TRIANGLE_DOWN
    cv2.drawMarker(marked, tuple(pt[0]), color, markerType=marker, markerSize=10, thickness=3)