139 lines
3.4 KiB
Python
139 lines
3.4 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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import json
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# camera settings
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file = '01-0001.png'
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I_Darkcurrent = 150.5
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exposure_time = 0.500
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f_stop = 2.4
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ISO = 64 # basically brightness
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# pyrometry config
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MAX_TEMP = 1200
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MIN_TEMP = 60
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# original range from paper
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# MAX_GR_RATIO = 1200
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# MIN_GR_RATIO = 600
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# Cropping config
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x1 = 420
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x2 = 1200
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y1 = 400
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y2 = -1
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# post-processing
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smoothing_radius = 2
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# temperature key generation
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key_entries = 6
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@jit(nopython=True)
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def rg_ratio_normalize(imgarr):
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# set max & min to most extreme values,
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# work up & down respectively from there
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tmin = MAX_TEMP
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tmax = 0
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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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temp_C = pyrometry_calibration_formula(g_norm, r_norm)
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# remove pixels outside calibration range
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if MAX_TEMP != None and temp_C > MAX_TEMP or MIN_TEMP != None and temp_C < MIN_TEMP:
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temp_C = 0
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# update min & max
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if temp_C < tmin and temp_C >= 0:
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tmin = temp_C
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if temp_C > tmax:
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tmax = temp_C
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imgnew[i][j] = [temp_C, temp_C, temp_C]
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return imgnew, tmin, tmax
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@jit(nopython=True)
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def normalization_func(i):
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"""
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does something to the pixels that i don't understand lol
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"""
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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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"""
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Given the green-red ratio, calculates an approximate temperature
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in Celsius.
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"""
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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) ** 2
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) + 4466.5 * math.log10(
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(i_ng / i_nr)
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) + 3753.5
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# read image & crop
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file_name = file.split(".")[0]
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file_ext = file.split(".")[1]
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img = cv.imread(file)
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img = img[y1:y2, x1:x2]
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cv.imwrite(f'{file_name}-cropped.{file_ext}', img)
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# img = cv.imread('ember_test.png')
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img, tmin, tmax = rg_ratio_normalize(img)
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print(f"min: {tmin}°C")
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print(f"max: {tmax}°C")
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# build & apply smoothing conv kernel
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k = []
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for i in range(smoothing_radius):
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k.append([1/(smoothing_radius**2) for i in range(smoothing_radius)])
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kernel = np.array(k)
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img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
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# write colormapped image
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img_jet = cv.applyColorMap(img, cv.COLORMAP_JET)
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cv.imwrite(f'{file_name}-cropped-transformed-ratio.{file_ext}', img_jet)
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# --- Generate temperature key ---
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# adjust max & min temps to be the same as the image
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# tmin_adj = tmin / (smoothing_radius ** 2)
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# tmax_adj = tmax / (smoothing_radius ** 2)
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# Generate 6-step key
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step = (tmax - tmin) / (key_entries-1)
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temps = []
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key_img_arr = [[]]
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for i in range(key_entries):
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res_temp = tmin + (i * step)
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res_color = (tmax - (i * step)) / MAX_TEMP * 255
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temps.append(res_temp)
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key_img_arr[0].append([res_color, res_color, res_color])
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key_img = np.array(key_img_arr).astype(np.uint8)
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key_img_jet = cv.applyColorMap(key_img, cv.COLORMAP_JET)
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# cv.imwrite(f'{file_name}-key.{file_ext}', key_img_jet)
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tempkey = {}
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for i in range(len(temps)):
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c = key_img_jet[0][i]
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tempkey[temps[i]] = f"rgb({c[0]}, {c[1]}, {c[2]})"
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print(json.dumps(tempkey, indent=4))
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