65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
from dataclasses import dataclass
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from vision.model import Image
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from typing import List
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import numpy as np
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from scipy.signal import convolve2d
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import math as m
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import cv2
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@dataclass
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class Edge:
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magnitude: np.array
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angle: np.array
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def get_edge_angle_hist(edge: Edge, bins: int, threshold: float):
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angle_vals = []
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for i in range(edge.magnitude.shape[0]):
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for j in range(edge.magnitude.shape[1]):
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if edge.magnitude[i, j] > threshold:
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bin_val = m.floor((edge.angle[i, j] / (2*np.pi)) * bins)
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angle_vals.append(bin_val)
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if len(angle_vals) > 0:
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return np.histogram(angle_vals, bins=bins, density=True)[0]
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else:
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return np.zeros(bins)
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def get_edge_info(pixels: np.array = None,
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image: Image = None,
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images: List[Image] = None,
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blur: bool = True):
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if pixels is None and image is None and images is None:
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raise KeyError('no image provided')
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def extract(i):
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kx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) / 4
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ky = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]) / 4
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grey = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
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if blur is True:
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grey = convolve2d(grey, np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) / 9)
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dx = convolve2d(grey, kx)
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dy = convolve2d(grey, ky)
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mag = np.sqrt(dx**2 + dy**2)
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angle = np.arctan2(dy, dx)
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angle = (angle + 2 * np.pi) % (2 * np.pi)
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return Edge(mag, angle)
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if images is not None:
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for image in images:
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image.descriptor = extract(image.pixels)
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return
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elif image is not None:
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return extract(image.pixels)
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else:
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return extract(pixels)
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