Super League stats & predictions

The Impact of Fan Engagement

Fan engagement is vital for the growth and success of any sports league. The Football Super League Brunei has embraced various strategies to enhance fan interaction and loyalty.

• Social Media Campaigns: Engaging content on social media platforms helps build a strong community around the league.
• Fan Zones at Stadia: Interactive fan zones offer entertainment options before and after matches, enhancing the overall experience.
• E-Sports Integration: Incorporating e-sports elements allows fans to engage with football in new and innovative ways.

Economic Impact of the League

The Football Super League Brunei contributes significantly to the local economy. It creates jobs, boosts tourism, and stimulates business growth in related sectors such as hospitality and retail.

• Tourism Boost: International matches attract visitors from around the world, benefiting local hotels, restaurants, and attractions.
• Sponsorship Deals: Successful sponsorship agreements provide financial support to clubs and promote local businesses.
• Youth Development Programs: Investment in youth academies helps nurture young talent, ensuring a bright future for Bruneian football.

Sustainability Initiatives

Sustainability is becoming increasingly important in sports. The Football Super League Brunei has implemented several initiatives to promote environmental responsibility.

• Eco-Friendly Stadia: Modern stadiums are designed with sustainability in mind, featuring energy-efficient systems and waste reduction measures.
• Clean Energy Use: Efforts are underway to transition stadia to clean energy sources like solar power.
• Eco-Conscious Merchandise: Clubs offer eco-friendly merchandise options made from sustainable materials.

Innovative Broadcasting Techniques

Broadcasting techniques have evolved significantly, providing fans with immersive viewing experiences. The Football Super League Brunei utilizes cutting-edge technology to enhance broadcasts.

• HDR Broadcasting: High Dynamic Range (HDR) technology delivers stunning visuals with enhanced color accuracy and contrast.
• Voice-Over Commentary: Expert commentators provide insightful analysis, adding depth to the viewing experience.
• Multicamera Angles:Uptick/first-order-motion-model<|file_sep|>/utils.py from __future__ import print_function import os import numpy as np import torch import matplotlib.pyplot as plt from torch.autograd import Variable def make_gif(images_path_list, gif_path, duration=0.1, loop=0, fps=20): # os.system('convert -delay {duration} -loop {loop} {images_path_list} {gif_path}'.format( # duration=int(duration*100), # loop=loop, # images_path_list=' '.join(images_path_list), # gif_path=gif_path)) # os.system('ffmpeg -f image2 -i {images_path_list} -y -vcodec libx264 -pix_fmt yuv420p {gif_path}'.format( # images_path_list=images_path_list, # gif_path=gif_path)) def save_image(image_tensor, file_name, nrow=8, padding=2): image_tensor = image_tensor.detach().cpu() if len(image_tensor.shape) ==4: image_tensor = torchvision.utils.make_grid(image_tensor, nrow=nrow, padding=padding) # image_numpy = image_tensor.numpy().transpose((1,2,0)) # image_numpy = (np.clip(image_numpy[...,:3],0.,1.) *255.).astype(np.uint8) # im = Image.fromarray(image_numpy) # im.save(file_name) def show_image(image_tensor): image_numpy = image_tensor.detach().cpu().numpy().transpose((1,2,0)) # plt.figure(figsize=(15.,15)) # plt.imshow(np.clip(image_numpy[...,:3],0.,1.),interpolation='nearest') def show_image_grid(image_tensor,nrow=8): # print('image shape: {}'.format(image_tensor.shape)) # plt.figure(figsize=(15.,15)) # show_image(torchvision.utils.make_grid(image_tensor,nrow=nrow)) def show_image_pair(im1, im2): def load_video(video_filename): cap = cv2.VideoCapture(video_filename) if not cap.isOpened(): raise IOError("Couldn't open {}".format(video_filename)) frames = [] while True: ret_val, img = cap.read() if not ret_val: break frames.append(img) cap.release() return frames def save_video(frames,output_filename,fps): out = cv2.VideoWriter(output_filename,cv2.VideoWriter_fourcc(*'XVID'), fps,(frames[0].shape[1],frames[0].shape[0])) for frame in frames: out.write(frame) out.release() def save_video_with_ssim(frames,output_filename,fps): out = cv2.VideoWriter(output_filename,cv2.VideoWriter_fourcc(*'XVID'), fps,(frames[0].shape[1],frames[0].shape[0])) ssim_video_frames = [] prev_frame = None for frame in frames: if prev_frame is not None: ssim_frame = compare_ssim(prev_frame.astype(np.float32),frame.astype(np.float32),multichannel=True) ssim_video_frames.append(ssim_frame) #ssim_frame_reshaped = np.reshape(ssim_frame,(ssim_frame.shape[0],ssim_frame.shape[1],1)) #ssim_frame_reshaped = np.repeat(ssim_frame_reshaped,(ssim_frame.shape[2],),axis=2) #ssim_frame_reshaped *=255. #cv2.imshow('SSIM',ssim_frame_reshaped.astype(np.uint8)) #cv2.waitKey(100) prev_frame = frame out.write(frame) out.release() return ssim_video_frames def tensor_to_array(tensor): return tensor.detach().cpu().numpy().transpose((1,2,0)) def array_to_tensor(array): return torch.FloatTensor(array.transpose((2,0,1))) def tensor_to_var(tensor): if tensor.is_cuda: var = Variable(tensor.cuda()) else: var = Variable(tensor) return var def var_to_tensor(var): if var.is_cuda: tensor = var.data.cuda() else: tensor = var.data return tensor def crop_border(im,crop_size): height,width,_ = im.shape crop_height_left,crop_height_right,crop_width_left,crop_width_right = crop_size cropped_im = im[crop_height_left:height-crop_height_right,crop_width_left:width-crop_width_right,:] return cropped_im def crop_center(im,crop_size): height,width,_ = im.shape crop_height,crop_width = crop_size crop_height_left,crop_height_right,crop_width_left,crop_width_right = (crop_height-width)/2,(crop_height-height)/2,(crop_width-width)/2,(crop_width-height)/2 cropped_im = im[crop_height_left:height-crop_height_right,crop_width_left:width-crop_width_right,:] return cropped_im def sample_gaussian_2d(mu_x,mu_y,sigma_x,sigma_y,size): sample_x,sample_y = np.random.normal(mu_x,sigma_x,size),np.random.normal(mu_y,sigma_y,size) sample_x,sample_y=np.clip(sample_x,-1.,1.),np.clip(sample_y,-1.,1.) sample=torch.from_numpy(np.concatenate([sample_x.reshape(-1,1),sample_y.reshape(-1,1)],axis=1).astype(np.float32)) # print(sample.size()) # print(sample) # exit() <|repo_name|>Uptick/first-order-motion-model<|file_sep|>/train.py from __future__ import print_function import os import time import argparse import random import torch.nn as nn import torch.optim as optim from model import FirstOrderMotionModel from utils import * parser = argparse.ArgumentParser(description='First Order Motion Model') parser.add_argument('--data_dir',default='datasets/',help='path to dataset directory') parser.add_argument('--model_dir',default='models/',help='path where model checkpoints will be saved') parser.add_argument('--batch_size',default=16,type=int) parser.add_argument('--num_workers',default=4,type=int) parser.add_argument('--num_epochs',default=50,type=int) parser.add_argument('--learning_rate',default=5e-4,type=float) parser.add_argument('--beta_1',default=0.9,type=float) parser.add_argument('--beta_2',default=0.999,type=float) args = parser.parse_args() device=torch.device("cuda" if torch.cuda.is_available() else "cpu") model_dir=args.model_dir if not os.path.exists(model_dir): os.mkdir(model_dir) model_file_name=os.path.join(model_dir,'model.pt') if os.path.exists(model_file_name): print('Loading existing model from {}'.format(model_file_name)) model_checkpoint=torch.load(model_file_name,map_location=device) model=model_checkpoint['model'] optimizer=model_checkpoint['optimizer'] start_epoch=model_checkpoint['epoch']+1 else: print('No existing model found.') model=FirstOrderMotionModel().to(device) optimizer=torch.optim.Adam(model.parameters(),lr=args.learning_rate,betas=(args.beta_1,args.beta_2)) criterion=nn.MSELoss() dataset=[os.path.join(args.data_dir,f) for f in os.listdir(args.data_dir) if f.endswith('.mp4')] for epoch_num in range(start_epoch,args.num_epochs+1): print('Epoch {}'.format(epoch_num)) start_time=time.time() for i_batch,batch_data_files in enumerate(batch(dataset,batch_size=args.batch_size)): batch_data=[] for batch_data_file in batch_data_files: batch_data.append(load_video(batch_data_file)) batch_data=torch.stack([torch.FloatTensor(torch.from_numpy(array_to_tensor(frame)).unsqueeze(0)) for frame in batch_data]).to(device) batch_target=[] for i in range(batch_data.size(0)): batch_target.append(batch_data[i][:-1]) batch_target=torch.cat(batch_target,dim=0).to(device) batch_input=batch_data[:-1].to(device) model.train() prediction=model(batch_input) prediction=prediction.to(device) loss=criterion(prediction,batch_target) print(loss.item()) optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(),max_norm=10.) optimizer.step() end_time=time.time() print('Epoch {} complete! Time elapsed: {}'.format(epoch_num,end_time-start_time)) torch.save({'epoch':epoch_num-1,'model':model,'optimizer':optimizer},model_file_name)<|file_sep|># First Order Motion Model This repository contains code for [First Order Motion Model](https://arxiv.org/abs/1805.08318). ## Requirements The code has been tested on Python version `python==3.6`, PyTorch version `pytorch==1.0` but it should also work on older versions. ## Setup git clone https://github.com/AshwinDeora/first-order-motion-model.git cd first-order-motion-model ## Download dataset Download `datasets.zip` from [here](https://drive.google.com/file/d/16o7W7CZiHgYqVbSJeO9FgRkXnFF5c8_E/view?usp=sharing) (or run `python download_dataset.py`). Unzip it inside `first-order-motion-model` directory. unzip datasets.zip -d datasets/ ## Training To train: python train.py --data_dir datasets --batch_size N --num_epochs N --learning_rate N --beta_1 N --beta_2 N Default parameters are: --data_dir datasets/ --batch_size 16 --num_epochs 50 --learning_rate .0005 --beta_1 .9 --beta_2 .999 ## Inference