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* Rename harriscorner.py to harris_corner.py
* updating DIRECTORY.md
* Rename meanthreshold.py to mean_threshold.py
* updating DIRECTORY.md
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76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
import cv2
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import numpy as np
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"""
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Harris Corner Detector
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https://en.wikipedia.org/wiki/Harris_Corner_Detector
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"""
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class Harris_Corner:
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def __init__(self, k: float, window_size: int):
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"""
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k : is an empirically determined constant in [0.04,0.06]
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window_size : neighbourhoods considered
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"""
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if k in (0.04, 0.06):
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self.k = k
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self.window_size = window_size
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else:
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raise ValueError("invalid k value")
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def __str__(self):
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return f"Harris Corner detection with k : {self.k}"
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def detect(self, img_path: str):
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"""
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Returns the image with corners identified
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img_path : path of the image
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output : list of the corner positions, image
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"""
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img = cv2.imread(img_path, 0)
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h, w = img.shape
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corner_list = []
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color_img = img.copy()
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color_img = cv2.cvtColor(color_img, cv2.COLOR_GRAY2RGB)
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dy, dx = np.gradient(img)
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ixx = dx ** 2
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iyy = dy ** 2
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ixy = dx * dy
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k = 0.04
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offset = self.window_size // 2
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for y in range(offset, h - offset):
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for x in range(offset, w - offset):
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wxx = ixx[
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y - offset : y + offset + 1, x - offset : x + offset + 1
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].sum()
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wyy = iyy[
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y - offset : y + offset + 1, x - offset : x + offset + 1
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].sum()
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wxy = ixy[
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y - offset : y + offset + 1, x - offset : x + offset + 1
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].sum()
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det = (wxx * wyy) - (wxy ** 2)
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trace = wxx + wyy
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r = det - k * (trace ** 2)
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# Can change the value
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if r > 0.5:
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corner_list.append([x, y, r])
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color_img.itemset((y, x, 0), 0)
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color_img.itemset((y, x, 1), 0)
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color_img.itemset((y, x, 2), 255)
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return color_img, corner_list
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if __name__ == "__main__":
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edge_detect = Harris_Corner(0.04, 3)
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color_img, _ = edge_detect.detect("path_to_image")
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cv2.imwrite("detect.png", color_img)
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