Unit 2.2 Data Compression, Images
- 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
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, rm-rf What are the command you use in Windows terminal to access files?
dir, rmdir, mkdir</p> </li>What are some of the major differences?
the differences between windows and linux the dir command vs the ls, they both list </p> </li> </ul>Provide what you observed, struggled with, or leaned while playing with this code.
Why is path a big deal when working with images?
you have to identify which file the image is in. It is especially important when you want to edit the image</p> </li>How does the meta data source and label relate to Unit 5 topics?
metadata is the information embeded in a image file. and the labeling is used to assign descriptive tags to certain files to help identify and classify them</p> </li>Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images?
IPython is a command shell for interactive computing that is now known as jupyter notebook. It is interesting for pandas and images because it helps us to express the metadata from the images into a datastructure(table) so that we can analyze or change it.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.png"}, {'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "volcano.jpg"}, {'source': "Claire Chen", 'label': "Smiley Face", 'file': "smiley.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.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 represents the most characters as it represents letters, numbers, and some symbols. Binary only does 0 or 1 and hexadecimals only 16 digits (0-9-A-F)</p> </blockquote>- Translate first 3 letters of your name to Base64.
The Base64 encoding of "cla" is "Y2xh".</li> </ul>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
</div> </div> </div>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?
when logging into synergy, it buffers during the sign in - From your consumer experience, what effects have you experienced from buffering?
it is kind of annoying because you have to wait for it to load to do whatever it is you're trying to do - How do these effects apply to images?
these affects apply to images because when loading images the buffer depends on how big the file is. Usually lossy files take a shorter time to load because it is compressed which makes the file smaller.
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</li> </ul> </blockquote>Describe Grey Scale algorithm in English or Pseudo code?
It converts a color image into a grayscale by taking the average of the red, green, and blue values of each pixel in the color image and setting the resulting value as the new gray value for that pixel.</p> </li>Describe scale image? What is before and after on pixels in three images?
Scaling an image is when you resize it by a certain factor horizontaly and/or vertically . The scale factor has to be greater than 1 to make the image bigger, but less than 1 to shrink the image. </p> </li>Is scale image a type of compression? If so, line it up with College Board terms described?
scaling an image is not a type of compression because although it reduces the size of the image itself, it doesn't reduce the amount of data in that image or reduce the actual file size. </p> </li> </ul> </div> </div> </div>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.png"}, {'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds.png"}, {'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "volcano.jpg"}, {'source': "Claire Chen", 'label': "Smiley Face", 'file': "smiley.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 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.png"}, {'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds.png"}, {'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "volcano.jpg"}, {'source': "Claire Chen", 'label': "Smiley Face", 'file': "smiley.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.
Lossless: image is fully restored even when you change the size
ex. a logo that is shown in multiple parts of the a page in different sizes
Lossy: techniques that reduce file size by discarding the less important information
ex. sending image files (email, dm) it might ask you what size you want the image to be. If you were to use lossless for sending files it might take a long time to send because the file might be too big.
</div>import numpy as np from PIL import Image # Load the image image = Image.open('images/smiley.png') # Add a title to the image image.info['Title'] = 'Redscaled' # Convert the image to a NumPy array img_array = np.asarray(image) # Convert the array to binary representation binary_pixels = np.unpackbits(img_array, axis=-1) # Convert the binary representation to hexadecimal hex_pixels = np.apply_along_axis(lambda x: hex(int(''.join(map(str, x)), 2))[2:].zfill(2), -1, binary_pixels) # Display the binary and hexadecimal pixels print("Binary pixels:\n", binary_pixels) print("Hexadecimal pixels:\n", hex_pixels) # Create a copy of the array red_img = np.copy(img_array) # Set the green and blue channels to 0, leaving only the red channel red_img[:, :, 1] = 0 red_img[:, :, 2] = 0 # Convert the NumPy array back to an image red_image = Image.fromarray(red_img) # Save the red-scale image red_image.save('images/smiley.png') # Resize the image to half its original size resized_image = red_image.resize((red_image.width // 2, red_image.height // 2)) print(image.info) # Show the resized image resized_image.show()
- Where have you been a consumer of buffering?