nerdery
I had an idea last month for a site based around identifying video game streamers while they are in game. If I can identify them in real time then I could have real time fantasy league style betting based around the stats... it was a fun experiment but my techniques ended up not being very practical.
In the game League of Legends I could try to find the "yellow health bar" that only your character has. Once I have the coordinates for the yellow health bar I can look above it to see the character name! Boom, with the character name I can look up stats and track all of their games automatically.
For example, watching Krepo's stream here's how I'd go about getting his character name.
from livestreamer import Livestreamer
while True:
session = Livestreamer()
streams = session.streams('http://www.twitch.tv/%s' % streamer)
if streams:
stream = streams['source']
container = av.open(stream.url)
video_stream = next(s for s in container.streams if s.type == b'video')
image = None
for packet in container.demux(video_stream):
for frame in packet.decode():
image = frame.to_image()
features = process_image(image)
def _find_our_champion(image, search_step=2, draw_on_image=False):
# should find the yellow bar underneath our hero and then
# we can use the top left of that to center our search area
data = np.asarray(image)
# remove any alpha channel in the data so we just have (r, g, b)
data = data[:, :, :3]
width, height = data.shape[1], data.shape[0]
character_name_coords = None
# Cut off the bottom few hundred pixels, not needed
height -= 200
for y in xrange(0, height):
hits_this_row = 0
for x in xrange(0, width, search_step):
r, g, b = data[y][x]
if r > 200 and 160 < g < 230 and 30 < b < 70:
# really yellow
hits_this_row += 1
if hits_this_row > 20:
# Top left and bottom right
character_name_coords = (x - 120, y - 35, x + 100, y - 8)
if draw_on_image:
red = (255, 0, 0)
draw = ImageDraw.Draw(image)
draw.rectangle(character_name_coords, fill=red)
break
if character_name_coords:
break
return character_name_coords
def _ocr_name_box(name_box_image):
# 0 means load in grayscale
try:
gray = cv2.cvtColor(np.array(name_box_image), 0)
except TypeError:
gray = cv2.cvtColor(name_box_image, 0)
ret, gray = cv2.threshold(gray, 160, 255, cv2.THRESH_BINARY)
# ocr code below
def _ocr_name_box(name_box_image):
# processing code above
gray_image = Image.fromarray(gray)
return pytesseract.image_to_string(gray_image)
OCR results: Krepo
So, it worked in this particular case... this was one of the few that did work.
NOT GOOD!
The idea was super fun to pursue but it doesn't perform very well for a few reasons:
So... it was a fun experiment but not very practical!
You can find all the source here.