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- from ipfml import processing, utils
- from skimage import transform
- import numpy as np
- import matplotlib.pyplot as plt
- import os
- from PIL import Image
- data_folder = "../fichiersSVD_light"
- def get_svd_mean_and_image_rotations(img_path):
- print("Extract features from... " + img_path)
- img = np.asarray(Image.open(img_path))
- width, height, dim = img.shape
- img_mean = np.empty([width, height, 3])
- rotations = []
- svd_data_rotation = []
- for i in range(4):
- rotations.append(processing.rotate_image(img, (i+1)*90, pil=False))
- svd_data_rotation.append(processing.get_LAB_L_SVD_s(rotations[i]))
- Image.fromarray(rotations[i]).show()
- mean_image = processing.fusion_images(rotations, pil=False)
- mean_data = processing.get_LAB_L_SVD_s(mean_image)
- # getting max and min information from min_max_filename
- with open(data_folder + "/lab_min_max_values", 'r') as f:
- min_val = float(f.readline())
- max_val = float(f.readline())
- mean_data = utils.normalize_arr_with_range(mean_data, min_val, max_val)
- return [utils.normalize_arr_with_range(data, min_val, max_val) for data in svd_data_rotation], mean_data
- scene = 'Appart1opt02'
- indices = ["00020", "00080", "00150", "00300", "00500", "00700", "00900"]
- for index in indices:
- path = os.path.join(data_folder, scene + '/appartAopt_' + index + '.png')
- svd_data, mean_svd_data = get_svd_mean_and_image_rotations(path)
- plt.title("SVD information of rotations and merged image from " + scene + " scene", fontsize=22)
- plt.ylabel('Singular values', fontsize=18)
- plt.xlabel('Vector features', fontsize=18)
- for id, data in enumerate(svd_data):
- p_label = "appartAopt_" + index + "_" + str((id +1) * 90)
- plt.plot(data, label=p_label)
- mean_label = "appartAopt_" + index + "_mean"
- plt.plot(mean_svd_data, label=mean_label)
- plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=16)
- plt.ylim(0, 0.01)
- plt.show()
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