restructure

examples/pyrometry/README.md

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+
+## Issues
+
+### Values too high
+
+This is supposed to be 1200°C, but is yielding much higher values:
+
+```
+
+DSC1022 (Dennis Calibration Image, 1200°C)
+
+RED     GREEN   RNORM   GNORM   G_n/R_n RES TEMP
+252     84      28.14   8.79    0.3123  2007.8543287627099
+252     77      28.14   7.98    0.2837  1904.7813013618734
+231     67      25.72   6.83    0.2656  1837.354974958358
+215     61      23.88   6.14    0.2571  1805.2478789012494
+182     48      20.08   4.64    0.2312  1704.0179576792798
+103     17      10.98   1.07    0.0976  1098.5597969276637
+97      15      10.29   0.84    0.0817  1015.7460683439863
+63      6       6.37    -0.2    -0.0307 dropped
+
+```

examples/pyrometry/base64_test.py

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+import base64
+import cv2 as cv
+
+img = cv.imread('01-0001-cropped.png')
+
+print(img[0:100])
+
+# print(base64.b64encode(img).decode()[0:5_000])

examples/pyrometry/edge_detection.py

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+# MONOCHROME EDGE DETECTION
+
+import cv2 as cv
+import numpy as np
+
+# edge-detection kernel amplification
+AMPLIFIER=9
+
+MIN_INTENSITY=100
+
+# file = '01-0001-cropped.png'
+file = 'streaktest.png'
+file_name = file.split(".")[0]
+file_ext = file.split(".")[1]
+
+img = cv.imread(file)
+
+img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+
+kernel = np.array([
+    [-1, -1, -1],
+    [-1, AMPLIFIER, -1],
+    [-1, -1, -1],
+])
+img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
+
+cv.imwrite(f'{file_name}-edge-detection.{file_ext}', img)

examples/pyrometry/formula_testing.py

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+import math
+
+rg_value_sets = {
+    "firebrand_test.png": [
+        (219, 7),
+        (227, 14),
+        (166, 14),
+        (197, 10),
+        (230, 25),
+        (228, 17),
+        (218, 17),
+        (221, 15),
+        (210, 22),
+        (229, 17),
+    ],
+
+    "streaktest.png": [
+        (50, 11),
+        (51, 12),
+        (52, 10),
+        (240, 115),
+        (254, 127),
+    ],
+
+    "ember_orange.png": [
+        (240, 147),
+        (235, 102),
+        (223, 103),
+        (232, 103),
+        (103, 34),
+        (128, 47),
+        (92, 27),
+    ],
+
+    "test5.png": [
+        (164, 49),
+        (177, 57),
+        (181, 58),
+        (156, 46),
+        (150, 41),
+        (151, 44),
+    ],
+
+    "DSC1022 (Dennis Calibration Image, 1200°C)": [
+        (252, 84),
+        (252, 77),
+        (231, 67),
+        (215, 61),
+        (182, 48),
+        (103, 17),
+        (97, 15),
+        (63, 6),
+    ]
+
+}
+
+# Settings
+I_Darkcurrent = 7.7
+exposure_time = 0.500
+f_stop = 2.4
+ISO = 100 # basically brightness
+
+def pyrometry_calibration_formula(i_ng, i_nr):
+    """
+    Given the green-red ratio, calculates an approximate temperature 
+    in Celsius. 
+    """
+    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 'dropped'
+
+
+def tprint(*items):
+    for item in items:
+        print(item, end="\t")
+    print()
+
+
+def grtemp(px):
+    r_norm = (px[0] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
+    g_norm = (px[1] - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
+    res = pyrometry_calibration_formula(g_norm, r_norm)
+    tprint(
+        px[0], 
+        px[1], 
+        # round(px[0] / px[1], 2),
+        round(r_norm, 2),
+        round(g_norm, 2),
+        round(g_norm / r_norm, 4),
+        res, 
+    )
+
+for (key, val) in rg_value_sets.items():
+    print(f"\n{key}\n")
+    tprint('RED', 'GREEN', 'RNORM', 'GNORM', 'G_n/R_n', 'RES TEMP')
+    for rg in val:
+        grtemp(rg)

examples/pyrometry/grayscale_pyrometry.py

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+import cv2 as cv
+import numpy as np
+
+img = cv.imread('01-0001.png', 0)
+
+kernel = np.array([
+    [1.3]
+])
+img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
+
+img = cv.applyColorMap(img, cv.COLORMAP_JET)
+cv.imwrite('01-0001-transformed-grayscale.png', img)

examples/pyrometry/ratio_pyrometry.py

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+import math
+import cv2 as cv
+import numpy as np
+from numba import jit
+import json
+
+# camera settings
+file = '01-0001.png'
+I_Darkcurrent = 150.5
+exposure_time = 0.500
+f_stop = 2.4
+ISO = 64 # basically brightness
+
+# pyrometry config
+MAX_TEMP = 1200
+MIN_TEMP = 60
+# original range from paper
+# MAX_GR_RATIO = 1200
+# MIN_GR_RATIO = 600
+
+# 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):
+    # set max & min to most extreme values, 
+    # work up & down respectively from there
+    tmin = MAX_TEMP
+    tmax = 0
+
+    imgnew = imgarr
+    for i in range(len(imgarr)):
+        for j in range(len(imgarr[i])):
+            px = imgarr[i][j]
+            r_norm = normalization_func(px[0])
+            g_norm = normalization_func(px[1])
+
+            # 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 normalization_func(i):
+    """
+    does something to the pixels that i don't understand lol
+    """
+    return (i - I_Darkcurrent) * (f_stop ** 2) / (ISO * exposure_time)
+
+
+@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) ** 2
+    ) + 4466.5 * math.log10(
+        (i_ng / i_nr)
+    ) + 3753.5
+
+
+# read image & crop
+file_name = file.split(".")[0]
+file_ext = file.split(".")[1]
+img = cv.imread(file)
+img = img[y1:y2, x1:x2]
+cv.imwrite(f'{file_name}-cropped.{file_ext}', img)
+
+# img = cv.imread('ember_test.png')
+
+img, tmin, tmax = rg_ratio_normalize(img)
+
+print(f"min: {tmin}°C")
+print(f"max: {tmax}°C")
+
+# 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(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)
+# cv.imwrite(f'{file_name}-key.{file_ext}', key_img_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]})"
+
+print(json.dumps(tempkey, indent=4))