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
- Happy Face with Markdown!!
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
Not all file types can be compressed, resized, or otherwise altered in the same way. For instance, the sort of compression used by jpg and png files could be different. PNG support transparent backgrounds. JPG contains lesser data than PNG and are usually smaller in size.
-
Size, height and width, number of pixels
The image files height, width, and size can be adjusted by compressing. In the below hacks I reduced width and height of my original happyface.JPG file.
-
Visual perception, lossy compression
When an image's file size is reduced using lossy compression, the visual experience of the image suffers. For example: the image quality becomes less crisp and clear.
Python Libraries and Concepts used for Jupyter and Files/Directories
Introduction to displaying images in Jupyter notebook
IPython
Support visualization of data in Jupyter notebooks. Visualization is specific to View, for the web visualization needs to be converted to HTML.
pathlib
File paths are different on Windows versus Mac and Linux. This can cause problems in a project as you work and deploy on different Operating Systems (OS's), pathlib is a solution to this problem.
- What are commands you use in terminal to access files?
cd to change directory ls to list the files in a directory cat to read the insides of a file through terminal. vi to edit the files
-
What are the command you use in Windows terminal to access files?
cd is used to change directory dir is used to list the files in a directory.
-
What are some of the major differences?
commands to view files are different Linux uses forward slashes while Windows uses backwards slashes, that makes the absolute directory paths different between the two OSs
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 concern, as all images are stored in folders, and to view those images, exact path has to be provided.
-
How does the meta data source and label relate to Unit 5 topics?
Unit 5 is about program that can analyze data. The metadata source and label relates to these topics. The metadata contains all the unique information from the image and the data can be outputted from the program onto the screen
-
Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images?
IPython is a Python interactive shell which can be utilized to display images. Pandas can be used to extract metadata from the images Pandas can be used to calculate mean, median, mode, and other statistic data
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': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"},
{'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "lassen-volcano.jpg"}
]
for image in images:
# File to open
image['filename'] = path / image['file'] # file with path
return images
def image_display(images):
for image in images:
display(Image(filename=image['filename']))
# Run this as standalone tester to see sample data printed in Jupyter terminal
if __name__ == "__main__":
# print parameter supplied image
green_square = image_data(images=[{'source': "Internet", 'label': "Green Square", 'file': "green-square-16.png"}])
image_display(green_square)
# 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. PIL (Python Imaging Library) is an open-source library for image processing tasks that requires python programming language. PIL can perform tasks on an image such as reading, rescaling, saving in different image formats. PIL can be used for Image archives, Image processing, Image display.
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 hex, since it is an encoding format which can convert a normal string/number into a series of encoded characters. It is different from binary and hexadecimal in the base that it uses. Base64 uses base 64 (0-63), while binary uses base 2 (0-1) and hexadecimal uses base 16 (0-9 and A-F).
-
Translate first 3 letters of your name to Base64.
See the below code snippet
import base64
sample_string = "Nav"
sample_string_bytes = sample_string.encode("ascii")
base64_bytes = base64.b64encode(sample_string_bytes)
base64_string = base64_bytes.decode("ascii")
print(f"Encoded string: {base64_string}")
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. Numpy is a python library used for working with arrays and also provides mathematical functions like, sqrt, sin, etc.
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 seen buffering mostly when streaming videos online either on TV or on mobile phones
-
From your consumer experience, what effects have you experienced from buffering?
Streaming lags continuity and videos are not played continuously
-
How do these effects apply to images?
Buffering can be used to add some edits to the features of images, such as, scaling - grey and red scale
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 are being performed to manipulate the images
-
Describe Grey Scale algorithm in English or Pseudo code?
All grayscale algorithms use the same basic three-step process: Get the red, green, and blue values of a pixel. Calculate the Average RGB of those numbers into a single gray value. Replace the original red, green, and blue values with the new gray value.
-
Describe scale image?
Scale image is a feature allowing for the images to get scaled up or down in size. For example both base height and width of bigger image are scaled down. When scaling a image, a new image with a higher or lower number of pixels is generated. In the case of decreasing the pixel number (scaling down) this usually results in a visible quality loss.
-
What is before and after on pixels in three images?
Before scaling, the pixels are of a larger size, but after the averages of all the pixels are taken, they are compressed down to a smaller size. In the example below, here is the scaled and original sizes-
Green Square Original size: (16, 16) Scaled size: (320, 320)
Clouds Impression Original size: (320, 234) Scaled size: (320, 234)
-
Is scale image a type of compression? If so, line it up with College Board terms described?
Yes, scaling image ia a type of lossy compression. The image file size is reduced and also the image quality is compromised.
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"}
]
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
PIL (Python Imaging Library) is an open-source library for image processing tasks that requires python programming language. PIL can perform tasks on an image such as reading, rescaling, saving in different image formats. PIL can be used for Image archives, Image processing, Image display. PIL is used to manipulate imported image properties.
-
numpy
Numpy is a python library used for working with arrays and also provides mathematical functions like, sqrt, sin, etc.
-
base64
Encoding algorithm representing binary data as text
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.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 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': "Navan Happy Face", 'file': "happyface.jpg", 'year' : "2023"},
]
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 other Scale Base64 representation of Image
def image_management_add_html_other_scales(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
image['red_data'] = [] # key/value for data converted to red scale
image['green_data'] = [] # key/value for data converted to green scale
image['blue_data'] = [] # key/value for data converted to green 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
image['red_data'].append((pixel[0]+25, average, average, pixel[3])) # PNG format
image['green_data'].append((average, pixel[0]+25, average, pixel[3])) # PNG format
image['blue_data'].append((average, average, pixel[0]+25, pixel[3])) # PNG format
else:
image['gray_data'].append((average, average, average))
image['red_data'].append((pixel[0]+25, average, average))
image['green_data'].append((average, pixel[0]+25, average))
image['blue_data'].append((average, average, pixel[0]+25))
# 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)
img.putdata(image['red_data'])
image['html_red'] = '<img src="data:image/png;base64,%s">' % image_to_base64(img, format)
img.putdata(image['green_data'])
image['html_green'] = '<img src="data:image/png;base64,%s">' % image_to_base64(img, format)
img.putdata(image['blue_data'])
image['html_blue'] = '<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(image['year'])
print("Original size: ", image['size'])
print("Scaled size: ", image['scaled_size'])
print("-- original image --")
display(HTML(image['html']))
image_management_add_html_other_scales(image)
print("--- grey image ----")
display(HTML(image['html_grey']))
print("--- red image ----")
display(HTML(image['html_red']))
print("--- green image ----")
display(HTML(image['html_green']))
print("--- blue image ----")
display(HTML(image['html_blue']))
print()
Programming Paradigm Explanation
I used NumPy array to access and change to the RGB values of the pixels of Happy Face image.
Converted the happy face to red scale, green scale, or blue scale.
For red scale I added Red=+25 pixel color values but kept average values for Green and Blue scale.
For blue scale I added Blue=+25 pixel color values but kept average values for Green and Red scale.
For green scale I added Green=+25 pixel color values but kept average values for Red and Blue scale.
Printed meta data of the Happy Face like name, size etc.
Also added an extra meta field of Year.
Two Images representing Lossy and Lossless Compression
Lossy Compression
The lassen-volcano.jpg image will represent lossy compression. This is because if the image file is compressed, it will definitely have a drastically reduced file size, but the image quality will also go down, due to the very fine details in the image, and the variety of colors. It will likely become more fuzzy.
Lossless Compression
The green-square-16.png is an example of lossless compression. That image has one sole color, so the RGB pixel values are going to be the same every time, and the quality of the image will not deteriorate, while the image file size will still reduce