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.

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

Displaying images in Python Jupyter notebook

Python Libraries and Concepts used for Jupyter and Files/Directories

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 to navigate. Use VIM or Nano to edit a file straight from the terminal.
  • What are the command you use in Windows terminal to access files? cd to navigate and typing a filename and extension to open the file in a default application.
  • What are some of the major differences? ll and ls don't work in Command Prompt, but do work in Windows Powershell. On Command Prompt, you need to use dir.

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

  • Why is path a big deal when working with images?

    It allows a program to access the image file it needs.

  • How does the meta data source and label relate to Unit 5 topics?

    Meta data could provide vital information about the creation of a file for reference in the case of legal concerns.

  • Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images?

    IPython can be used in conjunction with Pandas to perform analytic tasks and manipulate and display images.

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-256.jpg"},
            {'source': "Google", 'label': "Happy Face", 'file': "happy-face.png"}
        ]
    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)
    

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.

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? When I'm waiting for a Youtube video to load in an area with bad cell service.
  • From your consumer experience, what effects have you experienced from buffering?
    It's pretty annoying if you're impatient. The video quality suffers.
  • How do these effects apply to images? Image quality may be sacrificed to lower buffer time.

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? Base64 is similar to Binary and Hexadecimal because it's a way to store data using a unique string of characters. It's different because there are many more characters to choose from for each bit.

  • Translate first 3 letters of your name to Base64. See the code block below.
import base64

# TRANSLATION TO BASE64 FROM ALPHANUMERIC
def translateAN(name):
    # convert the string to bytes
    name_bytes = name.encode('ascii')
    # encode the bytes using Base64
    base64_bytes = base64.b64encode(name_bytes)
    # convert the Base64 bytes back to a string
    return name + " -> " + base64_bytes.decode('ascii')

print("Translating from Alphanumeric to Base64: \n \n" + translateAN("Aze") + "\n" + translateAN("Azeem") + "\n" + translateAN("Azeem Khan"))
Translating from Alphanumeric to Base64: 
 
Aze -> QXpl
Azeem -> QXplZW0=
Azeem Khan -> QXplZW0gS2hhbg==
import base64

# TRANSLATION TO ALPHANUMERIC FROM BASE64
def translateB64(name):
    # convert the string to bytes
    name_bytes = name.encode('ascii')
    # decode the bytes using Base64
    base64_bytes = base64.b64decode(name_bytes)
    # convert the Base64 bytes back to a string
    return name + " -> " + base64_bytes.decode('ascii')

print("Translating from Base64 to Alphanumeric: \n \n" + translateB64('QXpl') + "\n" + translateB64("QXplZW0=") + "\n" + translateB64("QXplZW0gS2hhbg=="))
Translating from Base64 to Alphanumeric: 
 
QXpl -> Aze
QXplZW0= -> Azeem
QXplZW0gS2hhbg== -> Azeem Khan

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, it is called an Imperative program
  • Describe Grey Scale algorithm in English or Pseudo code? It turns a colored image to a de-saturated image. It takes color information from each pixel and converts it to a shade of grey, depending on the luminance of the color of the pixel.
  • Describe scale image? What is before and after on pixels in three images? Scaling an image changes the size and pixel density of it. For example, upscaling usually causes quality loss because a high density of pixels is being blown out to a larger dimension, or less density.
  • Is scale image a type of compression? If so, line it up with College Board terms described? Yes, scaling an image can be considered a form of lossy compression as it reduces the file size by decreasing the number of pixels, which is similar to the College Board term "data compression."
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-256.jpg"},
            {'source': "Google", 'label': "Smiley Face", 'file': "happy-face.png"}
        ]
    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:  (320, 240)
Scaled size:  (320, 240)
-- original image --
--- grey image ----
---- meta data -----
Smiley Face
Google
PNG
RGBA
Original size:  (1280, 1280)
Scaled size:  (320, 320)
-- 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
  • Consider how data is organized in two examples, in relations to procedures
  • 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

    An image manipulation library. In this code, it is used to access an image from a source

  • numpy

    Lets you manipulate and analyze objects. In this case, it is used to grayscale the image(s) in a loop as it manipulates the RGB value of each pixel.

  • base64

    Is a character system used to encode images as strings. That is how it is being used in this code as well.

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-256.jpg"},
            {'source': "Google", 'label': "Happy Face", 'file': "happy-face.png"}
        ]
    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-256.jpg
JPEG
RGB
Original size:  (320, 240)
Scaled size:  (320, 240)
-- scaled image --
--- grey image ---
---- meta data -----
Happy Face
Google
happy-face.png
PNG
RGBA
Original size:  (1280, 1280)
Scaled size:  (320, 320)
-- 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

Q1 Q2

Q3

  • 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.

Lossy Image

A lossy image format is JPEG (Joint Photographic Experts Group). JPEG uses lossy compression techniques that get rid of some of the image's original data to reduce file size. The degree of compression can be adjusted, with higher compression ratios resulting in smaller file sizes but also in a greater loss of image quality. This means that when a JPEG image is saved with high compression, some of the details in the original image can be lost, resulting in a lower quality image.

Lossless Image

A lossless image format is PNG (Portable Network Graphics), which is a raster graphics file format that uses lossless compression techniques. This means that PNG files retain all the original data of the image when it is saved, resulting in a higher quality image without any loss of information. PNG images are often used for graphics that require high-quality images with transparent backgrounds, such as logos and icons.

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.
# Change hue and luminance of an 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': "Google", 'label': "Smiley Face", 'file': "../../images/carlol.jpg"},
            {'source': "Google", 'label': "Smiley Face", 'file': "../../images/IMG_3600-2.png"}
        ]
    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_up(img):
    baseWidth = 500
    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_up(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 Green 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['green_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 grey 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:
            # manipulate averages with +, -, *, or / to alter hue
            # Adding to average adds luminance and vice versa, so keep in mind what you are subtracting and adding
            image['green_data'].append((average-75, average+25, average-75, pixel[3])) # PNG format
        else:
            image['green_data'].append((average-75, average+25, average-75))
        # end for loop for pixels
        
    img.putdata(image['green_data'])
    image['html_green'] = '<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("-- original image --")
        display(HTML(image['html'])) 
        
        print("--- green image ----")
        image_management_add_html_grey(image)
        display(HTML(image['html_green'])) 
    print()
-- original image --
--- green image ----
-- original image --
--- green image ----

# Pixelate/blur an 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': "Google", 'label': "Smiley Face", 'file': "happy-face.png"}
        ]
    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_up(img):
    baseWidth = 700
    scalePercent = (baseWidth/float(img.size[0]))
    scaleHeight = int((float(img.size[1])*float(scalePercent)))
    scale = (baseWidth, scaleHeight)
    return img.resize(scale)

def scale_image_down(img):
    baseWidth = 20
    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_down(img)
    img = scale_image_up(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'])
    
# 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("---- scaling info -----")
        print("Original size: ", image['size'])
        print("Scaled size: ", image['scaled_size'])
        
        print("-- blurry image --")
        display(HTML(image['html']))  
    print()
---- scaling info -----
Original size:  (1280, 1280)
Scaled size:  (700, 700)
-- blurry image --