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.
- Notes
- 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 ...
- College Board 2.2 Problems
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?
- What are the command you use in Windows terminal to access files?
- What are some of the major differences?
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("images/"), images=None): # path of static images is defaulted
if images is None: # default image
images = [
{'source': "Internet", 'label': "Clouds Impression", 'file': "clouds-impression.png"},
{'source': "Internet", '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.
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?
Answer: 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.
Answer: Shr in Base64 is U2hy
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?
Answer:I have been a consumer of buffering when I was trying to load up a website full of fitness data last trimester on a flask website. Sometimes, the website was too slow.
- From your consumer experience, what effects have you experienced from buffering?
Answer: I was very annoyed because the website was super slow.
- How do these effects apply to images?
Answer: 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?
Answer: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?
Answer: 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?
Answer: 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?
Answer: 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': "Gsquare", 'file': "green-square-16.png"},
{'source': "Peter Carolin", 'label': "Cimpression", 'file': "clouds-impression.png"},
{'source': "Peter Carolin", 'label': "Lvolcano", '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_pink(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['pink_data'] = [] # key/value for data converted to pink 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['pink_data'].append((average, 0, average//2, pixel[3])) # PNG format
else:
image['pink_data'].append((average, 0, average//2))
# end for loop for pixels
img.putdata(image['pink_data'])
image['html_pink'] = '<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("--- pink image ----")
image_management_add_html_pink(image)
display(HTML(image['html_pink']))
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
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, 0, average//2, 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': "Gsquare", 'file': "green-square-16.png"},
{'source': "Internet", 'label': "Cimpression", 'file': "clouds-impression.png"},
{'source': "Internet", 'label': "Lvolcano", '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()
import numpy as np
from PIL import Image as pilImage
from io import BytesIO
import base64
from IPython.display import HTML, display
# Load image
img = pilImage.open("images/clouds-impression.png")
# Convert image to numpy array
img_array = np.array(img)
# Create red and blue scale versions of image
red_scale = img_array.copy()
red_scale[:, :, 1] = 0 # set green channel to 0
red_scale[:, :, 2] = 0 # set blue channel to 0
blue_scale = img_array.copy()
blue_scale[:, :, 0] = 0 # set red channel to 0
blue_scale[:, :, 1] = 0 # set green channel to 0
# Convert numpy arrays back to images
red_scale_img = pilImage.fromarray(red_scale)
blue_scale_img = pilImage.fromarray(blue_scale)
# Convert images to base64 for display
def image_to_base64(img, format):
with BytesIO() as buffer:
img.save(buffer, format)
return base64.b64encode(buffer.getvalue()).decode()
red_scale_html = '<img src="data:image/png;base64,%s">' % image_to_base64(red_scale_img, img.format)
blue_scale_html = '<img src="data:image/png;base64,%s">' % image_to_base64(blue_scale_img, img.format)
# Display images
display(HTML(red_scale_html))
display(HTML(blue_scale_html))
(1) Which of the following is an advantage of a lossless compression algorithm over a lossy compression algorithm?
Possible Answers:
(A) A lossless compression algorithm can guarantee that compressed information is kept secure, while a lossy compression algorithm cannot.
(B) A lossless compression algorithm can guarantee reconstruction of original data, while a lossy compression algorithm cannot.
(C) A lossless compression algorithm typically allows for faster transmission speeds than does a lossy compression algorithm.
(D) A lossless compression algorithm typically provides a greater reduction in the number of bits stored or transmitted than does a lossy compression algorithm.
Correct answer: B
(2) A user wants to save a data file on an online storage site. The user wants to reduce the size of the file, if possible, and wants to be able to completely restore the file to its original version. Which of the following actions best supports the user’s needs?
Possible Answers:
(A) Compressing the file using a lossless compression algorithm before uploading it
(B) Compressing the file using a lossy compression algorithm before uploading it
(C) Compressing the file using both lossy and lossless compression algorithms before uploading it
(D) Uploading the original file without using any compression algorithm
Correct answer: A
(3) A programmer is developing software for a social media platform. The programmer is planning to use compression when users send attachments to other users. Which of the following is a true statement about the use of compression?
Possible Answers:
(A) Lossless compression of video files will generally save more space than lossy compression of video files.
(B) Lossless compression of an image file will generally result in a file that is equal in size to the original file.
(C) Lossy compression of an image file generally provides a greater reduction in transmission time than lossless compression does.
(D) Sound clips compressed with lossy compression for storage on the platform can be restored to their original quality when they are played.
Correct answer: C