From aab3e22fd8f56a617ef172d12ed8719a3adc979d Mon Sep 17 00:00:00 2001 From: Tom Selier Date: Fri, 29 Sep 2023 21:49:27 +0200 Subject: [PATCH] added csv extraction --- src/experiments/algorithms/data/bgr_curve.csv | 24 ++++ src/experiments/algorithms/data/bgr_stats.csv | 9 ++ src/experiments/algorithms/data/hsv_curve.csv | 24 ++++ src/experiments/algorithms/data/hsv_stats.csv | 9 ++ .../algorithms/distribution_test.py | 123 +++++++++++++++--- src/experiments/algorithms/sift_v3.py | 2 +- 6 files changed, 169 insertions(+), 22 deletions(-) create mode 100644 src/experiments/algorithms/data/bgr_curve.csv create mode 100644 src/experiments/algorithms/data/bgr_stats.csv create mode 100644 src/experiments/algorithms/data/hsv_curve.csv create mode 100644 src/experiments/algorithms/data/hsv_stats.csv diff --git a/src/experiments/algorithms/data/bgr_curve.csv b/src/experiments/algorithms/data/bgr_curve.csv new file mode 100644 index 0000000..091c2e2 --- /dev/null +++ b/src/experiments/algorithms/data/bgr_curve.csv @@ -0,0 +1,24 @@ 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diff --git a/src/experiments/algorithms/data/hsv_stats.csv b/src/experiments/algorithms/data/hsv_stats.csv new file mode 100644 index 0000000..4659124 --- /dev/null +++ b/src/experiments/algorithms/data/hsv_stats.csv @@ -0,0 +1,9 @@ +label,avg_b,avg_g,avg_r,std_b,std_g,std_r, +accasia,35.90440242881845,79.12766192516973,96.03904497370279,10.316069136947837,32.12312271750131,28.112318716352682, +berk,37.269905856891874,66.77426733032776,109.28940151500194,12.477741919995777,32.92938477640848,39.80902152951404, +eik,39.76121762093227,41.20602771946309,121.12686669901616,23.03425646554001,25.365001089124593,33.494192142922856, +els,27.25760648235924,68.91241506727702,182.27155476463224,4.423353762285157,34.149610367865485,42.029268409707505, +esdoorn,40.44041857512247,131.63818552294646,77.84993286553097,9.535383780285505,39.540648429565636,24.46222201958771, +es,38.37579404108352,98.88868379328436,96.68345708286985,6.84651057363783,30.56118627938561,18.229878712396474, +linde,36.8388961321136,41.02522196067761,130.70675471758108,31.06746560545738,22.601057153094764,34.24357220627512, +plataan,30.208627954766115,123.22600953975079,88.02246907749901,4.833302068080008,20.980674302352114,19.054660495972172, diff --git a/src/experiments/algorithms/distribution_test.py b/src/experiments/algorithms/distribution_test.py index 30e2ca6..4f32eac 100644 --- a/src/experiments/algorithms/distribution_test.py +++ b/src/experiments/algorithms/distribution_test.py @@ -3,25 +3,14 @@ import matplotlib.pyplot as plt import cv2 import os import math - -def calcWeightedStd(values, weights): - N = len(values) - x_bar = np.average(values, weights=weights, axis=0) - numerator = 0 - for i in range(N): - numerator += weights[i] * ((values[i] - x_bar) ** 2) - denominator = np.sum(weights) - return math.sqrt(numerator / denominator) -def calcNormalFunc(mean, sd, len): +def calcNormalFunc(mean : float, sd : float, len : int): f = np.zeros(len, dtype=np.longdouble) - # calculate PDF for x in range(len): exp = (-(x - mean) ** 2)/(2 * sd ** 2) f[x] = 1 / math.sqrt(2 * np.pi * sd** 20 ) * (math.exp(exp)) - # normalize PDF max = np.amax(f) min = np.amin(f) for x in range(len): @@ -29,70 +18,162 @@ def calcNormalFunc(mean, sd, len): return f +## Constants ## DATASET_PATH = "dataset\\" +CSV_PATH = "src\\experiments\\algorithms\\data\\" BARK_TYPES = 8 +## Arrays ## avgs_bgr = [[[],[],[]] for x in range(BARK_TYPES)] +stds_bgr = [[[],[],[]] for x in range(BARK_TYPES)] wgts_bgr = [[[],[],[]] for x in range(BARK_TYPES)] avgs_hsv = [[[],[],[]] for x in range(BARK_TYPES)] +stds_hsv = [[[],[],[]] for x in range(BARK_TYPES)] wgts_hsv = [[[],[],[]] for x in range(BARK_TYPES)] labels = ["" for x in range(BARK_TYPES)] - + +## Data extraction ## i = -1 last_name = "" for file in os.listdir(DATASET_PATH): + # Store name + iterate tree type # name = file.split('_')[0] if(name != last_name): last_name = name i += 1 labels[i] = name + # Load picture # 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) + # Data storing # for j in range(3): avgs_bgr[i][j].append(np.mean(im_bgr[:, :, j])) + stds_bgr[i][j].append(np.std(im_bgr[:, :, j])) wgts_bgr[i][j].append(len(im_bgr[:, :, j])) avgs_hsv[i][j].append(np.mean(im_hsv[:, :, j])) + stds_hsv[i][j].append(np.std(im_hsv[:, :, j])) wgts_hsv[i][j].append(len(im_hsv[:, :, j])) - +## Arrays ## 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_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)] +w_std_hsv = [[0, 0, 0] for x in range(BARK_TYPES)] -#TODO: Mag de weighted std berekening met avgs? Denk t nie +## Statistics calculations ## for i in range(BARK_TYPES): 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]) - - 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]) + w_avg_bgr[i][j] = np.average( + avgs_bgr[i][j], + weights=wgts_bgr[i][j], + axis=0) + w_std_bgr[i][j] = np.average( + stds_bgr[i][j], + weights=wgts_bgr[i][j]) + w_avg_hsv[i][j] = np.average( + avgs_hsv[i][j], + weights=wgts_hsv[i][j], + axis=0) + w_std_hsv[i][j] = np.average( + stds_hsv[i][j], + weights=wgts_hsv[i][j]) +## Plots + Normal calculations ## fig, axs = plt.subplots(2,4) row = 0 col = -1 colours = ('b', 'g', 'r') for i in range(BARK_TYPES): + # Rows and columns # row = 1 if i > 3 else 0 col += 1 if i == 4: col = 0 + # Normal and plotting # for j in range(3): f = calcNormalFunc(w_avg_bgr[i][j], w_std_bgr[i][j], 255) label = "$\mu:$ %d, $\sigma: %d$"%(w_avg_bgr[i][j],w_std_bgr[i][j]) axs[row, col].plot(f, c=colours[j], label=label) + # Plot params # axs[row, col].grid() axs[row, col].legend() axs[row, col].set_title(str(labels[i])) - +plt.show(block=False) + +fig, axs = plt.subplots(2,4) +row = 0 +col = -1 +for i in range(BARK_TYPES): + # Rows and columns # + row = 1 if i > 3 else 0 + col += 1 + if i == 4: col = 0 + + # Normal and plotting # + for j in range(3): + f = calcNormalFunc(w_avg_hsv[i][j], w_std_hsv[i][j], 255) + label = "$\mu:$ %d, $\sigma: %d$"%(w_avg_hsv[i][j], w_std_hsv[i][j]) + axs[row, col].plot(f, label=label) + + # Plot params # + axs[row, col].grid() + axs[row, col].legend() + axs[row, col].set_title(str(labels[i])) plt.show() +## CSVs ## +# BGR mean and std # +with open(CSV_PATH + "bgr_stats.csv", 'w', newline='') as file: + file.write("label,avg_b,avg_g,avg_r,std_b,std_g,std_r,\n") + for i in range(BARK_TYPES): + file.write(labels[i] + ',') + for j in range(3): + file.write(str(w_avg_bgr[i][j]) + ',') + for j in range(3): + file.write(str(w_std_bgr[i][j]) + ',') + file.write('\n') + +# BGR normal curve per channel # +id = ('B', 'G', 'R') +with open(CSV_PATH + "bgr_curve.csv", 'w', newline='') as file: + for i in range(BARK_TYPES): + for j in range(3): + file.write(labels[i] + ',') + file.write(id[j] + ',') + f = calcNormalFunc(w_avg_bgr[i][j], w_std_bgr[i][j], 255) + for k in range(255): + file.write(str(f[k]) + ',') + file.write('\n') + +# HSV mean and std # +with open(CSV_PATH + "hsv_stats.csv", 'w', newline='') as file: + file.write("label,avg_b,avg_g,avg_r,std_b,std_g,std_r,\n") + for i in range(BARK_TYPES): + file.write(labels[i] + ',') + for j in range(3): + file.write(str(w_avg_hsv[i][j]) + ',') + for j in range(3): + file.write(str(w_std_hsv[i][j]) + ',') + file.write('\n') + +# BGR normal curve per channel # +id = ('H', 'S', 'V') +with open(CSV_PATH + "hsv_curve.csv", 'w', newline='') as file: + for i in range(BARK_TYPES): + for j in range(3): + file.write(labels[i] + ',') + file.write(id[j] + ',') + f = calcNormalFunc(w_avg_hsv[i][j], w_std_hsv[i][j], 255) + for k in range(255): + file.write(str(f[k]) + ',') + file.write('\n') diff --git a/src/experiments/algorithms/sift_v3.py b/src/experiments/algorithms/sift_v3.py index 8e8dbb7..f6ae40f 100644 --- a/src/experiments/algorithms/sift_v3.py +++ b/src/experiments/algorithms/sift_v3.py @@ -79,7 +79,7 @@ for folder in os.listdir(DATASET_PATH): print("Done!") ## Pandas ## -with open(CSV_PATH + "data.csv" , 'w', newline='') as file: +with open(CSV_PATH + "sift.csv" , 'w', newline='') as file: for i in range(len(labels)): file.write(labels[i] + '\n') for j in range(len(tot_magnitudes[i])):