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+241.65850698947906,3.0207313373684883,19.399702072143555,2.1353345471026124,0.017683570575900375,1.41468564607203,80 diff --git a/src/experiments/algorithms/sift_plot.py b/src/experiments/algorithms/sift_plot.py new file mode 100644 index 0000000..6ac684c --- /dev/null +++ b/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() diff --git a/src/experiments/algorithms/sift_v3.py b/src/experiments/algorithms/sift_v3.py index ddef7fc..8e8dbb7 100644 --- a/src/experiments/algorithms/sift_v3.py +++ b/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() \ No newline at end of file +# 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() \ No newline at end of file