137 lines
3.6 KiB
Python
137 lines
3.6 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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@jit(nopython=True)
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def rg_ratio_normalize(
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imgarr,
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I_Darkcurrent,
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f_stop,
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exposure_time,
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ISO,
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MIN_TEMP,
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MAX_TEMP
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):
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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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# copy image into new array & chop off alpha values (if applicable)
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imgnew = imgarr.copy()[:,:,:3]
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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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# normalize R & G pixels
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g_norm = (px[1] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
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r_norm = (px[2] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
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# apply camera calibration func
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temp_C = pyrometry_calibration_formula(g_norm, r_norm, default=MIN_TEMP)
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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 = MIN_TEMP
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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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# min intensity = 0
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pix_i = temp_C - MIN_TEMP
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imgnew[i][j] = [pix_i, pix_i, pix_i]
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# imgnew[i][j] = [0, g_norm, r_norm]
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return imgnew, tmin, tmax
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@jit(nopython=True)
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def pyrometry_calibration_formula(i_ng, i_nr, default=24.0):
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"""
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Given the green-red ratio, calculates an approximate temperature
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in Celsius. Defaults to room temperature if there's an error.
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"""
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try:
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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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except:
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return default
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def ratio_pyrometry_pipeline(
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file_bytes,
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# camera settings
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I_Darkcurrent: float,
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exposure_time: float,
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f_stop: float,
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ISO: float,
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# pyrometry config
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MAX_TEMP: float,
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MIN_TEMP: float,
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smoothing_radius: int,
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key_entries: int
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):
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# read image & crop
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img_orig = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED)
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# img = img[y1:y2, x1:x2]
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img, tmin, tmax = rg_ratio_normalize(
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img_orig,
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I_Darkcurrent,
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f_stop,
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exposure_time,
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ISO,
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MIN_TEMP,
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MAX_TEMP
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)
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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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# --- Generate temperature key ---
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# adjust max & min temps to be the same as the image
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# Generate key
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# step = (tmax - tmin) / (key_entries-1)
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step = (MAX_TEMP - MIN_TEMP) / (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_temp = MIN_TEMP + (i * step)
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res_color = res_temp / MAX_TEMP * 255
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temps.append(math.floor(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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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[2]}, {c[1]}, {c[0]})"
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# original, transformed, legend
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return img_orig, img_jet, tempkey
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