firelab-general/ratio_pyrometry.py

138 lines
3.5 KiB
Python

import math
import cv2 as cv
import numpy as np
from numba import jit
import json
# Cropping config
# x1 = 420
# x2 = 1200
# y1 = 400
# y2 = -1
# post-processing
smoothing_radius = 2
# temperature key generation
key_entries = 6
@jit(nopython=True)
def rg_ratio_normalize(
imgarr,
I_Darkcurrent,
f_stop,
exposure_time,
ISO,
MIN_TEMP,
MAX_TEMP
):
# set max & min to most extreme values,
# work up & down respectively from there
tmin = MAX_TEMP
tmax = 0
imgnew = imgarr.copy()
for i in range(len(imgarr)):
for j in range(len(imgarr[i])):
px = imgarr[i][j]
# normalize R & G pixels
r_norm = (px[0] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
g_norm = (px[1] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
# apply camera calibration func
temp_C = pyrometry_calibration_formula(g_norm, r_norm)
# remove pixels outside calibration range
if MAX_TEMP != None and temp_C > MAX_TEMP or MIN_TEMP != None and temp_C < MIN_TEMP:
temp_C = 0
# update min & max
if temp_C < tmin and temp_C >= 0:
tmin = temp_C
if temp_C > tmax:
tmax = temp_C
imgnew[i][j] = [temp_C, temp_C, temp_C]
return imgnew, tmin, tmax
@jit(nopython=True)
def pyrometry_calibration_formula(i_ng, i_nr):
"""
Given the green-red ratio, calculates an approximate temperature
in Celsius.
"""
return 362.73 * math.log10(
(i_ng/i_nr) ** 3
) + 2186.7 * math.log10(
(i_ng/i_nr) ** 3
) + 4466.5 * math.log10(
(i_ng / i_nr) ** 3
) + 3753.5
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
):
# read image & crop
img_orig = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED)
# img = img[y1:y2, x1:x2]
img, tmin, tmax = rg_ratio_normalize(
img_orig,
I_Darkcurrent,
f_stop,
exposure_time,
ISO,
MIN_TEMP,
MAX_TEMP
)
# 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)
# cv.imwrite(f'{file_name}-cropped-transformed-ratio.{file_ext}', img_jet)
# --- Generate temperature key ---
# adjust max & min temps to be the same as the image
# tmin_adj = tmin / (smoothing_radius ** 2)
# tmax_adj = tmax / (smoothing_radius ** 2)
# Generate 6-step key
step = (tmax - tmin) / (key_entries-1)
temps = []
key_img_arr = [[]]
for i in range(key_entries):
res_temp = tmin + (i * step)
res_color = (tmax - (i * step)) / MAX_TEMP * 255
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[0]}, {c[1]}, {c[2]})"
# original, transformed, legend
return img_orig, img_jet, tempkey