from IPython.display import Image, 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

# 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': "Luna Iwazaki", 'label': "Happy Face", 'file': "happyface.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

def image_display(images):
    for image in images:  
        display(Image(filename=image['filename']))


# Run this as standalone tester to see sample data printed in Jupyter terminal
if __name__ == "__main__":
    # print parameter supplied image
    green_square = image_data(images=[{'source': "Internet", 'label': "Green Square", 'file': "green-square-16.png"}])
    image_display(green_square)
    
    # display default images from image_data()
    default_images = image_data()
    image_display(default_images)
    

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 pilImage 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': "Luna Iwazaki", 'label': "Happy Face", 'file': "happyface.png"},
            {'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"},
            {'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "lassen-volcano.jpg"}
        ]
    #image to the happy face and the other
           
    for image in images:
        # File to open
        image['filename'] = path / image['file']  # file with path
    return images

# Large image scaled to baseWidth of 320
def scale_image(img):
    baseWidth = 320
    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()

# Set Properties of Image, Scale, and convert to Base64
def image_management(image):  # 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)
    image['pil'] = img
    image['scaled_size'] = img.size
    # Scaled HTML
    image['html'] = '<img src="data:image/png;base64,%s">' % image_to_base64(image['pil'], image['format'])
    
# Create Grey Scale Base64 representation of Image
def image_management_add_html_grey(image):
    # Image open return PIL image object
    img = image['pil']
    format = image['format']
    
    img_data = img.getdata()  # Reference https://www.geeksforgeeks.org/python-pil-image-getdata/
    image['data'] = np.array(img_data) # PIL image to numpy array
    image['gray_data'] = [] # key/value for data converted to gray scale

    # 'data' is a list of RGB data, the list is traversed and hex and binary lists are calculated and formatted
    for pixel in image['data']:
        # create gray scale of image, ref: https://www.geeksforgeeks.org/convert-a-numpy-array-to-an-image/
        average = (pixel[0] + pixel[1] + pixel[2]) // 3  # average pixel values and use // for integer division
        if len(pixel) > 3:
            image['gray_data'].append((average, average, average, pixel[3])) # PNG format
        else:
            image['gray_data'].append((average, average, average))
        # end for loop for pixels
        
    img.putdata(image['gray_data'])
    image['html_grey'] = '<img src="data:image/png;base64,%s">' % image_to_base64(img, format)


# Jupyter Notebook Visualization of Images
if __name__ == "__main__":
    # Use numpy to concatenate two arrays
    images = image_data()
    
    # Display meta data, scaled view, and grey scale for each image
    # imperitive think of all things need to do, write functions, and write them in that order
    for image in images:
        image_management(image)
        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("-- original image --")
        display(HTML(image['html'])) 
        
        print("--- grey image ----")
        image_management_add_html_grey(image)
        display(HTML(image['html_grey'])) 
    print()

from PIL import Image

def scale_image(img):
    baseWidth = 320
    scalePercent = (baseWidth/float(img.size[0]))
    scaleHeight = int((float(img.size[1])*float(scalePercent)))
    scale = (baseWidth, scaleHeight)
    return img.resize(scale)

def color_image(image):
    width, height = image.size
    for x in range(width):
        for y in range(height):
            image.putpixel( (x, y), (100, 100, 255) )
    return image

image = Image.open('images/happyface.png')
color_image(scale_image(image))

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
# blurring of the image use the def blur()
# chat gpt has more in depth
def blur(I):

    for i in range(1,I.shape[0]-1):
        for j in range(1,I.shape[1]-1):
            I[i,j] = (4*I[i,j] + I[i-1,j] + I[i+1,j] + I[i,j-1] + I[i,j+1])/8.0
    return I
img = plt.imread("images/clouds-impression.png")
image = Image.open("images/clouds-impression.png")
# blurring the image and changing what it looks like
blur(img)
blur(img)
blur(img)
#this is required so the image will show and not just numbers aka binary
plt.imshow(img, cmap="gray")
image