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import cv2

import numpy as np

"""

Harris Corner Detector

https://en.wikipedia.org/wiki/Harris_Corner_Detector

"""

class Harris_Corner:

def __init__(self, k: float, window_size: int):

"""

k : is an empirically determined constant in [0.04,0.06]

window_size : neighbourhoods considered

"""

if k in (0.04, 0.06):

self.k = k

self.window_size = window_size

else:

raise ValueError("invalid k value")

def __str__(self) -> str:

return f"Harris Corner detection with k : {self.k}"

def detect(self, img_path: str) -> tuple[cv2.Mat, list[list[int]]]:

"""

Returns the image with corners identified

img_path : path of the image

output : list of the corner positions, image

"""

img = cv2.imread(img_path, 0)

h, w = img.shape

corner_list: list[list[int]] = []

color_img = img.copy()

color_img = cv2.cvtColor(color_img, cv2.COLOR_GRAY2RGB)

dy, dx = np.gradient(img)

ixx = dx ** 2

iyy = dy ** 2

ixy = dx * dy

k = 0.04

offset = self.window_size // 2

for y in range(offset, h - offset):

for x in range(offset, w - offset):

wxx = ixx[

y - offset : y + offset + 1, x - offset : x + offset + 1

].sum()

wyy = iyy[

y - offset : y + offset + 1, x - offset : x + offset + 1

].sum()

wxy = ixy[

y - offset : y + offset + 1, x - offset : x + offset + 1

].sum()

det = (wxx * wyy) - (wxy ** 2)

trace = wxx + wyy

r = det - k * (trace ** 2)

# Can change the value

if r > 0.5:

corner_list.append([x, y, r])

color_img.itemset((y, x, 0), 0)

color_img.itemset((y, x, 1), 0)

color_img.itemset((y, x, 2), 255)

return color_img, corner_list

if __name__ == "__main__":

edge_detect = Harris_Corner(0.04, 3)

color_img, _ = edge_detect.detect("path_to_image")

cv2.imwrite("detect.png", color_img)

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