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31 lines
797 B
Python
31 lines
797 B
Python
import numpy as np
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from numba import jit
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from scipy.optimize import least_squares
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@jit(nopython=True)
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def compute_gaussian(r, img_w, img_h,
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gaussian_w, gaussian_h,
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gaussian_cx, gaussian_cy):
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for y in range(img_h):
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for x in range(img_w):
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ds = ((gaussian_cx-x)/gaussian_w)**2
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ds += ((gaussian_cy-y)/gaussian_h)**2
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r[x, y] = np.exp(-ds/2)
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def fit(data):
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img_w, img_h = data.shape
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def err(parameters):
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r = np.empty((img_w, img_h))
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compute_gaussian(r, img_w, img_h, *parameters)
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r -= data
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return r.ravel()
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guess = [12, 15, img_w/2, img_h/2]
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res = least_squares(err, guess)
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return res.x
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def get_and_fit():
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return fit(controller_driver.get_picture())
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