From 77125a5e9cad1831136b07b3f16b6e21cad2b141 Mon Sep 17 00:00:00 2001
From: Tom Selier <tbj.selier@gmail.com>
Date: Fri, 29 Sep 2023 17:56:46 +0200
Subject: [PATCH] added full bgr and hsv to distribution

---
 .../algorithms/distribution_test.py           | 59 +++++++++++++------
 1 file changed, 40 insertions(+), 19 deletions(-)

diff --git a/src/experiments/algorithms/distribution_test.py b/src/experiments/algorithms/distribution_test.py
index 8a08044..58cfcb2 100644
--- a/src/experiments/algorithms/distribution_test.py
+++ b/src/experiments/algorithms/distribution_test.py
@@ -47,9 +47,13 @@ def calcNormalFunc(mean, sd, len):
 DATASET_PATH = "dataset\\"
 BARK_TYPES = 8
 
-averages = [[] for x in range(BARK_TYPES)]
-weights = [[] for x in range(BARK_TYPES)] 
-variances = [[] for x in range(BARK_TYPES)] 
+avgs_bgr = [[[],[],[]] for x in range(BARK_TYPES)]
+wgts_bgr = [[[],[],[]] for x in range(BARK_TYPES)]
+
+avgs_hsv = [[[],[],[]] for x in range(BARK_TYPES)]
+wgts_hsv = [[[],[],[]] for x in range(BARK_TYPES)]
+
+labels = ["" for x in range(BARK_TYPES)]
            
 i = -1
 last_name = ""
@@ -58,27 +62,44 @@ for file in os.listdir(DATASET_PATH):
     if(name != last_name):
         last_name = name
         i += 1
+        labels[i] = name
 
-    image = cv2.imread(os.path.join(DATASET_PATH + file), 1)
-    assert image is not None, "Something went wrong"
+    im_bgr = cv2.imread(os.path.join(DATASET_PATH + file), 1)
+    assert im_bgr is not None, "Something went wrong"
+    im_hsv = cv2.cvtColor(im_bgr, cv2.COLOR_BGR2HSV)
 
-    # Weighted average
-    averages[i].append(np.mean(image[:, :, 0]))
-    weights[i].append(len(image[:, :, 0]))
+    for j in range(3):
+        avgs_bgr[i][j].append(np.mean(im_bgr[:, :, j]))
+        wgts_bgr[i][j].append(len(im_bgr[:, :, j]))
+
+        avgs_hsv[i][j].append(np.mean(im_hsv[:, :, j]))
+        wgts_hsv[i][j].append(len(im_hsv[:, :, j]))
     
-    # print()
 
-## Voor gewogen std: 
-## https://stackoverflow.com/questions/2413522/weighted-standard-deviation-in-numpy
-weighted_avg = np.zeros(BARK_TYPES)
-weighted_std = np.zeros(BARK_TYPES)
+w_avg_bgr = [[0, 0, 0] for x in range(BARK_TYPES)]
+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?
 for i in range(BARK_TYPES):
-    weighted_avg[i] = np.average(averages[i], weights=weights[i], axis=0)
-    weighted_std[i] = calcWeightedStd(averages[i], weights[i])
+    for j in range(3):
+        w_avg_bgr[i][j] = np.average(avgs_bgr[i][j], weights=wgts_bgr[i][j], axis=0)
+        w_std_bgr[i][j] = calcWeightedStd(avgs_bgr[i][j], wgts_bgr[i][j])
 
-print("Weighted averages: ")
-print(weighted_avg)
+        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])
 
-print("Weighted standard deviation: ")
-print(weighted_std)
+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])
+
+    print("Weighted averages HSV: ")
+    print(w_avg_hsv[i])
+    print("Weighted standard deviation HSV: ")
+    print(w_std_hsv[i])
+    print()