def make_srcnn_rgb_dataset_based_on_cifar10():
print('making SRCNN-RGB dataset using CIFAR-10...')
(Y_train, _), (Y_test, _) = get_dataset_part(cifar10.load_data(),
train_part=0.2)
from image_handler import get_image, get_image_data, zoom_out_image, zoom_up_image
# X_train
print('making X_train list...')
X_train = []
for item in Y_train:
image_data = item.tolist()
image = get_image(image_data, mode='RGB')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_train.append(zoomed_up_image_data)
# X_test
print('making X_test list...')
X_test = []
for item in Y_test:
image_data = item.tolist()
image = get_image(image_data, mode='RGB')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_test.append(zoomed_up_image_data)
dtype = 'uint8'
dataset = (np.array(X_train, dtype=dtype), np.array(Y_train, dtype=dtype)), \
(np.array(X_test, dtype=dtype), np.array(Y_test, dtype=dtype))
print('saving dataset to srcnn-rgb-cifar10-dataset.npz...')
np.savez('datasets/srcnn-rgb-cifar10-dataset.npz', dataset)
def show_mnist_example(count=1):
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
from image_handler import get_image
for i in range(count):
get_image(X_train[i].tolist())
def create_surface(self):
"""Creates a Surface depicting the Tweet."""
# TODO: Needs refactoring (including the helper methods).
header = []
profile_pic = image_handler.get_image(self.profile_image_url)
size = config2.config['profile_size']
profile_pic = pygame.transform.scale(profile_pic, (size, size))
header.append(profile_pic)
Tweet.font.set_underline(True)
name = Tweet.font.render('@' + self.screen_name, 1, BLACK)
Tweet.font.set_underline(False)
header.append(name)
text = Tweet.create_body(self.lines)
content = []
content.append(make_header(header, WHITE, text.get_rect().width))
content.append(text)
content.extend([image_handler.get_image(img) for img in self.imgs])
popularity_bar = self.create_popularity_surface()
content.append(popularity_bar)
content_surf = make_column(content, WHITE)
content_surf = Tweet.create_bubble(content_surf)#surrounds the content in the bubble
surfs = []
surfs.append(content_surf)
tweet = make_column(surfs, BLACK)
return content_surf
def get_srcnn_rgb_cifar10_20000_dataset_interpolation_metrics():
from image_handler import make_interpolated_image_from, get_images_difference_metrics, get_image
(X_train, Y_train), (X_test,
Y_test) = get_srcnn_rgb_cifar10_20000_dataset()
for image_data in Y_train:
get_image()
def show_dataset_example(dataset, count=1):
(X_train, Y_train), (X_test, Y_test) = dataset
from image_handler import get_image
for i in range(count):
# get_image(X_train[i], mode='RGB').show()
# get_image(Y_train[i], mode='RGB').show()
get_image(X_test[i], mode='RGB').show()
get_image(Y_test[i], mode='RGB').show()
def show_cifar100_example(count=1):
(X_train, y_train), (X_test, y_test) = cifar100.load_data()
# num_train, height, width, depth = X_train.shape
print(X_train.shape)
# print(X_train[0].tolist())
from image_handler import get_image
for i in range(count):
get_image(X_train[i].tolist(), mode='RGB').show()
def make_pasadena_dataset():
print('making PASADENA dataset...')
from image_handler import get_image_data, get_image, zoom_out_image, zoom_up_image
path = 'saved_images/Pasadena Dataset/'
filename_prefix = path + 'dcp_24'
size = 10
result = []
for i in range(size):
filename = filename_prefix + str(12 + i) + '.jpg'
image = Image.open(filename)
print('Image', filename.rpartition('/')[2], 'opened')
image_data = get_image_data(image)
result.append(image_data)
print('making Y_train and Y_test...')
train_size = 7
Y_train, Y_test = result[:train_size], result[train_size:]
# X_train
print('making X_train list...')
X_train = []
for item in Y_train:
image_data = item # .tolist()
image = get_image(image_data, mode='RGB')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_train.append(zoomed_up_image_data)
# X_test
print('making X_test list...')
X_test = []
for item in Y_test:
image_data = item # .tolist()
image = get_image(image_data, mode='RGB')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_test.append(zoomed_up_image_data)
dtype = 'uint8'
dataset = (np.array(X_train, dtype=dtype), np.array(Y_train, dtype=dtype)), \
(np.array(X_test, dtype=dtype), np.array(Y_test, dtype=dtype))
print('saving dataset to pasadena-dataset.npz...')
np.savez_compressed('datasets/pasadena-dataset.npz', dataset)
print('pasadena-dataset.npz saved')
def make_hundred_dataset():
print('making HUNDRED dataset...')
from image_handler import get_image_data, get_image, zoom_out_image, zoom_up_image
path = 'images/Hundred Dataset/images/'
size = 53
result = []
for i in range(size):
filename = path + str(1 + i) + '.png'
image = Image.open(filename)
print('Image', filename.rpartition('/')[2], 'opened')
image_data = get_image_data(image)
result.append(image_data)
print('making Y_train and Y_test...')
train_size = 40
Y_train, Y_test = result[:train_size], result[train_size:]
# X_train
print('making X_train list...')
X_train = []
for item in Y_train:
image_data = item # .tolist()
image = get_image(image_data, mode='RGB')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_train.append(zoomed_up_image_data)
# X_test
print('making X_test list...')
X_test = []
for item in Y_test:
image_data = item # .tolist()
image = get_image(image_data, mode='RGB')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_test.append(zoomed_up_image_data)
dtype = 'uint8'
dataset = (np.array(X_train, dtype=dtype), np.array(Y_train, dtype=dtype)), \
(np.array(X_test, dtype=dtype), np.array(Y_test, dtype=dtype))
print('saving dataset to hundred-dataset.npz...')
np.savez('datasets/hundred-dataset.npz', dataset) # _compressed
print('hundred-dataset.npz saved')
def handle_mnist():
from image_handler import get_image, get_image_data, zoom_out_image
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
dataset_size = 60000
dataset_X, dataset_Y = [], []
for i, X_train_item in enumerate(X_train[:dataset_size]):
image_data = X_train_item.tolist()
image = get_image(image_data, mode='L')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_out_image_data = get_image_data(zoomed_out_image)
dataset_X.append(zoomed_out_image_data)
dataset_Y.append(image_data)
print('image ' + str(i) + ', height : ' + str(len(image_data)),
'width : ' + str(len(image_data[0])),
sep=', ')
print('zoomed_out_image ' + str(i) + ', height : ' +
str(len(zoomed_out_image_data)),
'width : ' + str(len(zoomed_out_image_data[0])),
sep=', ')
# print('image_data :', image_data)
# print('zoomed_out_image_data :', zoomed_out_image_data)
# image.show()
# zoomed_out_image.show()
dataset = (dataset_X, dataset_Y)
import pickle
mnist_dataset_file = open('datasets/mnist-dataset.pkl', 'wb')
pickle.dump(dataset, mnist_dataset_file)
mnist_dataset_file.close()
def create_surface(self):
"""Creates a Surface depicting the Tweet."""
# TODO: Needs refactoring (including the helper methods).
surfs = []
image = image_handler.get_image(self.profile_image_url)
profile_pic = pygame.transform.scale(image, (70, 70))
surfs.append(profile_pic)
name = Tweet.font.render('@' + self.screen_name, 1, BLACK)
surfs.append(name)
text = Tweet.create_body(self.lines)
content = [text]
content.extend([image_handler.get_image(img) for img in self.imgs])
popularity_bar = self.create_popularity_surface()
content.append(popularity_bar)
content_surf = make_column(content, WHITE)
surfs.append(Tweet.create_bubble(content_surf))
tweet = make_column(surfs, TWITTER_BLUE)
return tweet
def show_srcnn_mnist_dataset_example(count=1):
from image_handler import get_image
(X_train, Y_train), (X_test, Y_test) = get_srcnn_mnist_dataset()
for i in range(count):
get_image(X_train[i], mode='L').show()
get_image(Y_train[i], mode='L').show()
for i in range(count):
get_image(X_test[i], mode='L').show()
get_image(Y_test[i], mode='L').show()
def make_srcnn_dataset_based_on_mnist():
print('making SRCNN dataset using MNIST...')
from image_handler import get_image, get_image_data, zoom_out_image, zoom_up_image
(Y_train, _), (Y_test, _) = mnist.load_data() # 60000, 10000
# making X_train list
print('making X_train list...')
X_train = []
for item in Y_train:
image_data = item.tolist()
image = get_image(image_data, mode='L')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_train.append(zoomed_up_image_data)
# making X_test list
print('making X_test list...')
X_test = []
for item in Y_test:
image_data = item.tolist()
image = get_image(image_data, mode='L')
zoomed_out_image = zoom_out_image(image, times=2)
zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
zoomed_up_image_data = get_image_data(zoomed_up_image)
X_test.append(zoomed_up_image_data)
# X_train, X_test = [], []
# for X, Y in [(X_train, Y_train), (X_test, Y_test)]:
# for item in Y:
# image_data = item.tolist()
# image = get_image(image_data, mode='L')
# zoomed_out_image = zoom_out_image(image, times=2)
# zoomed_up_image = zoom_up_image(zoomed_out_image, times=2)
# zoomed_up_image_data = get_image_data(zoomed_up_image)
# X.append(zoomed_up_image_data)
dtype = 'uint8'
dataset = (np.array(X_train, dtype=dtype), np.array(Y_train, dtype=dtype)), \
(np.array(X_test, dtype=dtype), np.array(Y_test, dtype=dtype))
print('saving dataset to srcnn-mnist-dataset.npz...')
np.savez('datasets/srcnn-mnist-dataset.npz', dataset)
def show_srcnn_rgb_cifar10_20000_dataset_example(count=1):
(X_train, Y_train), (X_test,
Y_test) = get_srcnn_rgb_cifar10_20000_dataset()
from image_handler import get_image
for i in range(count):
# get_image(X_train[i], mode='RGB').show()
# get_image(Y_train[i], mode='RGB').show()
# get_image(X_test[i], mode='RGB').show()
get_image(Y_test[i], mode='RGB').show()
get_image(X_train[i],
mode='RGB').save('saved_images/cifar10_20000_X_train[0].png')
get_image(Y_train[i],
mode='RGB').save('saved_images/cifar10_20000_Y_train[0].png')
def run(image_data):
print('neural network running...')
# preparing image_data
test = np.array([image_data])
test = test.astype('float32')
test /= 255 # Normalise data to [0, 1] range
test = test.reshape(1, 3, 32, 32)
# loading model
# path_to_model = 'models/srcnn-cifar10-20000-9_3_1_5-64_32_32-20epochs-he_uniform-custom_relu.h5'
path_to_model = ''
model = keras.models.load_model(path_to_model,
custom_objects={
'psnr_3_channels': psnr_3_channels,
'custom_relu': custom_relu,
}) # , custom_objects={'psnr_L': psnr_L})
print(model.summary())
batch_size = 64
prediction = model.predict(test, batch_size=batch_size, verbose=1)
from keras.backend import clear_session
clear_session()
prediction.resize((1, 32, 32, 3))
prediction = np.rint(prediction * 255).astype('uint8')
print_ndarray_info(
prediction,
verbose=True) # reshape((3, 4)) => a ; resize((2,6)) => on place
prediction_image_data = prediction[0]
print_ndarray_info(prediction_image_data, verbose=True)
from image_handler import get_image
prediction_image = get_image(prediction_image_data, mode='RGB')
# prediction_image.show()
return prediction_image_data
def show_pasadena_dataset_example(count=1):
(X_train, Y_train), (X_test, Y_test) = get_pasadena_dataset()
from image_handler import get_image
for i in range(count):
get_image(X_train[i], mode='RGB').show()
get_image(Y_train[i], mode='RGB').show()
def notifyGameEnd(summoner, gameId, previouslyFailedCount=0):
if gameId is None:
return
gameId = str(gameId) # api v5 fix
url = api_calls.BASE_API_URL_V5 + \
api_calls.MATCH_API_URL.format(matchId=f"NA1_{gameId}")
response = call_api(url)
if response and response.status_code == 200:
json_data = response.json()
participants = json_data["info"]["participants"]
participantID = None
participant = None
totalKills = 0
totalDamage = 0
damageRank = 1
team = None
for p in participants:
if p["summonerId"] == summoner.SummonerDTO["id"]:
summonerId = p["summonerId"]
participantID = p["participantId"]
participant = p
team = p["teamId"]
break
if participantID is None:
logging.info(
f"Could not find {summoner.SummonerDTO['name']} in participant list"
)
# participant now has the summoner stats for the game
# personal stats
champion = data.getChampionName(participant["championId"])
kills = int(participant["kills"])
deaths = int(participant["deaths"])
assists = int(participant["assists"])
damage = int(participant["totalDamageDealtToChampions"])
# cumulative team stats
for p in participants:
if p["teamId"] == team:
totalKills += int(p["kills"])
p_dmg = int(p["totalDamageDealtToChampions"])
totalDamage += p_dmg
if p["participantId"] != participantID and p_dmg > damage:
damageRank += 1
# kp
if totalKills > 0:
kp = str((100 * (kills + assists)) // totalKills)
else:
kp = "0"
# damage share
if totalDamage > 0:
damageShare = str((100 * damage) // totalDamage)
else:
damageShare = 0
damageRank = 5
# Game End Stats Message
win = participant["win"]
result = "won" if win else "lost"
dmg_text = ""
if damageRank == 1:
dmg_text = "highest in the team"
elif damageRank == 5:
dmg_text = "lowest in the team"
else:
pos = "2nd" if damageRank == 2 else "3rd" if damageRank == 3 else "4th" if damageRank == 4 else "NaN"
dmg_text = pos + " highest in the team"
msg = "GAME END: {} {} a game as {}. He went {}/{}/{} with a kp of {}% and dealt {:,} damage ({}% damage share, {})"
msg = msg.format(summoner.getName(), result, champion, kills, deaths,
assists, kp, damage, damageShare, dmg_text)
# Rank Change
if summoner.CurrentRank != None:
promos = None
url = api_calls.BASE_API_URL + \
api_calls.LEAGUE_API_URL.format(
encryptedSummonerId=summoner.SummonerDTO["id"])
response = call_api(url)
if response and response.status_code == 200:
leagueEntrySet = response.json()
rank = {}
for entry in leagueEntrySet:
if (entry["queueType"] != consts.RANKED_SOLO_QUEUE_TYPE):
continue
rank["tier"] = entry["tier"]
rank["division"] = entry["rank"]
rank["lp"] = entry["leaguePoints"]
if "miniSeries" in entry:
promos = entry["miniSeries"]
break
if rank["tier"] == summoner.CurrentRank["tier"] and rank[
"division"] == summoner.CurrentRank["division"]:
prevLp = int(summoner.CurrentRank["lp"])
newLp = int(rank["lp"])
lpDiff = 0
if win:
lpDiff = newLp - prevLp
else:
lpDiff = prevLp - newLp
result = "gained" if win else "lost"
if promos is None or lpDiff != 0:
msg += "\n" + summoner.SummonerDTO[
"name"] + " " + result + " " + str(
lpDiff
) + "lp for this game. He is currently " + str(
rank["tier"]) + " " + str(
rank["division"]) + " " + str(
rank["lp"]) + "lp."
if promos is not None:
wins = str(promos["wins"])
losses = str(promos["losses"])
nextRankIndex = consts.TIERS.index(
summoner.CurrentRank["tier"]) + 1
nextRank = consts.TIERS[nextRankIndex]
msg += "\n" + summoner.SummonerDTO["name"] + " is currently " + \
wins + "-" + losses + " in promos to " + nextRank + "."
# Match History Uri:
postMsg = None
matchUrls = [
("Mobalytics",
api_calls.MOBALYTICS_MATCH_URL.format(
summonerName=urllib.parse.quote(summoner.SummonerDTO["name"]),
gameId=gameId)),
("LeagueOfGraphs", api_calls.LOG_MATCH_URL.format(gameId=gameId))
]
urlListText = " | ".join([
"[{text}](<{url}>)".format(text=item[0], url=item[1])
for item in matchUrls
])
postMsg = "View Game Details Here: " + urlListText
color = utils.ColorCodes.GREEN if win else utils.ColorCodes.RED
image_handler.get_image(gameId)
image_handler.crop_image_post_game(gameId)
discord_bot.SendMessage(msg,
color,
postMsg,
file=discord.File(f"cropped_{gameId}.png"))
image_handler.remove_images(gameId)
# Special Naeem section xD
if summoner.getName(
) == "NaeemP" and not win and champion != "Nocturne":
discord_bot.SendMessage("SADGE: No Nocturne no LP. ",
file=discord.File("naeem_lose.jpg"))
else:
msg = "Try #" + str(previouslyFailedCount + 1) + ": Error Obtaining Game Info for match# " + \
str(gameId) + " (Game by " + summoner.SummonerDTO["name"] + ")"
if previouslyFailedCount > 3:
msg += " Stopping trying, match query failed. Look into it."
else:
failed_game_end_ids.add(
(gameId, summoner, previouslyFailedCount + 1))
logging.info(response) # print response to see what is going on
logging.info(msg)
discord_bot.SendMessage(msg)
