diff --git a/src/experiments/algorithms/data/sift.csv b/src/experiments/algorithms/data/sift.csv index aa803bc..a3e1f97 100644 --- a/src/experiments/algorithms/data/sift.csv +++ b/src/experiments/algorithms/data/sift.csv @@ -1,111 +1,103 @@ -Accasia_out -698.7577929496765,3.1334430177115538,14.639884948730469,1.828372937292583,0.019404600608275344,4.3272259356454015,223 -835.5868207216263,3.2014820717303687,12.595267295837402,1.6907475606337812,0.019033726119218657,4.96780251711607,261 -1322.7272791862488,3.06897280553654,31.375614166259766,1.9465412537508602,0.020197111614083055,8.704955105669796,431 -616.1432831287384,2.948053986261906,10.182110786437988,1.3003369871725294,0.01828445773126121,3.8214516658335924,209 -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 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matplotlib.pyplot as plt import csv import pandas as pd -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)) +DATASET_PATH = "C:\\Users\\tomse\\Downloads\\processed" +CSV_PATH = "C:\\Users\\tomse\\Desktop\\Repositories\\EV5_Beeldherk_Bomen\\src\\experiments\\algorithms\\data\\" +DATASET_FOLDERS_LEN = 7+1 # Actually tree types EARLY_BREAK = 0 -SCALE = .25 +SCALE = 1 sift = cv2.SIFT.create(enable_precise_upscale=True) ## Create 2D lists ## -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)] +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 ## labels = ['' for x in range(DATASET_FOLDERS_LEN)] -i = 0 +i = -1 +last_name = "" -for folder in os.listdir(DATASET_PATH): - if not folder.endswith("_out"): - continue +for file in os.listdir(DATASET_PATH): + name = file.split('_')[0] + if last_name != name: + last_name = name + i += 1 if EARLY_BREAK: - print("Step %d/%d processing %s:"%(i+1, EARLY_BREAK, folder)) + print("Step %d/%d processing %s:"%(i+1, EARLY_BREAK, file)) else: - print("Step %d/%d processing %s:"%(i+1, DATASET_FOLDERS_LEN, folder)) + print("Step %d/%d processing %s:"%(i+1, DATASET_FOLDERS_LEN, file)) - for file in os.listdir(DATASET_PATH + folder): - ## Load an image ## - path = DATASET_PATH + folder + "\\" + file - full_image = cv2.imread(path, 0) - image = cv2.resize(full_image, (0, 0), fx=SCALE, fy=SCALE) + ## Load an image ## + path = DATASET_PATH + "\\" + file + full_image = cv2.imread(path, 0) + image = cv2.resize(full_image, (0, 0), fx=SCALE, fy=SCALE) - ## Detect keypoints ## - kp = sift.detect(image, None) - image = cv2.drawKeypoints( - image, - kp, - image, - flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) + ## Detect keypoints ## + kp = sift.detect(image, None) + image = cv2.drawKeypoints( + image, + kp, + image, + flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) - ## Average and Max size ## - magnitudes = [keypoint.size for keypoint in kp] - 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) + ## Average and Max size ## + magnitudes = [keypoint.size for keypoint in kp] + 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 + labels[i] = name - ## Increment arrays ## - i += 1 - - if(i == EARLY_BREAK): + if(i == EARLY_BREAK and EARLY_BREAK != 0): break print("Done!") ## CSV ## with open(CSV_PATH + "sift.csv" , 'w', newline='') as file: + file.write("name,tot_mag,avg_mag,max_mag,std_mag,avg_rep,max_rep,counts\n") for i in range(len(labels)): - file.write(labels[i] + '\n') for j in range(len(tot_magnitudes[i])): + file.write(str(labels[i]) + ',') file.write(str(tot_magnitudes[i][j]) + ',') file.write(str(avg_magnitudes[i][j]) + ',') file.write(str(max_magnitudes[i][j]) + ',')