AI01, Loading and manipulating images

# example of loading an image with the Keras API
from keras.preprocessing.image import load_img

# load the image
image = load_img('beach.jpg')

# report details about the image
print(type(image))
print(image.format)
print(image.mode)
print(image.size)

# show the image
image.show()

# load and show an image with Pillow 
from PIL import Image 

# load the image 
image = Image.open('opera_house.jpg') 

# summarize some details about the image 
print(image.format)
print(image.mode)
print(image.size)
print(type(image))

# show the image 
image.show()

%matplotlib inline
from skimage import io                  # pip install scikit-image

image = io.imread('puppy.jpg')
print(type(image))
print(image.shape)

io.imshow(image)

# load and display an image with Matplotlib 
from matplotlib import image
from matplotlib import pyplot

# load image as pixel array 
data = image.imread('opera_house.jpg') 
print(type(data))

# summarize shape of the pixel array 
print(data.dtype) 
print(data.shape) 

# display the array of pixels as an image
pyplot.imshow(data) 
pyplot.show()

%matplotlib inline
from skimage import io
import cv2

image = cv2.imread('cat.jpg')
print(type(image))
print(image.shape)

io.imshow(image)

# load all images in a directory 
from os import listdir
from matplotlib import image

# load all images in a directory 
loaded_images = list() 

for filename in listdir('images'):
    # load image
    img_data = image.imread('images/' + filename) 
    
    # store loaded image 
    loaded_images.append(img_data)
    print('> loaded %s %s' % (filename, img_data.shape))

import matplotlib.pyplot as plt
from matplotlib import image

img = image.imread('input_image.jpg')
plt.imshow(img)
plt.savefig('output_image.jpg')

# example of saving an image with the Keras API
from keras.preprocessing.image import load_img
from keras.preprocessing.image import save_img
from keras.preprocessing.image import img_to_array

# load image as as grayscale
img = load_img('beach.jpg', color_mode='grayscale')

# convert image to a numpy array
img_array = img_to_array(img)

# save the image with a new filename
save_img('bondi_beach_grayscale.jpg', img_array)

# example of saving an image in another format 
from PIL import Image

# load the image 
image = Image.open('opera_house.jpg')

# save as PNG format 
image.save('opera_house.png', format='PNG') 

# load the image again and inspect the format 
image2 = Image.open('opera_house.png')
print(image2.format)

#Import libraries 
from skimage import io 
from skimage import color 

#Read image 
img = io.imread('puppy.jpg')

#Convert to YPbPr 
img_ypbpr= color.rgb2ypbpr(img)

#Convert back to RGB 
img_rgb= color.ypbpr2rgb(img_ypbpr)

#Save image
io.imsave("puppy_ypbpr.jpg", img_ypbpr)

from skimage import io                  
import pandas as pd

image = io.imread('puppy.jpg')
df = pd.DataFrame(image.flatten())        # flatten() : convert the three dimensions of an RGB image to a single dimension

# saving in excel format
filepath = 'pixel_values1.xlsx'
df.to_excel(filepath, index=False)      # pip install OpenPyXL

from PIL import Image

img = Image.open('input_image.jpg').convert('LA')
img.show()

%matplotlib inline

#Import libraries 
from skimage import io
from skimage import color
from skimage import data
from pylab import *

img = io.imread('puppy.jpg')         #Read image 
img_hsv = color.rgb2hsv(img)         #Convert to HSV
img_rgb = color.hsv2rgb(img_hsv)     #Convert back to RGB 

#Show both figures 
figure(0)                            
io.imshow(img_hsv)
figure(1)
io.imshow(img_rgb)

%matplotlib inline

#Import libraries 
from skimage import io 
from skimage import color
from skimage import data 

#Read image 
img = io.imread('puppy.jpg')

#Convert to XYZ 
img_xyz = color.rgb2xyz(img)

#Convert back to RGB 
img_rgb = color.xyz2rgb(img_xyz)

#Show both figures 
figure(0) 
io.imshow(img_xyz) 
figure(1) 
io.imshow(img_rgb)

%matplotlib inline

#Import libraries 
from skimage import io
from skimage import color 

#Read image 
img = io.imread('puppy.jpg')

#Convert to LAB 
img_lab = color.rgb2lab(img)

#Convert back to RGB 
img_rgb = color.lab2rgb(img_lab)

#Show both figures 
figure(0) 
io.imshow(img_lab)
figure(1)
io.imshow(img_rgb)

%matplotlib inline

#Import libraries 
from skimage import io 
from skimage import color 

#Read image
img = io.imread('puppy.jpg')

#Convert to YUV 
img_yuv = color.rgb2yuv(img)

#Convert back to RGB
img_rgb = color.yuv2rgb(img_yuv)

#Show both figures 
figure(0) 
io.imshow(img_yuv) 
figure(1) 
io.imshow(img_rgb)

%matplotlib inline

#Import libraries 
from skimage import io 
from skimage import color 

#Read image 
img = io.imread('puppy.jpg')

#Convert to YIQ 
img_yiq = color.rgb2yiq(img)

#Convert back to RGB 
img_rgb = color.yiq2rgb(img_yiq)

#Show both figures 
figure(0)
io.imshow(img_yiq) 
figure(1)
io.imshow(img_rgb)

%matplotlib inline

#Import libraries 
from skimage import io 
from skimage import color 

#Read image
img = io.imread('puppy.jpg')

#Convert to YPbPr 
img_ypbpr= color.rgb2ypbpr(img)

#Convert back to RGB 
img_rgb= color.ypbpr2rgb(img_ypbpr)

#Show both figures
figure(0)
io.imshow(img_ypbpr) 
figure(1) 
io.imshow(img_rgb)

# example of converting an image with the Keras API
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import array_to_img
import numpy as np
from PIL import Image

# load the image
img = load_img('beach.jpg')
print(type(img))

# convert to numpy array(img_ndarray is equal way to img_array)
img_ndarray = np.asarray(img).astype('float32')
img_array = img_to_array(img)
print(img_ndarray.dtype)
print(img_ndarray.shape)
print(img_array.dtype)
print(img_array.shape)

# convert back to image
img_pil1 = array_to_img(img_array)
img_pil2 = img_array.astype(np.uint8)
img_pil2 = Image.fromarray(img_pil2)

# load image and convert to and from NumPy array 
from PIL import Image
from numpy import asarray

# load the image 
image = Image.open('opera_house.jpg') 
print(type(image))

# convert image to numpy array 
data = asarray(image) 

# summarize shape 
print(type(data))
print(data.shape)

# create Pillow image
image2 = Image.fromarray(data) 
print(type(image2))

# summarize image details 
print(image2.format) 
print(image2.mode)
print(image2.size)

image2

%matplotlib inline
from skimage import io                  # pip install scikit-image
import numpy as np

image = io.imread('puppy.jpg')
image = np.asarray(image)
type(image)

# create a thumbnail of an image 
from PIL import Image

# load the image 
image = Image.open('opera_house.jpg') 

# report the size of the image
print(image.size)

# create a thumbnail and preserve aspect ratio 
image.thumbnail((100,100)) 

# report the size of the modified image 
print(image.size) 

# show the image 
image.show()

# resize image and force a new shape 
from PIL import Image

# load the image
image = Image.open('opera_house.jpg') 

# report the size of the image 
print(image.size) 

# resize image and ignore original aspect ratio
img_resized = image.resize((200,200))

# report the size of the thumbnail 
print(img_resized.size)

# show the image 
img_resized.show()

from skimage import io
from skimage.transform import resize

img = io.imread('puppy.jpg')
img_res = resize(img, (100,100)) 
io.imshow(img_res)
io.imsave("ss.jpg", img_res)

# create flipped versions of an image 
from PIL import Image
from matplotlib import pyplot 

# load image 
image = Image.open('opera_house.jpg')

# horizontal flip 
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT) 

# vertical flip 
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM) 

# plot all three images using matplotlib 
pyplot.subplot(311)
pyplot.imshow(image)
pyplot.subplot(312)
pyplot.imshow(hoz_flip) 
pyplot.subplot(313) 
pyplot.imshow(ver_flip)
pyplot.show()

# create rotated versions of an image 
from PIL import Image 
from matplotlib import pyplot

# load image 
image = Image.open('opera_house.jpg')

# plot original image 
pyplot.subplot(311) 
pyplot.imshow(image) 

# rotate 45 degrees 
pyplot.subplot(312) 
pyplot.imshow(image.rotate(45))

# rotate 90 degrees
pyplot.subplot(313) 
pyplot.imshow(image.rotate(90))
pyplot.show()

%matplotlib inline
from skimage import io 
from skimage.transform import rotate 

img = io.imread('puppy.jpg')
img_rot = rotate(img, 20) 
io.imshow(img_rot)

# example of cropping an image 
from PIL import Image

# load image
image = Image.open('opera_house.jpg') 

# create a cropped image 
cropped = image.crop((100, 100, 200, 200))

# show cropped image 
cropped.show()

%matplotlib inline
from skimage import io 
from skimage import draw

img = io.imread('puppy.JPG')
print(img.shape)
x,y = draw.line(0,0,100,100)
img[x, y] = 0
io.imshow(img)

from skimage import io 
from skimage import draw
img = io.imread('puppy.jpg') 

def rectangle(x, y, w, h): 
    rr, cc = [x, x + w, x + w, x], [y, y, y + h, y + h]
    return (draw.polygon(rr, cc))

rr, cc = rectangle(10, 10, 355,355)
img[rr, cc] = 1 
io.imshow(img)

#Import libraries 
from skimage import io 
from skimage import draw

#Load image
img = io.imread('puppy.jpg')

#Define circle coordinates and radius 
x, y = draw.circle(10,10, 100)

#Draw circle 
img[x, y] = 1

#Show image
io.imshow(img)

#Import libraries
from skimage import io 
from skimage import draw
#Load image 
img = io.imread('puppy.jpg')

#Define Bezier curve coordinates 
x, y = draw.bezier_curve(0,0, 100, 100, 200,300,100)

#Draw Bezier curve 
img[x, y] = 1

#Show image 
io.imshow(img)

%matplotlib inline
#import required packages 
import cv2
import numpy as np
from skimage import io

#Read image
image = cv2.imread("cat_1.jpg")

#Define font 
font  = cv2.FONT_HERSHEY_SIMPLEX

#Write on the image 
cv2.putText(image, "I am a Cat", (50, 50), font, 0.8, (0, 255, 0), 2, cv2.LINE_AA)
io.imshow(image)

%matplotlib inline

from skimage import exposure 
from skimage import io 
from pylab import * 

img = io.imread('puppy.jpg') 
gamma_corrected1 = exposure.adjust_gamma(img, 0.5) 
gamma_corrected2 = exposure.adjust_gamma(img, 5) 

figure(0) 
io.imshow(gamma_corrected1) 
figure(1)
io.imshow(gamma_corrected2)

from skimage import io 
from skimage.measure import compare_ssim as ssim

img_original = io.imread('puppy.jpg')
img_modified = io.imread('puppy_ypbpr.jpg')

ssim_original = ssim(img_original, img_original, data_range=img_original.max() - img_original.min(), multichannel=True) 
ssim_different = ssim(img_original, img_modified, data_range=img_modified.max() - img_modified.min(), multichannel=True) 

print(ssim_original,ssim_different)