src/experiments/algorithms/data/data.csv

@@ -1,111 +0,0 @@
-Accasia_out
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-2042.4658575057983,3.370405705455113,41.08128356933594,2.6347973287004307,0.025092723959190124,15.206190719269216,606
-679.9872843027115,3.2849627261000554,19.78511619567871,2.3892941334247926,0.020702168241971068,4.285348826088011,207
-702.1195831298828,2.901320591445797,12.382240295410156,1.3507806557012718,0.01787308056661782,4.325285497121513,242
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-991.9575394392014,3.4562980468264857,37.2266845703125,3.4535018683730523,0.02128367463146249,6.108414619229734,287
-883.6591415405273,2.814201087708686,19.852262496948242,1.35472176718185,0.01984180409200252,6.230326484888792,314
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-905.6036241054535,3.1554133244092455,24.95030975341797,2.079331380141301,0.01953536727326153,5.606650407426059,287
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-2635.2527647018433,3.4313187040388584,21.754484176635742,2.423580701045881,0.03490584164925773,26.80768638662994,768
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-780.677412033081,3.500795569655072,29.299978256225586,3.1758027558377497,0.017996931807743595,4.0133157931268215,223
-Berk_out
-2687.594747185707,3.7172818080023613,48.48632049560547,3.884212269938133,0.02503235707572181,18.098394165746868,723
-2449.5360144376755,3.9129968281752006,48.72830581665039,4.190141018929445,0.026578323430598925,16.638030467554927,626
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-Eik_out
-1579.444641828537,3.6816891417914617,19.227346420288086,2.4975664779859987,0.031246154553893006,13.4046003036201,429
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-1097.9404437541962,2.9998372780169293,12.264142990112305,1.581511211694125,0.023072358251350823,8.444483119994402,366
-1291.582367658615,3.2864691289023287,28.83696937561035,2.3913841440343515,0.022361759129560933,8.788171337917447,393
-1363.1331936120987,3.549826025031507,19.18490219116211,2.6828781845914205,0.022771707088395488,8.744335521943867,384
-Els_out
-1359.8641113042831,3.2767809910946584,19.266313552856445,1.897638219600126,0.03115534636539867,12.929468741640449,415
-1134.7650756835938,3.327756820186492,18.936323165893555,1.7635501819834738,0.03266596192182684,11.13909301534295,341
-1632.902286529541,3.5040821599346375,24.11359214782715,2.5618395036682124,0.03067660743626466,14.295299065299332,466
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-1107.7055238485336,3.886686048591346,22.485553741455078,2.9948413624608015,0.026233021696016455,7.476411183364689,285
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-1111.8425114154816,3.3897637543154926,34.425537109375,2.6968539571918693,0.02739228969780592,8.984671020880342,328
-Esdoorn_out
-1804.0408366918564,3.280074248530648,16.878116607666016,2.014352156832684,0.023109257851134647,12.710091818124056,550
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-2109.4739229679108,3.3859934558072404,22.446571350097656,1.9491651806275798,0.024649366696049755,15.356555451638997,623
-566.1849731206894,3.494968969880799,14.277800559997559,2.26332032519058,0.02264520879489956,3.668523824773729,162
-654.3276780843735,3.255361582509321,14.174254417419434,2.0223312092105425,0.0228689360798369,4.596656152047217,201
-2245.0964748859406,3.6804860244031814,24.625778198242188,2.75325816629717,0.028883854125733258,17.619151016697288,610
-2144.3980325460434,3.9060073452569095,23.537641525268555,2.982770847358778,0.0322847970131995,17.724353560246527,549
-716.7942097187042,3.303199123127669,12.021895408630371,1.8694689392752937,0.022289771651033706,4.836880448274314,217
-512.756842136383,3.308108658944407,12.388520240783691,1.9337408376098448,0.022146687284111975,3.4327365290373564,155
-203.3936928510666,2.676232800671929,9.652430534362793,1.0712162703218076,0.01939073234404388,1.473695658147335,76
-69.72195136547089,2.9050813068946204,8.56438159942627,1.3172613562659046,0.019768514127160113,0.4744443390518427,24
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-1182.883133649826,3.360463447868824,19.758787155151367,2.1385583241671227,0.022289208096811886,7.845801250077784,352
-Es_out
-321.85712587833405,2.7746303955028795,8.890114784240723,1.3376489632824087,0.01832420542707731,2.125607829540968,116
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-338.75891745090485,2.7996604748008664,9.217413902282715,1.283988791527709,0.018358958323201363,2.221433957107365,121
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-Linde_out
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-1867.9105838537216,3.612979852715129,32.11227035522461,2.97824320863418,0.024062555778692377,12.440341337583959,517
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-2013.289783000946,3.653883453722225,25.39375114440918,2.890201927887927,0.024696742919714826,13.60790534876287,551
-Plataan_out
-393.0376785993576,6.238693311100914,54.77334213256836,8.288615256835522,0.018721998817036078,1.1794859254732728,63
-121.42510282993317,3.0356275707483293,6.407865047454834,1.2202699276482052,0.018214209564030172,0.7285683825612068,40
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-437.9166510105133,2.9588962906115763,11.90050983428955,1.4047910909894616,0.01991794491186738,2.9478558469563723,148
-165.1281417608261,2.580127215012908,4.76331090927124,0.7642263750567146,0.018476538331015036,1.1824984531849623,64
-232.94678628444672,2.875886250425268,9.826898574829102,1.3727227350017257,0.019708929284487243,1.5964232720434666,81
-332.89813327789307,3.78293333270333,22.732511520385742,3.045459303553181,0.019926275486465205,1.753512242808938,88
-375.2972539663315,4.415261811368605,24.255054473876953,4.276442351754282,0.017834563169847516,1.515937869437039,85
-55.11076760292053,3.061709311273363,9.913771629333496,1.935650105177096,0.017019378021359444,0.30634880438447,18
-38.28573513031006,2.3928584456443787,3.611070394515991,0.5145855975780511,0.014993195189163089,0.23989112302660942,16
-599.6391468048096,5.259992515831663,40.99736404418945,5.811482494116048,0.01938449298113323,2.2098321998491883,114
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-316.32959711551666,4.108176585915801,40.63800048828125,6.427567028038508,0.017561782018414566,1.3522572154179215,77
-138.6963803768158,2.52175237048756,6.668946743011475,0.8272403842232803,0.016272347149523823,0.8949790932238102,55
-241.65850698947906,3.0207313373684883,19.399702072143555,2.1353345471026124,0.017683570575900375,1.41468564607203,80

src/experiments/algorithms/sift_plot.py

@@ -1,86 +0,0 @@
-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

@@ -2,14 +2,13 @@ import numpy as np
 import cv2
 import os
 import matplotlib.pyplot as plt
-import csv
-import pandas as pd
 
-DATASET_PATH = "C:\\Users\\Tom\\Downloads\\Dataset_out\\"
+DATASET_PATH = "C:\\Users\\tomse\\Downloads\\Dataset\\"
-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 = .25
+SCALE = 1
+
+# Plataan, Berk, Accasia
 
 sift = cv2.SIFT.create(enable_precise_upscale=True)
 
@@ -17,9 +16,6 @@ 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 ##
@@ -54,23 +50,17 @@ 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 arrays ##    
+    ## Increment folder ##    
     i += 1
 
     if(i == EARLY_BREAK):
@@ -78,36 +68,8 @@ 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)
@@ -118,11 +80,11 @@ with open(CSV_PATH + "data.csv" , 'w', newline='') as file:
 # ax.grid(True)
 # plt.show()
 
-# ax.scatter(tot_magnitudes[2], max_magnitudes[2], label='Berk', alpha=0.7)
+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[3], max_magnitudes[3], label='Els', alpha=0.7)
-# ax.scatter(tot_magnitudes[7], max_magnitudes[7], label='Plataan', alpha=0.7)
+ax.scatter(tot_magnitudes[7], max_magnitudes[7], label='Plataan', alpha=0.7)
-# ax.set_xlabel("Total magnitude")
+ax.set_xlabel("Total magnitude")
-# ax.set_ylabel("Maximal magnitude")
+ax.set_ylabel("Maximal magnitude")
-# ax.legend() 
+ax.legend() 
-# ax.grid(True)
+ax.grid(True)
-# plt.show()
+plt.show()

src/experiments/template_extraction/script.py

@@ -92,16 +92,13 @@ 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.lower().endswith('.jpg'):
+    if folder.endswith('.jpg'):
         continue
 
     if SAVE:
@@ -150,12 +147,10 @@ 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:
@@ -251,10 +246,5 @@ 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()