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#!/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=52pMFnkDU-4
"""
Many ways to open images in Python.
PIL
matplotlib
skimage
openCV
other libraries to open propriatery images like czi, OME-TIFF
"""
"""
############################################################################
######### Using PIL, Python Imaging Library #########
#Pillow is an image manipulation and processing library
#You can use pillow to crop, resize images and to do basic filtering.
#For advanced tasks that require computer vision or machine elarning we have other packages.
#such as openCV, scikit image and scikit learn.
# to install pillow, pip install Pillow
# to import the package you need to use import PIL
"""
from PIL import Image
import numpy as np #Use numpy to convert images to arrays
# Read image
img = Image.open("images/test_image.jpg") #Not a numpy array
print(type(img))
# Output Images
img.show()
# prints format of image
print(img.format)
# prints mode of image
print(img.mode)
#PIL is not by default numpy array but can convert PIL image to numpy array.
img1 = np.asarray(img)
print(type(img1))
"""
###############################################################################
######### Using Matplotlib #########
#Matplotlib is a plotting library for the Python programming language
#Pyplot is a Matplotlib module which provides a MATLAB-like interface
#Pyplot is commonly used not just to generate plots and graphs but also to visualize images.
#because visualizing images is nothing but plotting data in 2D.
# to install matplotlib, pip install matplotlib
# to import the package you need to use import matplotlib
"""
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
img = mpimg.imread("images/test_image.jpg") #this is a numpy array
print(type(img))
print(img)
print(img.shape)
plt.imshow(img)
plt.colorbar() #Puts a color bar next to the image.
"""
#############################################################################
######Using scikit image ############
# to install matplotlib, pip install scikit-image
# to import the package you need to use import skimage
#scikit image is an image processing library that includes alforithms for
#segmentation, geometric transformation, color space manipulation, analysis, filtering,
#feature detection, and more.
#A very good package for traditional machine learning, using Random forest or SVM
"""
from skimage import io, img_as_float, img_as_ubyte
import numpy as np
import matplotlib.pyplot as plt
image = img_as_float(io.imread("images/test_image.jpg"))
#image2 = io.imread("images/test_image.jpg").astype(np.float)
#avoid using astype as it violates assumptions about dtype range.
#for example float should range from 0 to 1 (or -1 to 1) but if you use
#astype to convert to float, the values do not lie between 0 and 1.
#print(image.shape)
#plt.imshow(img)
print(image)
#print(image2)
#image8byte = img_as_ubyte(image)
#print(image8byte)
#End of Skimage
"""
#################################################################################
######### Using openCV #########
#to install open CV : pip install opencv-python
#to import the package you need to use import cv2
#openCV is a library of programming functions mainly aimed at computer vision.
#Very good for images and videos, especially real time videos.
#It is used extensively for facial recognition, object recognition, motion tracking,
#optical character recognition, segmentation, and even for artificial neural netwroks.
You can import images in color, grey scale or unchanged usingindividual commands
cv2.IMREAD_COLOR : Loads a color image. Any transparency of image will be neglected. It is the default flag.
cv2.IMREAD_GRAYSCALE : Loads image in grayscale mode
cv2.IMREAD_UNCHANGED : Loads image as such including alpha channel
Instead of these three flags, you can simply pass integers 1, 0 or -1 respectively.
"""
import cv2
grey_img = cv2.imread("images/test_image.jpg", 0)
color_img = cv2.imread("images/test_image.jpg", 1)
#images opened using cv2 are numpy arrays
print(type(grey_img))
print(type(color_img))
# Use the function cv2.imshow() to display an image in a window.
# First argument is the window name which is a string. second argument is our image.
cv2.imshow("pic", grey_img)
cv2.imshow("color pic", color_img)
# Maintain output window until
# user presses a key or 1000 ms (1s)
cv2.waitKey(0)
#destroys all windows created
cv2.destroyAllWindows()
#OpenCV imread, imwrite and imshow all work with the BGR order, not RGB
#but there is no need to change the order when you read an image with
#cv2.imread and then want to show it with cv2.imshow
#if you use matplotlib, it uses RGB.
import matplotlib.pyplot as plt
plt.imshow(color_img)
#OpenCV represents RGB images as multi-dimensional NumPy arrays, but as BGR.
#we can convert the images from BGR to RGB
plt.imshow(cv2.cvtColor(color_img, cv2.COLOR_BGR2RGB))
#We can also change color spaces from RGB to HSV..
plt.imshow(cv2.cvtColor(color_img, cv2.COLOR_BGR2HSV))
"""
###############################################################################
###########Reading OME-TIFF using apeer_ometiff_library ###########
# pip install apeer-ometiff-library first
# to import the package you need to use import apeer_ometiff_library
#OME-TIFF has tiff and metada (as XML) embedded
#Image is a 5D array.
"""
from apeer_ometiff_library import io #Use apeer.com free platform for image processing in the cloud
(pic2, omexml) = io.read_ometiff("images/test_image.ome.tif") #Unwrap image and embedded xml metadata
print (pic2.shape) #to verify the shape of the array
print(pic2)
print(omexml)
"""
####################################################################################
#reading czi files
# pip install czifile
# to import the package you need to use import czifile
# https://pypi.org/project/czifile/
"""
import czifile
img = czifile.imread('images/test_image.czi')
print(img.shape)
import czifile
from skimage import io
img = czifile.imread('images/Osteosarcoma_01.czi')
print(img.shape)
img1=img[0, 0, :, :, :, 0]
print(img1.shape)
img2=img1[2,:,:]
io.imshow(img2)
"""
######################################################################################
### Reading multiple images from a folder
#The glob module finds all the path names
#matching a specified pattern according to the rules used by the Unix shell
#The glob.glob returns the list of files with their full path
"""
#import the library opencv
import cv2
import glob
#select the path
path = "images/test_images/aeroplane/*.*"
for file in glob.glob(path):
print(file) #just stop here to see all file names printed
a= cv2.imread(file) #now, we can read each file since we have the full path
print(a) #print numpy arrays for each file
#let us look at each file
# cv2.imshow('Original Image', a)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#process each image - change color from BGR to RGB.
c = cv2.cvtColor(a, cv2.COLOR_BGR2RGB)
cv2.imshow('Color image', c)
#wait for 1 second
k = cv2.waitKey(0)
#destroy the window
cv2.destroyAllWindows()
#######################################################################################