"""
Functions to quickly extract reduced information from image
"""
from PIL import Image
import random
import cv2
from skimage import transform, color
import numpy as np
import ipfml.metrics as metrics
from ipfml import exceptions
from scipy import signal
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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 as PIL Image (default True)
Returns:
list containing all 2D Numpy blocks (in RGB or not)
Raises:
ValueError: If `image_width` or `image_height` 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
[docs]def fusion_images(images, pil=True):
'''Fusion array of images into single image
Args:
images: array of images (PIL Image or Numpy array)
pil: block type returned as PIL Image (default True)
Returns:
merged image from array of images
Raises:
ValueError: if `images` is not an array or is empty
NumpyShapeComparisonException: if `images` array contains images with different shapes
Example:
>>> import numpy as np
>>> from ipfml import processing
>>> image_values_1 = np.random.randint(255, size=(800, 800, 3))
>>> image_values_2 = np.random.randint(255, size=(800, 800, 3))
>>> merged_image = processing.fusion_images([image_values_1, image_values_2], pil=False)
>>> merged_image.shape
(800, 800, 3)
'''
mode = 'RGB'
dim = 1
if len(images) == 0:
raise ValueError('Empty array of images provided...')
# convert image in numpy array (perhaps not necessary)
images = [np.asarray(img) for img in images]
image_array = images[0]
if image_array.ndim != 3:
mode = 'L'
width, height = image_array.shape
else:
width, height, dim = image_array.shape
# raise exception if all images do not have same shape
if not np.array([image_array.shape == a.shape for a in images]).all():
raise NumpyShapeComparisonException()
if dim == 1:
image_mean = np.empty([width, height])
else:
image_mean = np.empty([width, height, dim])
nb_images = len(images)
# construction of mean image from rotation
for i in range(width):
for j in range(height):
if dim == 1:
grey_value = 0
# for each image we merge pixel values
for img in images:
grey_value += img[i][j]
image_mean[i][j] = grey_value / nb_images
else:
for k in range(dim):
canal_value = 0
# for each image we merge pixel values
for img in images:
canal_value += img[i][j][k]
image_mean[i][j][k] = canal_value / nb_images
image_mean = np.array(image_mean, 'uint8')
if pil:
return Image.fromarray(image_mean, mode)
else:
return image_mean
[docs]def rotate_image(image, angle=90, pil=True):
"""Rotate image using specific angle
Args:
image: PIL Image or Numpy array
angle: Angle value of the rotation
pil: block type returned as PIL Image (default True)
Returns:
Image with rotation applied
Example:
>>> from PIL import Image
>>> import numpy as np
>>> from ipfml import processing
>>> image_values = Image.open('./images/test_img.png')
>>> rotated_image = processing.rotate_image(image_values, 90, pil=False)
>>> rotated_image.shape
(200, 200, 3)
"""
mode = 'RGB'
image_array = np.asarray(image)
if image_array.ndim != 3:
mode = 'L'
rotated_image = np.array(
transform.rotate(image_array, angle) * 255, 'uint8')
if pil:
return Image.fromarray(rotated_image, mode)
else:
return rotated_image