from IPython.display import HTML, display
from pathlib import Path  # https://medium.com/@ageitgey/python-3-quick-tip-the-easy-way-to-deal-with-file-paths-on-windows-mac-and-linux-11a072b58d5f
from PIL import Image as pilImage # as PIL Image is used to avoid conflicts
from io import BytesIO
import base64
import numpy as np


# prepares a series of images
def image_data(path=Path("images/"), images=None):  # path of static images is defaulted
    if images is None:  # default image
        images = [
            {'source': "Internet", 'label': "Green Square", 'file': "green-square-16.png"},
            {'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"},
            {'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "lassen-volcano.jpg"}
        ]
    for image in images:
        # File to open
        image['filename'] = path / image['file']  # file with path
    return images

# Scale to baseWidth
def scale_image(img, baseWidth):
    scalePercent = (baseWidth/float(img.size[0]))
    scaleHeight = int((float(img.size[1])*float(scalePercent)))
    scale = (baseWidth, scaleHeight)
    return img.resize(scale)

# PIL image converted to base64
def image_to_base64(img, format):
    with BytesIO() as buffer:
        img.save(buffer, format)
        return base64.b64encode(buffer.getvalue()).decode()
    
# Convert pixels to Grey Scale
def grey_pixel(pixel):
    average = (pixel[0] + pixel[1] + pixel[2]) // 3  # average pixel values and use // for integer division
    if len(pixel) > 3:
        return( (average, average, average, pixel[3]) ) # PNG format
    else:
        return( (average, average, average) )
    
# Convert pixels to Red Scale
def red_pixel(pixel):
    if len(pixel) > 3:
        return( (pixel[0], 0, 0, pixel[3]) ) # PNG format
    else:
        return( (pixel[0], 0, 0) )
    
# Convert pixels to Red Scale
def green_pixel(pixel):
    if len(pixel) > 3:
        return( (0, pixel[1], 0, pixel[3]) ) # PNG format
    else:
        return( (0, pixel[1], 0) )
    
# Convert pixels to Red Scale
def blue_pixel(pixel):
    if len(pixel) > 3:
        return( (0, 0, pixel[2], pixel[3]) ) # PNG format
    else:
        return( (0, 0, pixel[2]) )
        
# Set Properties of Image, Scale, and convert to Base64
def image_management(image, baseWidth):  # path of static images is defaulted        
    # Image open return PIL image object
    img = pilImage.open(image['filename'])
    
    # Python Image Library operations
    image['format'] = img.format
    image['mode'] = img.mode
    image['size'] = img.size
    # Scale the Image
    img = scale_image(img, baseWidth)
    image['pil'] = img
    image['scaled_size'] = img.size
    image['numpy'] = np.array(img.getdata())
    # Scaled HTML
    image['html'] = '<img src="data:image/png;base64,%s">' % image_to_base64(image['pil'], image['format'])
    
    # Grey HTML
    # each pixel in numpy array is turned to grey 
    # then resulting list, using List Comprehension, is put back into img    
    img.putdata([grey_pixel(pixel) for pixel in image['numpy']])
    image['html_grey'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    # Red HTML
    img.putdata([red_pixel(pixel) for pixel in image['numpy']])
    image['html_red'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    # Green HTML
    img.putdata([green_pixel(pixel) for pixel in image['numpy']])
    image['html_green'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    # Blue HTML
    img.putdata([blue_pixel(pixel) for pixel in image['numpy']])
    image['html_blue'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    
def process_image(image, baseWidth=320):
    image_management(image, baseWidth)
    print("---- meta data -----")
    print(image['label'])
    print(image['source'])
    print(image['format'])
    print(image['mode'])
    print("Original size: ", image['size'])
    print("Scaled size: ", image['scaled_size'])
    
    print("-- images --")
    display(HTML(image['html'])) 
    display(HTML(image['html_grey'])) 
    display(HTML(image['html_red'])) 
    display(HTML(image['html_green'])) 
    display(HTML(image['html_blue']))
if __name__ == "__main__":
    # setup default images
    images = image_data()

    # Sequential Processing    
    for image in images:
        process_image(image)
        
    print()
---- meta data -----
Green Square
Internet
PNG
RGBA
Original size:  (16, 16)
Scaled size:  (320, 320)
-- images --
---- meta data -----
Clouds Impression
Peter Carolin
PNG
RGBA
Original size:  (320, 234)
Scaled size:  (320, 234)
-- images --
---- meta data -----
Lassen Volcano
Peter Carolin
JPEG
RGB
Original size:  (2792, 2094)
Scaled size:  (320, 240)
-- images --

import concurrent.futures

# Jupyter Notebook Visualization of Images
if __name__ == "__main__":
    # setup default images
    images = image_data()
    
    # Parallel Processsing
    # executor allocates threads, it considers core execution capability of machine
    with concurrent.futures.ThreadPoolExecutor() as executor:
        executor.map(process_image, images)  # order is not predictable
        
    print()
---- meta data -----
Green Square
Internet
PNG
RGBA
Original size:  (16, 16)
Scaled size:  (320, 320)
-- images --
---- meta data -----
Clouds Impression
Peter Carolin
PNG
RGBA
Original size:  (320, 234)
Scaled size:  (320, 234)
-- images --
---- meta data -----
Lassen Volcano
Peter Carolin
JPEG
RGB
Original size:  (2792, 2094)
Scaled size:  (320, 240)
-- images --