2.2 Images
Images printed with python from 2.2
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-impression.png"},
{'source': "Internet", 'label': "happy happy", 'file': "happy.jpg"},
{'source': "Internet", 'label': "grey", 'file': "old internet color.png"},
{'source': "Asher", 'label': "Asher", 'file': "asher3.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)
The third image would result in lossless data compression. The whole image is one shade, meaning there's no data to lose. This image could be compressed to one pixel and would stay identical. The fourth image would likely result in lossy data compression. There's many colors and shapes to this image, meaning a loss in data would be very noticable.
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-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 // 2, average, average // 2, pixel[3])) # PNG format
else:
image['gray_data'].append((average // 2, average, average // 2))
# 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("--- green image ----")
image_management_add_html_grey(image)
display(HTML(image['html_grey']))
print()
