firelab-general/ratio_pyrometry.py

137 lines
3.7 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,
):
"""
Get normalized G/R -> temperature data + list of all temperatures
"""
# copy image into new array & chop off alpha values (if applicable)
imgnew = imgarr.copy()[:,:,:3]
positive_temps = []
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
elif temp_C > MIN_TEMP:
positive_temps.append(temp_C)
# 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, positive_temps
@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, ptemps = 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 = 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, ptemps