Added more statistics to sift

src/experiments/algorithms/data/data.csv

@@ -0,0 +1,111 @@
+Accasia_out
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+Es_out
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+Plataan_out
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src/experiments/algorithms/sift_plot.py

@@ -0,0 +1,86 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import csv
+
+def isFloat(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+DATA_PATH = "C:\\Users\\Tom\\Desktop\\Files\\Repositories\\EV5_Beeldherk_Bomen\\src\\experiments\\algorithms\\data\\data.csv"
+BARK_TYPES = 8
+
+tot_mag = [["", []] for x in range(BARK_TYPES)]
+avg_mag = [["", []] for x in range(BARK_TYPES)]
+max_mag = [["", []] for x in range(BARK_TYPES)]
+std_mag = [["", []] for x in range(BARK_TYPES)]
+avg_rep = [["", []] for x in range(BARK_TYPES)]
+max_rep = [["", []] for x in range(BARK_TYPES)]
+counts = [["", []] for x in range(BARK_TYPES)]
+i = 0
+
+with open(DATA_PATH, 'r') as file:
+    reader = csv.reader(file, delimiter=',')
+    for row in reader:
+        if isFloat(row[0]):
+            tot_mag[i-1][1].append(float(row[0]))
+            avg_mag[i-1][1].append(float(row[1]))
+            max_mag[i-1][1].append(float(row[2]))
+            std_mag[i-1][1].append(float(row[3]))
+            avg_rep[i-1][1].append(float(row[4]))
+            max_rep[i-1][1].append(float(row[5]))
+            counts[i-1][1].append(float(row[6]))
+        else:
+            tot_mag[i][0] = row[0]
+            avg_mag[i][0] = row[0]
+            max_mag[i][0] = row[0]
+            std_mag[i][0] = row[0]
+            avg_rep[i][0] = row[0]
+            max_rep[i][0] = row[0]
+            counts[i][0] = row[0]
+            i += 1
+
+fig, axs = plt.subplots(2, 3)
+
+for i in range(BARK_TYPES):
+    axs[0, 0].scatter(tot_mag[i][1], avg_mag[i][1], label=tot_mag[i][0], alpha=0.6)
+    axs[0, 1].scatter(tot_mag[i][1], max_mag[i][1], label=tot_mag[i][0], alpha=0.6)
+    axs[1, 0].scatter(tot_mag[i][1], std_mag[i][1], label=tot_mag[i][0], alpha=0.6)
+    axs[1, 1].scatter(tot_mag[i][1], counts[i][1], label=tot_mag[i][0], alpha=0.6)
+    axs[0, 2].scatter(tot_mag[i][1], avg_rep[i][1], label=tot_mag[i][0], alpha=0.6)
+    axs[1, 2].scatter(avg_rep[i][1], max_rep[i][1], label=tot_mag[i][0], alpha=0.6)
+
+
+axs[0, 0].set_xlabel("Total")
+axs[0, 0].set_ylabel("Average")
+axs[0, 0].grid()
+axs[0, 0].legend()
+
+axs[0, 1].set_xlabel("Total")
+axs[0, 1].set_ylabel("Maximum")
+axs[0, 1].grid()
+axs[0, 1].legend()
+
+axs[1, 0].set_xlabel("Total")
+axs[1, 0].set_ylabel("Standard deviation")
+axs[1, 0].grid()
+axs[1, 0].legend()
+
+axs[1, 1].set_xlabel("Total")
+axs[1, 1].set_ylabel("Count")
+axs[1, 1].grid()
+axs[1, 1].legend()
+
+axs[0, 2].set_xlabel("Total")
+axs[0, 2].set_ylabel("Average response")
+axs[0, 2].grid()
+axs[0, 2].legend()
+
+axs[1, 2].set_xlabel("Average response")
+axs[1, 2].set_ylabel("Max response")
+axs[1, 2].grid()
+axs[1, 2].legend()
+
+plt.show()

src/experiments/algorithms/sift_v3.py

@@ -2,13 +2,14 @@ import numpy as np
 import cv2
 import os
 import matplotlib.pyplot as plt
+import csv
+import pandas as pd
 
-DATASET_PATH = "C:\\Users\\tomse\\Downloads\\Dataset\\"
+DATASET_PATH = "C:\\Users\\Tom\\Downloads\\Dataset_out\\"
+CSV_PATH = "C:\\Users\\Tom\\Desktop\\Files\\Repositories\\EV5_Beeldherk_Bomen\\src\\experiments\\algorithms\\data\\"
 DATASET_FOLDERS_LEN = len(os.listdir(DATASET_PATH))
 EARLY_BREAK = 0
-SCALE = 1
-
-# Plataan, Berk, Accasia
+SCALE = .25
 
 sift = cv2.SIFT.create(enable_precise_upscale=True)
 
@@ -16,6 +17,9 @@ sift = cv2.SIFT.create(enable_precise_upscale=True)
 max_magnitudes = [[] for x in range(DATASET_FOLDERS_LEN)]
 avg_magnitudes = [[] for x in range(DATASET_FOLDERS_LEN)]
 tot_magnitudes = [[] for x in range(DATASET_FOLDERS_LEN)]
+std_magnitudes = [[] for x in range(DATASET_FOLDERS_LEN)]
+max_responses = [[] for x in range(DATASET_FOLDERS_LEN)]
+avg_responses = [[] for x in range(DATASET_FOLDERS_LEN)]
 counts = [[] for x in range(DATASET_FOLDERS_LEN)]
 
 ## Create other variables ##
@@ -50,17 +54,23 @@ for folder in os.listdir(DATASET_PATH):
         tot_magnitudes[i].append(np.sum(magnitudes))
         max_magnitudes[i].append(np.amax(magnitudes))
         avg_magnitudes[i].append(np.sum(magnitudes)/len(kp))
+        std_magnitudes[i].append(np.std(magnitudes))
         
         ## Number of keypoints ##
         counts[i].append(len(kp))
 
+        ## Response ##
+        responses = [keypoint.response for keypoint in kp]
+        max_responses[i].append(np.sum(responses))
+        avg_responses[i].append(np.mean(responses))
+
         # cv2.imshow("Opencv tech", image)
         # cv2.waitKey(0)
     
     ## Store labels ##
     labels[i] = folder
 
-    ## Increment folder ##    
+    ## Increment arrays ##    
     i += 1
 
     if(i == EARLY_BREAK):
@@ -68,8 +78,36 @@ for folder in os.listdir(DATASET_PATH):
 
 print("Done!")
 
+## Pandas ##
+with open(CSV_PATH + "data.csv" , 'w', newline='') as file:
+    for i in range(len(labels)):
+        file.write(labels[i] + '\n')
+        for j in range(len(tot_magnitudes[i])):
+            file.write(str(tot_magnitudes[i][j]) + ',')
+            file.write(str(avg_magnitudes[i][j]) + ',')
+            file.write(str(max_magnitudes[i][j]) + ',')
+            file.write(str(std_magnitudes[i][j]) + ',')
+            file.write(str(avg_responses[i][j]) + ',')
+            file.write(str(max_responses[i][j]) + ',')
+            file.write(str(counts[i][j]) + '\n')
+
+## CSV ##
+# with open(CSV_PATH + "data.csv" , 'w', newline='') as file:
+    # writer = csv.writer(file, delimiter=',')
+
+    # for i in range(len(tot_magnitudes)):
+        # writer.writerow(tot_magnitudes[i])
+        # writer.writerow(avg_magnitudes[i])
+        # writer.writerow(max_magnitudes[i])
+        # writer.writerow(counts[i])
+        # writer.writerow('')
+
+    # writer.writerows(max_magnitudes)
+    # writer.writerows(avg_magnitudes)
+    # writer.writerows(counts)
+
 ## Plots ##
-fig, ax = plt.subplots()
+# fig, ax = plt.subplots()
 # for i in range(DATASET_FOLDERS_LEN):
 #     ax.scatter(tot_magnitudes[i], max_magnitudes[i],label=labels[i], alpha=0.7)
 # ax.scatter(avg_magnitudes[0], max_magnitudes[0], label=labels[0], alpha=0.7)
@@ -80,11 +118,11 @@ fig, ax = plt.subplots()
 # ax.grid(True)
 # plt.show()
 
-ax.scatter(tot_magnitudes[2], max_magnitudes[2], label='Berk', alpha=0.7)
-ax.scatter(tot_magnitudes[3], max_magnitudes[3], label='Els', alpha=0.7)
-ax.scatter(tot_magnitudes[7], max_magnitudes[7], label='Plataan', alpha=0.7)
-ax.set_xlabel("Total magnitude")
-ax.set_ylabel("Maximal magnitude")
-ax.legend() 
-ax.grid(True)
-plt.show()
+# ax.scatter(tot_magnitudes[2], max_magnitudes[2], label='Berk', alpha=0.7)
+# ax.scatter(tot_magnitudes[3], max_magnitudes[3], label='Els', alpha=0.7)
+# ax.scatter(tot_magnitudes[7], max_magnitudes[7], label='Plataan', alpha=0.7)
+# ax.set_xlabel("Total magnitude")
+# ax.set_ylabel("Maximal magnitude")
+# ax.legend() 
+# ax.grid(True)
+# plt.show()

src/experiments/template_extraction/script.py

@@ -92,13 +92,16 @@ detector_params.adaptiveThreshWinSizeMax = 150 # Takes longer, but better chance
 dictionary = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_ARUCO_ORIGINAL)  
 detector = cv2.aruco.ArucoDetector(dictionary, detector_params)
 
+images_converted = 0
+images_skipped = 0
+
 ### IMAGE CONVERSIE ###
 for folder in os.listdir(input_directory):
     if folder.endswith('.py'):
         print("Skipping", folder)
         continue
     
-    if folder.endswith('.jpg'):
+    if folder.lower().endswith('.jpg'):
         continue
 
     if SAVE:
@@ -147,10 +150,12 @@ for folder in os.listdir(input_directory):
         if ids is None:
             print("Skipping: ", filename)
             print("=============================================")
+            images_skipped += 1
             continue
         if len(ids) != 4:
             print("Skipping: ", filename)
             print("=============================================")
+            images_skipped += 1
             continue 
 
         if VERBOSE:
@@ -246,5 +251,10 @@ for folder in os.listdir(input_directory):
             cv2.waitKey(0)
         if VERBOSE:
             print("=============================================")
+        images_converted += 1
+
+if VERBOSE:
+    print("%d van de %d succesvol"
+          %(images_converted, (images_converted+images_skipped)))
 
 cv2.destroyAllWindows()