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.
- 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
- Notes, Questions/Answers, and etc.
- College Board Practice Quiz:
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
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? ls, cd (filename) are both commands that can be used in the terminal to access files.
- What are the command you use in Windows terminal to access files? cd, dir, and mkdir are some of the commands that can be used in the Windows terminal to access files.
- What are some of the major differences? Both are similar, but some of the major differences include the commands themselves, which do different things and access different parts of the device.
Provide what you observed, struggled with, or leaned while playing with this code.
- Why is path a big deal when working with 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?
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("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.jpg"},
{'source': "Keira", 'label': "Smiley face", 'file': "smiley 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.
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?
- From your consumer experience, what effects have you experienced from buffering?
- How do these effects apply to images?
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?
- Describe Grey Scale algorithm in English or Pseudo code?
- Describe scale image? What is before and after on pixels in three images?
- Is scale image a type of compression? If so, line it up with College Board terms described?
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.jpg"},
{'source': "Keira", 'label': "Smiley face", 'file': "smiley 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()
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
# This is basically the same as backend/frontend type coding for the project/server
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.jpg"},
{'source': "Keira", 'label': "Smiley face", 'file': "smiley 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()
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.
Notes, Questions/Answers, and etc.
- Lossy - Losing data, data that gets lost from the original and cannot be fully recovered; discards some of the image data to achieve smaller file sizes
- Lossless - Some data is lost, restores and recovers data; maintain all the original image data but may result in larger file sizes
- Grayscale takes the average of the pixel color values and divides them by 3, making the image gray
- The metadata is the information about the image, such as the location the image was taken (if it is a picture) or who the creator is
- Object-oriented programming using data structures can also be used to display images (as in, frontend/backend with get, update, pull, delete, etc.), this basically pulls the image data from the backend to the frontend
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 - There are different types of image files such as PNG, JPEG, BMP, TIFF, etc. Each file type has its own compression method, level of quality, and compatibility with different platforms.
- Size, height and width, number of pixels - These parameters describe the dimensions and number of pixels in an image. More pixels usually mean higher resolution, but also larger file sizes. Size is the amount of disk space an image takes up. Height and width refer to the number of pixels in the vertical and horizontal directions, respectively.
- Visual perception, lossy compression - Data compression is a technique used to reduce the size of image files. Lossy compression algorithms discard some of the image data to achieve smaller file sizes. Lossless compression algorithms maintain all the original image data but may result in larger file sizes. Visual perception of an image can be influenced by the amount of pixels in it and how it is compressed.
What are commands you use in terminal to access files? ls, cd (filename) are both commands that can be used in the terminal to access files.
What are the command you use in Windows terminal to access files? cd, dir, and mkdir are some of the commands that can be used in the Windows terminal to access files.
What are some of the major differences? Both are similar, but some of the major differences include the commands themselves, which do different things and access different parts of the device.
Why is path a big deal when working with images? If the path is wrong, the code won't be able to locate the image, because it can't find the right pathway.
How does the meta data source and label relate to Unit 5 topics? Metadata can be used to protect user info by hiding things such as IP addresses to protect user privacy.
Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images? IPython is a command-line shell that is used for Python, and it provides more features. For notebooks, IPython is used as the default kernal to execute cells. For Pandas, it allows the user to view/manipulate large datasets more easily, and for images it allows for images to be displayed in the program.
Translate first 3 letters of your name to Base64. (See below) S2Vp
Where have you been a consumer of buffering? I have been a consumer of buffering in websites that have lots of popups or parts to them.
From your consumer experience, what effects have you experienced from buffering? It's much harder to have a good consumer experience when things buffer for too long, as it's difficult to do things on the site.
How do these effects apply to images? When images don't load, it's difficult to see what you're buying, so you'll have difficulting deciding what to buy.
Does this code seem like a series of steps are being performed? Yes, multiple parts are needed for a final image to be produced.
Describe Grey Scale algorithm in English or Pseudo code? The grayscale algorithm takes the pixels (the numbers for the RGBs) and divides them so that the saturation decreases. The pixels are then averaged to create the full image.
Describe scale image? What is before and after on pixels in three images? A scale image changes the size of an image. The pixels get removed, so the image quality decreases.
Is scale image a type of compression? If so, line it up with College Board terms described? Yes, it can be related to lossy compression, where some information is lost in the process of compression, but the overall image remains recognizable.
Additionally, review all the imports in these three demos. Create a definition of their purpose, specifically these:
PIL - (Python Imaging Library) A library in Python used for manipulating images.
numpy - (Numerical Python) A Python library that supports large arrays and matrices, and mathmatical functions.
base64 - A binary-to-text encoding scheme that represents binary data in an ASCII string format.
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
- This image is more likely to result in lossy data compression, beacuse it's an image that has very little detail in it, and it is very simple. Lossy data compression may result in the image losing some of its detail, and making the image much lower quality as a result.
Lossless
- This image is more likely to result in lossless data compression, because the image is highly realistic and has many details in it, which is difficult for lossless data compression to keep up with.
College Board Practice Quiz:
Corrections:
-
For the first question, I should have answered B instead of A. I thought that a lossless compression would keep the information the same, equating it to keeping the information secure. The answer is actually B, because lossless compression ensures that the data can be reconstructed.
-
For the second question, I got confised and assumed that compression was done for both lossy and lossless files, but it was only done for lossless compressions, not both.
import base64
name = "Kei"
translation = base64.b64encode(name.encode('utf-8')).decode('utf-8')
print(translation)
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 PIL import Image, ImageFilter
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': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"},
{'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "lassen-volcano.jpg"},
{'source': "Keira", 'label': "Smiley face", 'file': "smiley 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 = 200
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['redShift_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] + 200) # average pixel values and use // for integer division
if len(pixel) > 3:
image['redShift_data'].append((average, pixel[1], pixel[2], pixel[3])) # PNG format
else:
image['redShift_data'].append((average, pixel[1], pixel[2]))
# end for loop for pixels
img.putdata(image['redShift_data'])
image['html_redShift'] = '<img src="data:image/png;base64,%s">' % image_to_base64(img, format)
def blurImage(image):
img = image['pil'] #Opens existing image
return img.filter(ImageFilter.BoxBlur(5)) # blurs the image that was input
# 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("--- redshifted image ----")
image_management_add_html_grey(image)
display(HTML(image['html_redShift']))
print("--- blurred image ----")
blur = blurImage(image)
display(blur)
print()
