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- from PIL import Image
- from matplotlib import cm
- import random
- from skimage import color
- import numpy as np
- import ipfml.metrics as metrics
- import cv2
- from scipy import signal
- def get_LAB_L_SVD(image):
- """Returns Singular values from LAB L Image information
- Args:
- image: PIL Image or Numpy array
- Returns:
- U, s, V information obtained from SVD compression using Lab
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> U, s, V = processing.get_LAB_L_SVD(img)
- >>> U.shape
- (200, 200)
- >>> len(s)
- 200
- >>> V.shape
- (200, 200)
- """
- L = metrics.get_LAB_L(image)
- return metrics.get_SVD(L)
- def get_LAB_L_SVD_s(image):
- """Returns s (Singular values) SVD from L of LAB Image information
- Args:
- image: PIL Image or Numpy array
- Returns:
- vector of singular values
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> s = processing.get_LAB_L_SVD_s(img)
- >>> len(s)
- 200
- """
- L = metrics.get_LAB_L(image)
- return metrics.get_SVD_s(L)
- def get_LAB_L_SVD_U(image):
- """Returns U SVD from L of LAB Image information
- Args:
- image: PIL Image or Numpy array
- Returns:
- U matrix of SVD compression
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> U = processing.get_LAB_L_SVD_U(img)
- >>> U.shape
- (200, 200)
- """
- L = metrics.get_LAB_L(image)
- return metrics.get_SVD_U(L)
- def get_LAB_L_SVD_V(image):
- """Returns V SVD from L of LAB Image information
- Args:
- image: PIL Image or Numpy array
- Returns:
- V matrix of SVD compression
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> V = processing.get_LAB_L_SVD_V(img)
- >>> V.shape
- (200, 200)
- """
- L = metrics.get_LAB_L(image)
- return metrics.get_SVD_V(L)
- def divide_in_blocks(image, block_size, pil=True):
- '''Divide image into equal size blocks
- Args:
- image: PIL Image or Numpy array
- block: tuple (width, height) representing the size of each dimension of the block
- pil: block type returned (default True)
- Returns:
- list containing all 2D Numpy blocks (in RGB or not)
- Raises:
- ValueError: If `image_width` or `image_heigt` are not compatible to produce correct block sizes
- Example:
- >>> import numpy as np
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> from ipfml import metrics
- >>> image_values = np.random.randint(255, size=(800, 800, 3))
- >>> blocks = divide_in_blocks(image_values, (20, 20))
- >>> len(blocks)
- 1600
- >>> blocks[0].width
- 20
- >>> blocks[0].height
- 20
- >>> img_l = Image.open('./images/test_img.png')
- >>> L = metrics.get_LAB_L(img_l)
- >>> blocks_L = divide_in_blocks(L, (100, 100))
- >>> len(blocks_L)
- 4
- >>> blocks_L[0].width
- 100
- '''
- blocks = []
- mode = 'RGB'
- # convert in Numpy array
- image_array = np.array(image)
- # check dimension of input image
- if image_array.ndim != 3:
- mode = 'L'
- image_width, image_height = image_array.shape
- else:
- image_width, image_height, _ = image_array.shape
- # check size compatibility
- width, height = block_size
- if (image_width % width != 0):
- raise ValueError("Width size issue, block size not compatible")
- if (image_height % height != 0):
- raise ValueError("Height size issue, block size not compatible")
- nb_block_width = image_width / width
- nb_block_height = image_height / height
- for i in range(int(nb_block_width)):
- begin_x = i * width
- for j in range(int(nb_block_height)):
- begin_y = j * height
- # getting sub block information
- current_block = image_array[begin_x:(begin_x + width), begin_y:(
- begin_y + height)]
- if pil:
- blocks.append(
- Image.fromarray(current_block.astype('uint8'), mode))
- else:
- blocks.append(current_block)
- return blocks
- def normalize_arr(arr):
- '''Normalize data of 1D array shape
- Args:
- arr: array data of 1D shape
- Returns:
- Normalized 1D array
- Example:
- >>> from ipfml import processing
- >>> import numpy as np
- >>> arr = np.arange(11)
- >>> arr_normalized = processing.normalize_arr(arr)
- >>> arr_normalized[1]
- 0.1
- '''
- output_arr = []
- max_value = max(arr)
- min_value = min(arr)
- for v in arr:
- output_arr.append((v - min_value) / (max_value - min_value))
- return output_arr
- def normalize_arr_with_range(arr, min, max):
- '''Normalize data of 1D array shape
- Args:
- arr: array data of 1D shape
- Returns:
- Normalized 1D Numpy array
- Example:
- >>> from ipfml import processing
- >>> import numpy as np
- >>> arr = np.arange(11)
- >>> arr_normalized = processing.normalize_arr_with_range(arr, 0, 20)
- >>> arr_normalized[1]
- 0.05
- '''
- output_arr = []
- for v in arr:
- output_arr.append((v - min) / (max - min))
- return output_arr
- def normalize_2D_arr(arr):
- """Return array normalize from its min and max values
- Args:
- arr: 2D Numpy array
- Returns:
- Normalized 2D Numpy array
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> img_mscn = processing.rgb_to_mscn(img)
- >>> img_normalized = processing.normalize_2D_arr(img_mscn)
- >>> img_normalized.shape
- (200, 200)
- """
- # getting min and max value from 2D array
- max_value = arr.max(axis=1).max()
- min_value = arr.min(axis=1).min()
- # normalize each row
- output_array = []
- width, height = arr.shape
- for row_index in range(0, height):
- values = arr[row_index, :]
- output_array.append(
- normalize_arr_with_range(values, min_value, max_value))
- return np.asarray(output_array)
- def rgb_to_mscn(image):
- """Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN)
- Args:
- image: 3D RGB image Numpy array or PIL RGB image
- Returns:
- 2D Numpy array with MSCN information
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> img_mscn = processing.rgb_to_mscn(img)
- >>> img_mscn.shape
- (200, 200)
- """
- # check if PIL image or not
- img_arr = np.array(image)
- # convert rgb image to gray
- im = np.array(color.rgb2gray(img_arr) * 255, 'uint8')
- return metrics.gray_to_mscn(im)
- def rgb_to_grey_low_bits(image, nb_bits=4):
- """Convert RGB Image into grey image using only 4 low bits values
- Args:
- image: 3D RGB image Numpy array or PIL RGB image
- nb_bits: optional parameter which indicates the number of bits to keep (default 4)
- Returns:
- 2D Numpy array with low bits information kept
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> low_bits_grey_img = processing.rgb_to_grey_low_bits(img, 5)
- >>> low_bits_grey_img.shape
- (200, 200)
- """
- img_arr = np.array(image)
- grey_block = np.array(color.rgb2gray(img_arr) * 255, 'uint8')
- return metrics.get_low_bits_img(grey_block, nb_bits)
- def rgb_to_LAB_L_low_bits(image, nb_bits=4):
- """Convert RGB Image into Lab L channel image using only 4 low bits values
- Args:
- image: 3D RGB image Numpy array or PIL RGB image
- nb_bits: optional parameter which indicates the number of bits to keep (default 4)
- Returns:
- 2D Numpy array with low bits information kept
- Example:
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> low_bits_Lab_l_img = processing.rgb_to_LAB_L_low_bits(img, 5)
- >>> low_bits_Lab_l_img.shape
- (200, 200)
- """
- L_block = np.asarray(metrics.get_LAB_L(image), 'uint8')
- return metrics.get_low_bits_img(L_block, nb_bits)
- def rgb_to_LAB_L_bits(image, interval):
- """Returns only bits from LAB L canal specified into the interval
- Args:
- image: image to convert using this interval of bits value to keep
- interval: (begin, end) of bits values
- Returns:
- 2D Numpy array with reduced values
- >>> from PIL import Image
- >>> from ipfml import processing
- >>> img = Image.open('./images/test_img.png')
- >>> bits_Lab_l_img = processing.rgb_to_LAB_L_bits(img, (2, 6))
- >>> bits_Lab_l_img.shape
- (200, 200)
- """
- L_block = np.asarray(metrics.get_LAB_L(image), 'uint8')
- return metrics.get_bits_img(L_block, interval)
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