Unit 2.2 Data Compression, Images
Lab will perform alterations on images, manipulate RGB values, and reduce the number of pixels. College Board requires you to learn about Lossy and Lossless compression.
- What is Compression
- Enumerate "Data" Big Idea from College Board
- Image Files and Size
- Python Libraries and Concepts used for Jupyter and Files/Directories
- Reading and Encoding Images (2 implementations follow)
- Data Structures, Imperative Programming Style, and working with Images
- Data Structures and OOP
- Additionally, review all the imports in these three demos. Create a definition of their purpose, specifically these ...
- Hacks
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
imagesdirectory 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
Some of the meta data and considerations when managing image files have to do with the type of the file, the size of the image, the number of pixels that comprise and image, and how detailed the image is. All these factors influence how the quality of the image changes when it undergoes data 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.
- Allows you to take the image from the folder and make it show up in the notebook
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.
- Allows you to get access to the directory
- Helps access the images
- What are commands you use in terminal to access files?
- cd ~/[file path]
- ls (to view what files are there)
- What are the command you use in Windows terminal to access files?
- dir
- enter
- code .. (to open vscode files from terminal)
- What are some of the major differences?
- in the jupyter notebooks, we have to use
returnfunctions to get the images
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 when working with images, because images are stored within certain files, and you need the correct path in order to open the files and extract the images.
- How does the meta data source and label relate to Unit 5 topics?
- ???
- Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images?
- a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers introspection, rich media, shell syntax, tab completion, and history
- you can use it to print images
from IPython.display import Image, display #IPython.dislay allows you to take the image from the folder and make it show up in the notebook
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("ghtop_images/"), images=None): # path of static images is defaulted; using images folder within _notebooks directory
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': "Pinterest", 'label': "Happy Face", 'file': "happyface.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)
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. - allows you to put text on top of 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?
- Every digit in hexadecimal corresponds to a number from 0-9 and then switches to letters - Binary uses 0s and 1s - base64 has the alphabet as lowercase and us uppercase, has numbers, and even has symbols to build up to 64 unique characters --> that's how images are created; usually encoded within 6 bits
- Translate first 3 letters of your name to Base64
- S: Uw==
- H: SA==
- R: Ug==
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.- Shows you the images
- Allows you to change the values of the rgb; values are stores together in an array --> change the values to change the colors of the images
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.
- You can look at how the images are inputted vs. how they are outputted
- Where have you been a consumer of buffering?
- When searching things on the internet
- When playing video games
- When streaming movies/TV
- From your consumer experience, what effects have you experienced from buffering?
- Sometimes I get annoyed when things start buffering, and will exit out of the site or the streaming site. Also, when I used to play some video games and buffering would occur, I would end up losing.
- How do these effects apply to images?
- Buffering helps que up frames of input to the server so that images can be 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?
- It seems like the images are being defined based on their file name.
- Then, the path within which the image file is contained is called, and the images are returned.
- Then, a function is created which scales the image, and the resized image is returned.
- Then, the PIL image is converted to base64 and buffered to the server.
- Then, the image is grayscaled using an array.
- Then, the meta data, scaled image, and grayscaled version of each image is printed.
- Describe Grey Scale algorithm in English or Pseudo code?
- First, open and return the PIL image object
- Then, create a list of RGB data called 'data', and create a PIL image to numpy array.
- Then, create an image key/value for the data converted to a gray scaled image.
- Next, create a for loop with a nested if-else statement to take an average of the pixels to create a gray scaled images.
- Then, append the image key/value for the data converted to a gray scaled image by assigning the copy of the image that underwent the grayscaling to the key's value.
- Lastly, print the image into the Jupyter notebook.
- Describe scale image? What is before and after on pixels in three images?
- The scale image command changes the physical size of the image, specifically by changing the number of pixels it contains.
- The width or height of the image can be changed.
- Before:normal color - After: grayscaled images
- Before: normal sized image
- After: image with an altered height/weight
- Is scale image a type of compression? If so, line it up with College Board terms described?
- Scaling an image is an example of lossless data compression. Resizing the images to smaller widths/heights does not alter image quality.
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("ghtop_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-original.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): # created a function that scales the image
baseWidth = 320 # only one of the dimensions is being scaled; always set either the length or a width as a constant
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; divide by 3 to get the grayscale
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']) # giving information about the image that you have, such as source and 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()
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
- creating a database of the images
- different functions that return the different images
- taking the images one at a time and output them
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("ghtop_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-original.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.
from IPython.display import Image, display #IPython.dislay allows you to take the image from the folder and make it show up in the notebook
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("ghtop_images/"), images=None): # path of static images is defaulted; using images folder within _notebooks directory
if images is None: # default image
images = [
{'file': "datacompression1.png"},
{'file': "datacompression2.png"},
{'file': "datacompression3.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)
from PIL import Image
import io
import IPython.display as display
# Load the image
img = Image.open('ghtop_images/lossless.jpg')
# Resize the image
width, height = img.size
new_width = width // 4
new_height = height // 4
resized_img = img.resize((new_width, new_height))
# Display the resized image in Jupyter notebook
img_bytes = io.BytesIO()
resized_img.save(img_bytes, format='JPEG')
display.display(display.Image(data=img_bytes.getvalue()))
from PIL import Image
import io
import IPython.display as display
# Load the image
img = Image.open('ghtop_images/citylights.jpeg')
img_bytes = io.BytesIO()
resized_img.save(img_bytes, format='JPEG')
display.display(display.Image(data=img_bytes.getvalue()))
The image of the simple emoji is an example of an image that would most likely undergo lossless data compression, because it has only two solid colors and simple shapes to recreate. The quality of the image won't go down when it is compressed. On the other hand, the image of the city skyline at night is an example of an image that would most likely undergo lossy data compression, because it is a high-resolution image with a lot of detail and many colors and shapes to be recreated. It is easy for some of this data to be lost when compressing this image. The quality of the image will most likely go down when it is compressed, and it may look blurry as you can begin to see more of the pixels.
import numpy as np
from PIL import Image
import io
import IPython.display as display
# Load the image
img = Image.open('ghtop_images/lossy.jpg')
# Resize the image
width, height = img.size
new_width = width // 4
new_height = height // 4
resized_img = img.resize((new_width, new_height))
# Create a redscales version of the image by manipulating pixels directly
greenscale_img = resized_img.copy()
pixels = greenscale_img.load()
for i in range(greenscale_img.size[0]): # width
for j in range(greenscale_img.size[1]): # height
r, g, b = pixels[i, j]
pixels[i, j] = (0, r, 0)
# Display the red-scaled image in Jupyter notebook
img_bytes = io.BytesIO()
greenscale_img.save(img_bytes, format='JPEG')
display.display(display.Image(data=img_bytes.getvalue()))
