73 lines
1.6 KiB
Python
73 lines
1.6 KiB
Python
import math
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import cv2 as cv
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import numpy as np
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from numba import jit
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# camera settings
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I_Darkcurrent = 150.5
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exposure_time = 0.5
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f_stop = 2.4
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ISO = 64 # basically brightness
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# I_Darkcurrent = 50.1
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# exposure_time = 0.1
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# f_stop = 1
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# ISO = 70 # basically brightness
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@jit(nopython=True)
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def rg_ratio_normalize(imgarr):
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imgnew = imgarr
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for i in range(len(imgarr)):
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for j in range(len(imgarr[i])):
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px = imgarr[i][j]
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r_norm = normalization_func(px[0])
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g_norm = normalization_func(px[1])
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# apply camera calibration func
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ratio = pyrometry_calibration_formula(g_norm, r_norm)
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# remove edge cases
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if ratio > 1900:
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ratio = 0
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imgnew[i][j] = [ratio, ratio, ratio]
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return imgnew
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@jit(nopython=True)
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def normalization_func(i):
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return (i - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
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@jit(nopython=True)
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def pyrometry_calibration_formula(i_ng, i_nr):
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return 362.73 * math.log10(
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(i_ng/i_nr) ** 3
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) + 2186.7 * math.log10(
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(i_ng/i_nr) ** 3
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) + 4466.5 * math.log10(
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(i_ng / i_nr) ** 3
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) + 3753.5
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# read image & crop
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img = cv.imread('01-0001.png')
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img = img[400:, 420:1200]
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# img = cv.imread('ember_test.png')
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img = rg_ratio_normalize(img)
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# apply smoothing conv kernel
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kernel = np.array([
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[1/2, 1/2],
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[1/2, 1/2],
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])
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# Scaling adjustment factor
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kernel *= 3/5
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img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
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# apply jet color map
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img = cv.applyColorMap(img, cv.COLORMAP_JET)
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cv.imwrite('01-0001-transformed-ratio.png', img)
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