from IPython.display import Image
Image(filename='images/smiley.jpg') 

Notes

Lossy - data lost cannot be recovered

Lossless - data lost can be recovered

Pros Cons
Lossy Small file sizes. Ideal for web use. Lots of tools, plugins and software support it. Quality degrades due to higher rate of compression.
Lossless No loss in quality. Slight decreases in file sizes. Compressed files are larger than lossy files.

Enumerate "Data" Big Idea from College Board

Some of the big ideas and vocab that you observe, talk about it with a partner ...

  • "Data compression is the reduction of the number of bits needed to represent data"
  • "Data compression is used to save transmission time and storage space."
  • "lossy data can reduce data but the original data is not recovered"
  • "lossless data lets you restore and recover"

The Image Lab Project contains a plethora of College Board Unit 2 data concepts. Working with Images provides many opportunities for compression and analyzing size.

Image Files and Size

Here are some Images Files. Download these files, load them into images directory under _notebooks in your Blog. - Clouds Impression

Describe some of the meta data and considerations when managing Image files. Describe how these relate to Data Compression ...

  • File Type, PNG and JPG are two types used in this lab
  • Size, height and width, number of pixels
  • Visual perception, lossy compression

Python Libraries and Concepts used for Jupyter and Files/Directories

Introduction to displaying images in Jupyter notebook

IPython

Support visualization of data in Jupyter notebooks. Visualization is specific to View, for the web visualization needs to be converted to HTML.

pathlib

File paths are different on Windows versus Mac and Linux. This can cause problems in a project as you work and deploy on different Operating Systems (OS's), pathlib is a solution to this problem.

  • What are commands you use in terminal to access files?
    • "cd" command allows you to access files, "ls" allows you to view files, "rm" allows you to delete files, and there are many more commands to interact with files
  • What are the command you use in Windows terminal to access files?
    • "cd" command allows you to access files, "dir" allows you to view files, "rd" allows you to delete files, and there are many more commands to interact with files
  • What are some of the major differences?
    • The largest difference between windows commands and ubuntu commands is in the syntax, when code uses commands in order to run, different syntax will break the requirements of this code.

Provide what you observed, struggled with, or leaned while playing with this code.

  • Why is path a big deal when working with images?
    • Without accessing images you cannot display/use them, and without path you cannot access images on all OS's
  • How does the meta data source and label relate to Unit 5 topics?
    • This allows you to track images on the internet and find the source of images
  • Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images?
    • IPython allows for easy manipulation of data, in the context of images, it allows you to store them efficiently and manipulate them easily.
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': "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)
    smiley = image_data(images=[{'source': "Internet", 'label': "smiley", 'file': "smiley.jpg"}])
    image_display(smiley)
    
    # display default images from image_data()
    default_images = image_data()
    image_display(default_images)
    

Reading and Encoding Images (2 implementations follow)

PIL (Python Image Library)

Pillow or PIL provides the ability to work with images in Python. Geeks for Geeks shows some ideas on working with images.

base64

Image formats (JPG, PNG) are often called *Binary File formats, it is difficult to pass these over HTTP. Thus, base64 converts binary encoded data (8-bit, ASCII/Unicode) into a text encoded scheme (24 bits, 6-bit Base64 digits). Thus base64 is used to transport and embed binary images into textual assets such as HTML and CSS.- How is Base64 similar or different to Binary and Hexadecimal?

  • Translate first 3 letters of your name to Base64.

numpy

Numpy is described as "The fundamental package for scientific computing with Python". In the Image Lab, a Numpy array is created from the image data in order to simplify access and change to the RGB values of the pixels, converting pixels to grey scale.

io, BytesIO

Input and Output (I/O) is a fundamental of all Computer Programming. Input/output (I/O) buffering is a technique used to optimize I/O operations. In large quantities of data, how many frames of input the server currently has queued is the buffer. In this example, there is a very large picture that lags.

  • Where have you been a consumer of buffering?
    • Most often this happens to me on video streaming websites like youtube, or vimeo
  • From your consumer experience, what effects have you experienced from buffering?
    • slower videos, longer loading times, more ram usage
  • How do these effects apply to images?
    • images load slower but more efficiently

Data Structures, Imperative Programming Style, and working with Images

Introduction to creating meta data and manipulating images. Look at each procedure and explain the the purpose and results of this program. Add any insights or challenges as you explored this program.

  • Does this code seem like a series of steps are being performed?
    • yes, the steps seem to be printing the meta data, displaying the regular image, and displaying the grey scale image.
  • Describe Grey Scale algorithm in English or Pseudo code?
    • For Each Pixel in Image {

Red = Pixel.Red; Green = Pixel.Green; Blue = Pixel.Blue;

Gray = (Red + Green + Blue) / 3

Pixel.Red = Gray; Pixel.Green = Gray; Pixel.Blue = Gray;

}

  • Describe scale image? What is before and after on pixels in three images?
    • scale image increases or decreases the size of each pixel in an image, first image is scaled up, second and third images are scaled down and compressed.
  • Is scale image a type of compression? If so, line it up with College Board terms described?
    • Scale image down normally lossy compression by lowering the resolution of the image.
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': "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

# 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
    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()
---- meta data -----
Green Square
Internet
PNG
RGBA
Original size:  (16, 16)
Scaled size:  (320, 320)
-- original image --
--- grey image ----
---- meta data -----
Clouds Impression
Peter Carolin
PNG
RGBA
Original size:  (320, 234)
Scaled size:  (320, 234)
-- original image --
--- grey image ----
---- meta data -----
Lassen Volcano
Peter Carolin
JPEG
RGB
Original size:  (2792, 2094)
Scaled size:  (320, 240)
-- original image --
--- grey image ----

Data Structures and OOP

Most data structures classes require Object Oriented Programming (OOP). Since this class is lined up with a College Course, OOP will be talked about often. Functionality in remainder of this Blog is the same as the prior implementation. Highlight some of the key difference you see between imperative and oop styles.

  • Read imperative and object-oriented programming on Wikipedia
    • both use classes and definitions to support procedural programming
  • Consider how data is organized in two examples, in relations to procedures
    • relational databases organize data into rows and columns in the form of a table and then is structured across multiple tables to do something
  • Look at Parameters in Imperative and Self in OOP

Additionally, review all the imports in these three demos. Create a definition of their purpose, specifically these ...

  • PIL
    • adds support for opening, manipulating, and saving many different image file formats. PIL provides a set of standard image processing algorithms like resizing, cropping, filtering, and enhancing images
  • numpy
    • support for large, multi-dimensional arrays and matrices, as well as a wide range of mathematical operations that can be performed on these arrays
  • base64
    • In Python, the base64 module provides functions for encoding and decoding data using the base64 scheme
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


class Image_Data:

    def __init__(self, source, label, file, path, baseWidth=320):
        self._source = source    # variables with self prefix become part of the object, 
        self._label = label
        self._file = file
        self._filename = path / file  # file with path
        self._baseWidth = baseWidth

        # Open image and scale to needs
        self._img = pilImage.open(self._filename)
        self._format = self._img.format
        self._mode = self._img.mode
        self._originalSize = self.img.size
        self.scale_image()
        self._html = self.image_to_html(self._img)
        self._html_grey = self.image_to_html_grey()


    @property
    def source(self):
        return self._source  
    
    @property
    def label(self):
        return self._label 
    
    @property
    def file(self):
        return self._file   
    
    @property
    def filename(self):
        return self._filename   
    
    @property
    def img(self):
        return self._img
             
    @property
    def format(self):
        return self._format
    
    @property
    def mode(self):
        return self._mode
    
    @property
    def originalSize(self):
        return self._originalSize
    
    @property
    def size(self):
        return self._img.size
    
    @property
    def html(self):
        return self._html
    
    @property
    def html_grey(self):
        return self._html_grey
        
    # Large image scaled to baseWidth of 320
    def scale_image(self):
        scalePercent = (self._baseWidth/float(self._img.size[0]))
        scaleHeight = int((float(self._img.size[1])*float(scalePercent)))
        scale = (self._baseWidth, scaleHeight)
        self._img = self._img.resize(scale)
    
    # PIL image converted to base64
    def image_to_html(self, img):
        with BytesIO() as buffer:
            img.save(buffer, self._format)
            return '<img src="data:image/png;base64,%s">' % base64.b64encode(buffer.getvalue()).decode()
            
    # Create Grey Scale Base64 representation of Image
    def image_to_html_grey(self):
        img_grey = self._img
        numpy = np.array(self._img.getdata()) # PIL image to numpy array
        
        grey_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 numpy:
            # 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:
                grey_data.append((average, average, average, pixel[3])) # PNG format
            else:
                grey_data.append((average, average, average))
            # end for loop for pixels
            
        img_grey.putdata(grey_data)
        return self.image_to_html(img_grey)

        
# prepares a series of images, provides expectation for required contents
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"}
        ]
    return path, images

# turns data into objects
def image_objects():        
    id_Objects = []
    path, images = image_data()
    for image in images:
        id_Objects.append(Image_Data(source=image['source'], 
                                  label=image['label'],
                                  file=image['file'],
                                  path=path,
                                  ))
    return id_Objects

# Jupyter Notebook Visualization of Images
if __name__ == "__main__":
    for ido in image_objects(): # ido is an Imaged Data Object
        
        print("---- meta data -----")
        print(ido.label)
        print(ido.source)
        print(ido.file)
        print(ido.format)
        print(ido.mode)
        print("Original size: ", ido.originalSize)
        print("Scaled size: ", ido.size)
        
        print("-- scaled image --")
        display(HTML(ido.html))
        
        print("--- grey image ---")
        display(HTML(ido.html_grey))
        
    print()
---- meta data -----
Green Square
Internet
green-square-16.png
PNG
RGBA
Original size:  (16, 16)
Scaled size:  (320, 320)
-- scaled image --
--- grey image ---
---- meta data -----
Clouds Impression
Peter Carolin
clouds-impression.png
PNG
RGBA
Original size:  (320, 234)
Scaled size:  (320, 234)
-- scaled image --
--- grey image ---
---- meta data -----
Lassen Volcano
Peter Carolin
lassen-volcano.jpg
JPEG
RGB
Original size:  (2792, 2094)
Scaled size:  (320, 240)
-- scaled image --
--- grey image ---

Hacks

Early Seed award

  • Add this Blog to you own Blogging site.
  • In the Blog add a Happy Face image.
  • Have Happy Face Image open when Tech Talk starts, running on localhost. Don't tell anyone. Show to Teacher.

AP Prep

  • In the Blog add notes and observations on each code cell that request an answer.
  • In blog add College Board practice problems for 2.3
  • Choose 2 images, one that will more likely result in lossy data compression and one that is more likely to result in lossless data compression. Explain.

Project Addition

  • If your project has images in it, try to implement an image change that has a purpose. (Ex. An item that has been sold out could become gray scale)

Pick a programming paradigm and solve some of the following ...

  • Numpy, manipulating pixels. As opposed to Grey Scale treatment, pick a couple of other types like red scale, green scale, or blue scale. We want you to be manipulating pixels in the image.
  • Binary and Hexadecimal reports. Convert and produce pixels in binary and Hexadecimal and display.
  • Compression and Sizing of images. Look for insights into compression Lossy and Lossless. Look at PIL library and see if there are other things that can be done.
  • There are many effects you can do as well with PIL. Blur the image or write Meta Data on screen, aka Title, Author and Image size.

red scale with numpy and PIL

import numpy as np
from PIL import Image

# Load image
img = Image.open("images/smiley.jpg")

# Convert image to NumPy array
img_array = np.array(img)

# Create a copy of the array to manipulate
red_array = img_array.copy()

# Set green and blue channels to zero
red_array[:,:,1] = 0
red_array[:,:,2] = 0

# Multiply red channel by scalar value
red_array[:,:,0] = red_array[:,:,0] * 1.5

# Convert NumPy array back to image
red_img = Image.fromarray(np.uint8(red_array))

# Display the image
red_img.show()