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example/example_code-doc.md

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+# Documentation for `example_code.py`
+
+## `remove_dirt(image)`
+This function removes small dirt and noise from a binary image by closing small holes and removing small objects.
+
+Here is an example of how to use the function:
+
+```
+import skimage.morphology as morphology
+from skimage import data
+
+# Load a binary image
+image = data.coins() > 100
+
+# Remove dirt from the image
+cleaned_image = remove_dirt(image)
+```
+
+## `center_of_mass(X)`
+This function calculates the center of mass of a 2D shape defined by a set of points.
+
+Here is an example of how to use the function:
+
+```
+import numpy as np
+
+# Define a set of points that define a shape
+X = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
+
+# Calculate the center of mass of the shape
+center_of_mass = center_of_mass(X)
+
+# Print the center of mass
+print(center_of_mass)
+```
+
+The output will be `[0.5, 0.5]`, which is the coordinates of the center of the square defined by the points `X`.
+
+## `center_of_mass(X)`
+This function calculates the center of mass of a 2D shape defined by a set of points.
+
+Here is an example of how to use the function:
+
+```
+import numpy as np
+
+# Define a set of points that define a shape
+X = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
+
+# Calculate the center of mass of the shape
+center_of_mass = center_of_mass(X)
+
+# Print the center of mass
+print(center_of_mass)
+```
+
+The output will be `[0.5, 0.5]`, which is the coordinates of the center of the square defined by the points `X`.
+

example/example_code.py

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+def remove_dirt(image):
+    image = morphology.area_closing(image, area_threshold=250, connectivity=1)
+    # image = morphology.opening(image, morphology.square(5))
+
+    return image
+
+def calculate_area(countour):
+    c = np.expand_dims(countour.astype(np.float32), 1)
+    c = cv.UMat(c)
+    
+    return cv.contourArea(c)
+
+def center_of_mass(X):
+    x = X[:,0]
+    y = X[:,1]
+    g = (x[:-1]*y[1:] - x[1:]*y[:-1])
+    A = 0.5*g.sum()
+    cx = ((x[:-1] + x[1:])*g).sum()
+    cy = ((y[:-1] + y[1:])*g).sum()
+
+    return 1./(6*A)*np.array([cx,cy])
+
+img = remove_dirt(thresh_gray)
+
+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
+
+
+def pyrometry_calibration_formula(i_ng, i_nr):
+    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