#!/usr/bin/env python

__author__ = "Sreenivas Bhattiprolu"

__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"

# https://www.youtube.com/watch?v=s_hDL2fGvow&t=

"""

#Image processing using Scipy

Scipy is a python library that is part of numpy stack.

It contains modules for linear algebra, FFT, signal processing and

image processing. Not designed for image processing but has a few tools

"""

#You can use imread from scipy to read images

from scipy import misc

img = misc.imread("images/monkey.jpg")

print(type(img)) #numpy array

#since it gives a message about imread being depreciated I will use

#skimage which also gives a numpy array.

from skimage import io

img = io.imread("images/monkey.jpg")

print(type(img)) #numpy array

from skimage import io, img_as_ubyte

import matplotlib.pyplot as plt

import numpy as np

from scipy import ndimage

img = img_as_ubyte(io.imread("images/monkey.jpg", as_gray=True))

#img_as_ubyte converts image to 8 bit unsigned int.

print(type(img))

print(img.shape, img.dtype)

#plt.imshow(img)

#individual pixel values

print(img[0,0]) #reports pixel value at 0,0. Remove img_as_ubte and see the value.

#also make as_grey=True and see the above values

#pixel values from a slice

print(img[10:15, 20:25]) #Values from a slice

mean_grey = img.mean()

max_value = img.max()

min_value = img.min()

print(mean_grey, min_value, max_value)

plt.imshow(img)

#geometric transformation

#flipped

flipped_img_LR = np.fliplr(img)

flipped_img_UD = np.flipud(img)

plt.subplot(2,1,1)

plt.imshow(img, cmap="Greys")

plt.subplot(2,2,3)

plt.imshow(flipped_img_LR, cmap="Blues")

plt.subplot(2,2,4)

plt.imshow(flipped_img_UD, cmap="hsv")

#For all other options: https://matplotlib.org/tutorials/colors/colormaps.html

#Rotation

rotated_img = ndimage.rotate(img, 45)

plt.imshow(rotated_img)

rotated_img_noreshape = ndimage.rotate(img, 45, reshape=False)

plt.imshow(rotated_img_noreshape)

###################3

#Filtering

#Local filters: replace the value of pixels by a function of the values of neighboring pixels.

from skimage import io, img_as_ubyte

import matplotlib.pyplot as plt

import numpy as np

from scipy import ndimage

img = img_as_ubyte(io.imread("images/nucleiTubolin_small_noisy.jpg", as_gray=True))

img1 = img_as_ubyte(io.imread("images/test_image.jpg", as_gray=True))

img2 = img_as_ubyte(io.imread("images/test_images/aeroplane/1.jpg", as_gray=False))

uniform_filtered_img = ndimage.uniform_filter(img, size=9)

#plt.imshow(uniform_filtered_img)

#Gaussian filter: from scipy.ndimage

# Gaussian filter smooths noise but also edges

blurred_img = ndimage.gaussian_filter(img, sigma=3) #also try 5, 7

#plt.imshow(blurred_img)

#Median filter is better than gaussian. A non-local means is even better

median_img = ndimage.median_filter(img, 3)

#plt.imshow(median_img)

#Edge detection

sobel_img = ndimage.sobel(img2, axis=0) #Axis along which to calculate sobel

#plt.imshow(sobel_img)

#for a list of all filters

#https://docs.scipy.org/doc/scipy/reference/ndimage.html