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artiq/examples/master/repository/remote_exec_processing.py

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797 B
Python

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