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?
- Mac users use cd and ls to access files.

  • What are the command you use in Windows terminal to access files?

    • Windows users also use cd and ls to access files. I can first ls to see what folders and files I have and then cd into them.
  • What are some of the major differences?

    • Windows uses the command C:\ to take you to the root directory. Windows users use the backslash and Mac users us the front slash. Provide what you observed, struggled with, or leaned while playing with this code.
  • Why is path a big deal when working with images?

    • Path is a big deal with images because it tells your computer where to find your image files. If the path is incorrect, the image will not be found and therefore not displayed.
  • How does the meta data source and label relate to Unit 5 topics?

    • Meta data source and label is a way of crediting owners of their work. This relates to the Unit 5 topics dealing with crediting people for their work and making sure different images have certain licenses and/or permissions.
  • Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images?

    • IPython is a useful and powerful shell. IPython allows for python code to be run in Jupyter Notebooks and display images and format using pandas.
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"},
            {'source': "Internet", 'label': "Happy Face", 'file': "happy.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.

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 Hexademical because it is a translation of characters that is held in bits. Base64 represents an ASCII string format of Binary data. It contains 64 different ASCII characters.

  • Translate first 3 letters of your name to Base64.

    • Ava in Base64 is QXZh. I used a translator.

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?
- I have been a consumer to buffering when trying to load up a shopping website. All of the items that I am trying to buy will load, but their images will take awhile and make the website slow.

  • From your consumer experience, what effects have you experienced from buffering?

    • From buffering, I have experienced annoyance when having to reload my page when shopping. The page will be super slow when I am trying to switch between items quickly. I have also seen images be displayed with super poor quality.
  • How do these effects apply to images?

    • Larger images will take add more to the queue, causing it to lag when being displayed.

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 this code seems like a series of steps being performed. First, the code accesses image data, and then will resize the images and make its alterations next. Sequencing is used to resize, change formatting, and edit data. 

  • Describe Grey Scale algorithm in English or Pseudo code?

    • Grey scale works by averaging out all of the RBG values from the numpy arrays causing the pixels and images to be grey.
  • Describe scale image? What is before and after on pixels in three images?

    • Scale image is a function that resizes images by resizing the base, and then the height is proportionally resized as well.
  • Is scale image a type of compression? If so, line it up with College Board terms described?

    • Yes scale image is a type of compression. There is lossy and lossless compression of images. When resizing your images, some images will loose data (lossy) and some will be able to have data retrieved again (lossless).
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 ----

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_green(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 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['green_data'].append((0, average, 0, pixel[3])) # PNG format
        else:
            image['green_data'].append((0, average, 0))
        # 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("---- 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_green(image)
        display(HTML(image['html_green'])) 
    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 ----

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_purple(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['purple_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 purple 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['purple_data'].append((average//2, 0, average//3, pixel[3])) # PNG format
        else:
            image['purple_data'].append((average//2, 0, average//3))
        # end for loop for pixels
        
    img.putdata(image['purple_data'])
    image['html_purple'] = '<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_purple(image)
        display(HTML(image['html_purple'])) 
    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 ----

Pasting images on top of each other

# with the help of ChatGPT
from PIL import Image
from pathlib import Path
def image_data(path=Path("images/"), images=None):
    if images is None:
        images = [
            {'source': "Internet", 'label': "hand out", 'file': "handout.jpg"},
            {'source': "Internet", 'label': "Apple", 'file': "apple.jpg"},
        ]
    for image in images:
        image['filename'] = path / image['file']  # file with path
    return images
# Call the image_data function to get the image data
image_data = image_data()
# Open the two images
background_image = Image.open(image_data[0]['filename'])
foreground_image = Image.open(image_data[1]['filename'])
# Resize the foreground image to fit on the background image
new_size = (200, 200) # You can adjust this to the size you want
foreground_image = foreground_image.resize(new_size)
# Paste the foreground image onto the background image at position (x, y)
x = 100
y = 200
background_image.paste(foreground_image, (x+80, y-110))
# Display the resulting image
background_image.show()

Blurring an Image

from pathlib import Path
from PIL import Image, ImageFilter
from IPython.display import display
def image_data(path=Path("images/"), images=None):
    if images is None:
        images = [
            {'source': "Internet", 'label': "Hand Out", 'file': "handout.jpg"}
        ]
    for image in images:
        # File to open
        image['filename'] = path / image['file']
        # Open the image
        img = Image.open(image['filename'])
        # Apply the blur filter
        img = img.filter(ImageFilter.BLUR)
        # Update the filename to the blurred image
        blurred_filename = "blurred-" + image['file']
        image['filename'] = path / blurred_filename
        # Save the blurred image
        img.save(image['filename'])
        # Display the blurred image
        display(img)
image_data()

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
  • numpy
  • base64
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()

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.

Lossy and Lossless Insight Notes

According to chat GPT:

  • Use the 'save' function with a lossless compression format like PNG or TIFF instead of lossy formats like JPEG. This will preserve all the original data without any loss.
  • If you need to use a lossy compression format like JPEG, you can control the level of compression using the 'save' function's 'quality' parameter. Higher quality levels will result in less data loss, but also larger file sizes.
  • You can also use the 'resize' function to reduce the image size before compression. This will reduce the amount of data that needs to be compressed, which can help to reduce the loss of data.
  • Another option is to use the 'filter' function to apply a smoothing or blurring filter to the image before compression. This can help to reduce the amount of detail in the image, which can make it easier to compress without losing too much data.
  • Finally, you can use the 'quantize' function to reduce the number of colors in the image. This can help to reduce the amount of data that needs to be compressed, which can help to reduce the loss of data. However, this can also result in a loss of detail and color accuracy in the image. 12:34

2 Images: Lossy and Lossless Data Compression

  • Images with higher details are more likely to lose data (lossy compression) when being compressed to due the averaging of pixels. An example of this is the mountain image above. For example, when being compressed, the details of the shading may be lost.

  • Simple images that have pixels that are all the same RBG will likely result in lossless data compression. For example, the green square only has one color throughout the image meaning when averaging the pixels' RBGs, the color will remain the same and can be expanded again.