Added distribution plots

src/experiments/algorithms/distribution_test.py

@@ -3,21 +3,6 @@ import matplotlib.pyplot as plt
 import cv2
 import os
 import math
-
-def imgStats(img):
-    mean = np.zeros(3)
-    std = np.zeros(3)
-    for i in range(img.shape[2]):
-        mean[i] = np.mean(img[:, :, i])
-        std[i] = np.std(img[:, :, i])
-    return mean, std
-
-def isFloat(num):
-    try:
-        float(num)
-        return True
-    except ValueError:
-        return False
     
 def calcWeightedStd(values, weights):
     N = len(values)
@@ -81,7 +66,7 @@ w_std_bgr = [[0, 0, 0] for x in range(BARK_TYPES)]
 w_avg_hsv = [[0, 0, 0] for x in range(BARK_TYPES)]
 w_std_hsv = [[0, 0, 0] for x in range(BARK_TYPES)]
 
-#TODO: Mag de weighted std berekening met avgs?
+#TODO: Mag de weighted std berekening met avgs? Denk t nie
 for i in range(BARK_TYPES):
     for j in range(3):
         w_avg_bgr[i][j] = np.average(avgs_bgr[i][j], weights=wgts_bgr[i][j], axis=0)
@@ -90,16 +75,24 @@ for i in range(BARK_TYPES):
         w_avg_hsv[i][j] = np.average(avgs_hsv[i][j], weights=wgts_hsv[i][j], axis=0)
         w_std_hsv[i][j] = calcWeightedStd(avgs_hsv[i][j], wgts_hsv[i][j])
 
+
+fig, axs = plt.subplots(2,4)
+row = 0
+col = -1
+colours = ('b', 'g', 'r')
 for i in range(BARK_TYPES):
-    print("Tree: " + labels[i])
-    print("Weighted averages BGR: ")
-    print(w_avg_bgr[i])
-    print("Weighted standard deviation BGR: ")
-    print(w_std_bgr[i])
+    row = 1 if i > 3 else 0
+    col += 1
+    if i == 4: col = 0
 
-    print("Weighted averages HSV: ")
-    print(w_avg_hsv[i])
-    print("Weighted standard deviation HSV: ")
-    print(w_std_hsv[i])
-    print()
+    for j in range(3):
+        f = calcNormalFunc(w_avg_bgr[i][j], w_std_bgr[i][j], 255)
+        label = "$\mu:$ %d, $\sigma: %d$"%(w_avg_bgr[i][j],w_std_bgr[i][j])
+        axs[row, col].plot(f, c=colours[j], label=label)
+
+    axs[row, col].grid()
+    axs[row, col].legend()
+    axs[row, col].set_title(str(labels[i]))
+    
+plt.show()