One of the biggest challenges for this project is how to gather all of the team logos since it isn't just a matter of downloading them but also formatting them so that they will work with and look nice on an RGB LED matrix. To handle all of this you can run the get_team_logos.py Python script on your desktop or laptop computer.
# SPDX-FileCopyrightText: 2023 Liz Clark for Adafruit Industries
#
# SPDX-License-Identifier: MIT
# Written by Liz Clark (Adafruit Industries) with OpenAI ChatGPT v4 Aug 3rd, 2023 build
# https://help.openai.com/en/articles/6825453-chatgpt-release-notes
# https://chat.openai.com/share/2fabba2b-3f17-4ab6-a4d9-58206a3b9916
# process() function originally written by Phil B. for Adafruit Industries
# https://raw.githubusercontent.com/adafruit/Adafruit_Media_Converters/master/protomatter_dither.py
import os
import math
import requests
from PIL import Image
# the name of the sports you want to follow
sport_names = ["football", "baseball", "soccer", "hockey", "basketball"]
# the name of the corresponding leages you want to follow
sport_leagues = ["nfl", "mlb", "usa.1", "nhl", "nba"]
# directory to match CircuitPython code folder names
bitmap_directories = ["team0_logos", "team1_logos", "team2_logos", "team3_logos", "team4_logos"]
# Constants and function for image processing
GAMMA = 2.6
PASSTHROUGH = ((0, 0, 0),
(255, 0, 0),
(255, 255, 0),
(0, 255, 0),
(0, 255, 255),
(0, 0, 255),
(255, 0, 255),
(255, 255, 255))
def process(filename, output_8_bit=True, passthrough=PASSTHROUGH):
"""Given a color image filename, load image and apply gamma correction
and error-diffusion dithering while quantizing to 565 color
resolution. If output_8_bit is True, image is reduced to 8-bit
paletted mode after quantization/dithering. If passthrough (a list
of 3-tuple RGB values) is provided, dithering won't be applied to
colors in the provided list, they'll be quantized only (allows areas
of the image to remain clean and dither-free).
"""
img = Image.open(filename).convert('RGB')
err_next_pixel = (0, 0, 0)
err_next_row = [(0, 0, 0) for _ in range(img.size[0])]
for row in range(img.size[1]):
for column in range(img.size[0]):
pixel = img.getpixel((column, row))
want = (math.pow(pixel[0] / 255.0, GAMMA) * 31.0,
math.pow(pixel[1] / 255.0, GAMMA) * 63.0,
math.pow(pixel[2] / 255.0, GAMMA) * 31.0)
if pixel in passthrough:
got = (pixel[0] >> 3,
pixel[1] >> 2,
pixel[2] >> 3)
else:
got = (min(max(int(err_next_pixel[0] * 0.5 +
err_next_row[column][0] * 0.25 +
want[0] + 0.5), 0), 31),
min(max(int(err_next_pixel[1] * 0.5 +
err_next_row[column][1] * 0.25 +
want[1] + 0.5), 0), 63),
min(max(int(err_next_pixel[2] * 0.5 +
err_next_row[column][2] * 0.25 +
want[2] + 0.5), 0), 31))
err_next_pixel = (want[0] - got[0],
want[1] - got[1],
want[2] - got[2])
err_next_row[column] = err_next_pixel
rgb565 = ((got[0] << 3) | (got[0] >> 2),
(got[1] << 2) | (got[1] >> 4),
(got[2] << 3) | (got[2] >> 2))
img.putpixel((column, row), rgb565)
if output_8_bit:
img = img.convert('P', palette=Image.ADAPTIVE)
img.save(filename.split('.')[0] + '.bmp')
# Create a base directory to store the logos if it doesn't exist
base_dir = 'sport_logos'
if not os.path.exists(base_dir):
os.makedirs(base_dir)
# Loop through each league to get the teams
for i in range(len(sport_leagues)):
sport = sport_names[i]
league = sport_leagues[i]
# Create a directory for the current sport if it doesn't exist
sport_dir = os.path.join(base_dir, bitmap_directories[i])
if not os.path.exists(sport_dir):
os.makedirs(sport_dir)
# Set the URL for the JSON file for the current league
url = f"https://site.api.espn.com/apis/site/v2/sports/{sport}/{league}/teams"
# Fetch the JSON data
response = requests.get(url)
data = response.json()
# Extract team data
teams = data.get('sports', [{}])[0].get('leagues', [{}])[0].get('teams', [])
# Download, process, resize, and save each logo
for team in teams:
abbreviation = team['team']['abbreviation']
logo_url = team['team']['logos'][0]['href']
print(f"Downloading logo for {abbreviation} from {league}...")
img_path_png = os.path.join(sport_dir, f"{abbreviation}.png")
response = requests.get(logo_url, stream=True)
with open(img_path_png, 'wb') as file:
for chunk in response.iter_content(chunk_size=1024):
file.write(chunk)
# Open, resize, and save the image with PIL
with Image.open(img_path_png) as the_img:
img_resized = the_img.resize((32, 32))
img_resized.save(img_path_png)
process(img_path_png)
# Delete the original .png file
os.remove(img_path_png)
print("All logos have been downloaded, processed, and resized!")
To run the script you will need a desktop or laptop computer with Python 3 installed. A Raspberry Pi running Raspberry Pi OS would also be a great option.
Python Dependencies
First, you'll use pip to install the Python libraries required to run the script:
pip install pillow pip install requests
Customize the Script
Open the get_team_logos.py script in your preferred text editor or IDE. At the top of the code, you can customize which sports and corresponding leagues you want to gather the logos for. Edit the sport_names array with the name of the sport (baseball, basketball, etc) and edit the sport_leagues array with the corresponding name of the league (MLB, NBA, etc).
import os import math import requests from PIL import Image # the name of the sports you want to follow sport_names = ["football", "baseball", "soccer", "hockey", "basketball"] # the name of the corresponding leages you want to follow sport_leagues = ["nfl", "mlb", "usa.1", "nhl", "nba"] # directory to match CircuitPython code folder names bitmap_directories = ["team0_logos", "team1_logos", "team2_logos", "team3_logos", "team4_logos"]
Each set of team logos will be saved in the corresponding folders named in the bitmap_directories array. These folder names are utilized in the CircuitPython code that will run on your Matrix Portal S3.
Run the Script
After customizing the script, you can run the get_team_logos.py script on your computer with:
python get_team_logos.py
When the script finishes, you'll see a folder called /sport_logos in the same directory where you have the get_team_logos.py script saved. If you go into the /sport_logos folder, you'll see the folders containing the team logos. You'll want to drag and drop these five folders onto your Matrix Portal S3 CIRCUITPY drive.
How the Script Works
The script creates a folder called /sport_logos in the same directory where you have the script saved. Then it fetches the ESPN API Teams JSON feed associated with each league that you defined at the top of the script. There is a URL for each team logo in that feed. The logo .PNG image file is downloaded to your computer. Then it is resized to be 32x32 pixels and run thru the process() function to add gamma correction and save the image as a bitmap. The script finishes by deleting the previously downloaded .PNG image since you don't need it anymore.
This script utilizes the process() function from the protomatter_dither.py script that is used in the Image Correction for RGB LED Matrices guide. That script provides gamma correction for the images so that they look beautiful on RGB LED matrices. By including the function in the get_team_logos.py, all of the images are automatically converted without having to run another script.
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