131 lines
3.5 KiB
Python
131 lines
3.5 KiB
Python
import math
|
|
from multiprocessing.sharedctypes import Value
|
|
import cv2 as cv
|
|
import numpy as np
|
|
from numba import jit
|
|
|
|
@jit(nopython=True)
|
|
def rg_ratio_normalize(
|
|
imgarr,
|
|
I_Darkcurrent,
|
|
f_stop,
|
|
exposure_time,
|
|
ISO,
|
|
MIN_TEMP,
|
|
MAX_TEMP,
|
|
eqn_scaling_factor
|
|
):
|
|
# copy image into new array & chop off alpha values (if applicable)
|
|
imgnew = imgarr.copy()[:,:,:3]
|
|
|
|
for i in range(len(imgarr)):
|
|
for j in range(len(imgarr[i])):
|
|
px = imgarr[i][j]
|
|
|
|
# normalize R & G pixels
|
|
g_norm = (px[1] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
|
|
r_norm = (px[2] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
|
|
|
|
# apply camera calibration func
|
|
temp_C = pyrometry_calibration_formula(g_norm, r_norm, default=MIN_TEMP) * eqn_scaling_factor
|
|
|
|
# remove pixels outside calibration range
|
|
if (MIN_TEMP != None and temp_C < MIN_TEMP) or (MAX_TEMP != None and temp_C > MAX_TEMP):
|
|
temp_C = MIN_TEMP
|
|
|
|
# scale light intensity to calculated temperature
|
|
pix_i = scale_temp(temp_C, MIN_TEMP, MAX_TEMP)
|
|
imgnew[i][j] = [pix_i, pix_i, pix_i]
|
|
|
|
return imgnew
|
|
|
|
|
|
@jit(nopython=True)
|
|
def pyrometry_calibration_formula(i_ng, i_nr, default=24.0):
|
|
"""
|
|
Given the green-red ratio, calculates an approximate temperature
|
|
in Celsius. Defaults to room temperature if there's an error.
|
|
"""
|
|
try:
|
|
return (
|
|
(362.73 * math.log10(i_ng / i_nr) ** 3) +
|
|
(2186.7 * math.log10(i_ng / i_nr) ** 2) +
|
|
(4466.5 * math.log10(i_ng / i_nr)) +
|
|
3753.5
|
|
)
|
|
except:
|
|
return default
|
|
|
|
|
|
@jit(nopython=True)
|
|
def scale_temp(t, min, max):
|
|
"""
|
|
Scale pixel temperature (t) to light intensity given min & max temp.
|
|
"""
|
|
return (t - min) / (max - min) * 255
|
|
|
|
|
|
def ratio_pyrometry_pipeline(
|
|
file_bytes,
|
|
# camera settings
|
|
I_Darkcurrent: float,
|
|
exposure_time: float,
|
|
f_stop: float,
|
|
ISO: float,
|
|
# pyrometry config
|
|
MAX_TEMP: float,
|
|
MIN_TEMP: float,
|
|
smoothing_radius: int,
|
|
key_entries: int,
|
|
eqn_scaling_factor: float,
|
|
):
|
|
|
|
# read image & crop
|
|
img_orig = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED)
|
|
|
|
img = rg_ratio_normalize(
|
|
img_orig,
|
|
I_Darkcurrent,
|
|
f_stop,
|
|
exposure_time,
|
|
ISO,
|
|
MIN_TEMP,
|
|
MAX_TEMP,
|
|
eqn_scaling_factor
|
|
)
|
|
|
|
# build & apply smoothing conv kernel
|
|
k = []
|
|
for i in range(smoothing_radius):
|
|
k.append([1/(smoothing_radius**2) for i in range(smoothing_radius)])
|
|
kernel = np.array(k)
|
|
|
|
img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
|
|
|
|
# write colormapped image
|
|
# img_jet = img
|
|
img_jet = cv.applyColorMap(img, cv.COLORMAP_JET)
|
|
|
|
# --- Generate temperature key ---
|
|
|
|
# Generate key
|
|
step = (MAX_TEMP - MIN_TEMP) / (key_entries-1)
|
|
temps = []
|
|
key_img_arr = [[]]
|
|
for i in range(key_entries):
|
|
res_temp = MIN_TEMP + (i * step)
|
|
res_color = scale_temp(res_temp, MIN_TEMP, MAX_TEMP)
|
|
temps.append(math.floor(res_temp))
|
|
key_img_arr[0].append([res_color, res_color, res_color])
|
|
|
|
key_img = np.array(key_img_arr).astype(np.uint8)
|
|
key_img_jet = cv.applyColorMap(key_img, cv.COLORMAP_JET)
|
|
|
|
tempkey = {}
|
|
for i in range(len(temps)):
|
|
c = key_img_jet[0][i]
|
|
tempkey[temps[i]] = f"rgb({c[2]}, {c[1]}, {c[0]})"
|
|
|
|
# original, transformed, legend
|
|
return img_orig, img_jet, tempkey
|