src/experiments/algorithms/distribution.py

@@ -1,37 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import math
-
-mean = 125
-sd = 20
-N = 255
-f = np.zeros(N, dtype=np.longdouble)
-
-def calcNormalFunc(mean, sd, len):
-    f = np.zeros(len, dtype=np.longdouble)
-    
-    # calculate PDF
-    for x in range(len):
-        exp = (-(x - mean) ** 2)/(2 * sd ** 2)
-        f[x] = 1 / math.sqrt(2 * np.pi * sd** 20 ) * (math.exp(exp))
-
-    # normalize PDF
-    max = np.amax(f)
-    min = np.amin(f)
-    for x in range(len):
-        f[x] = (f[x] - min) / (max - min)
-
-    return f
-
-f = calcNormalFunc(mean, sd, N)
-
-plt.title("PDF: $\mu = %d$, $\sigma = %d$"%(mean, sd))    
-plt.plot(f)
-plt.grid()
-plt.xlim(0, 255)
-plt.ylim(0, 1.05)
-plt.show()
-
-print("Score at 1 sigma: %f"%f[sd+mean])
-print("Score at 2 sigma: %f"%f[2*sd+mean])
-print("Score at 3 sigma: %f"%f[3*sd+mean])

src/helpers/statistics.py

@@ -1,5 +1,4 @@
 import numpy as np
-import math
 
 def imgStats(img):
     mean = np.zeros(3)
@@ -7,20 +6,4 @@ def imgStats(img):
     for i in range(img.shape[2]):
         mean[i] = np.mean(img[:, :, i])
         std[i] = np.std(img[:, :, i])
-    return mean, std
-
-def calcNormalFunc(mean, sd, len):
-    f = np.zeros(len, dtype=np.longdouble)
-    
-    # calculate PDF
-    for x in range(len):
-        exp = (-(x - mean) ** 2)/(2 * sd ** 2)
-        f[x] = 1 / math.sqrt(2 * np.pi * sd** 20 ) * (math.exp(exp))
-
-    # normalize PDF
-    max = np.amax(f)
-    min = np.amin(f)
-    for x in range(len):
-        f[x] = (f[x] - min) / (max - min)
-
-    return f
+    return mean, std