| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162 |
- 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_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]))
- nb_rotations = len(rotations)
- img_mean = processing.fusion_images(rotations, pil=False)
- data = processing.get_LAB_L_SVD_s(img_mean)
- # 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())
- data = utils.normalize_arr_with_range(data, min_val, max_val)
- return data
- scene = 'Appart1opt02'
- mean_svd_values = []
- indices = ["00020", "00080", "00150", "00300", "00500", "00700", "00900"]
- for index in indices:
- path = os.path.join(data_folder, scene + '/appartAopt_' + index + '.png')
- mean_svd_values.append(get_svd_mean_image_rotations(path))
- plt.title("Information from merged rotations images at different noise level from " + scene + " scene", fontsize=22)
- plt.ylabel('Singular values', fontsize=18)
- plt.xlabel('Vector features', fontsize=18)
- for id, data in enumerate(mean_svd_values):
- p_label = "appartAopt_" + indices[id]
- plt.plot(data, label=p_label)
- plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=16)
- plt.ylim(0, 0.01)
- plt.show()
|
