Third NL Center stats & predictions

Overview of Tomorrow's Football Matches in the Third NL Center, Croatia

Football enthusiasts are eagerly anticipating the upcoming matches in the Third NL Center of Croatia scheduled for tomorrow. This highly anticipated event promises thrilling encounters and expert betting predictions that will captivate fans and bettors alike. With several key matches on the agenda, let's delve into the details and explore what to expect from this exciting day of football.

Match Highlights

• Team A vs. Team B: This match is expected to be a closely contested battle, with both teams having strong form recently. Team A, known for their solid defense, will face off against Team B's dynamic attacking lineup.
• Team C vs. Team D: A classic rivalry match that never fails to deliver excitement. Both teams have been in excellent form, making this an unpredictable and thrilling encounter.
• Team E vs. Team F: With Team E looking to bounce back from their last defeat, they will be eager to secure a victory against Team F, who are currently leading the table.

Expert Betting Predictions

Betting enthusiasts will find plenty of opportunities to place strategic bets on tomorrow's matches. Here are some expert predictions:

Team A vs. Team B

• Prediction: Draw - Both teams have shown resilience and tactical prowess, making a draw a likely outcome.
• Betting Tip: Over 2.5 Goals - Given the attacking capabilities of both teams, expect a match with multiple goals.

Team C vs. Team D

• Prediction: Team C Victory - Despite being underdogs, Team C has been performing exceptionally well at home.
• Betting Tip: Both Teams to Score - With both teams known for their offensive play, it's likely that both will find the back of the net.

Team E vs. Team F

• Prediction: Team F Victory - As league leaders, Team F is expected to maintain their winning streak.
• Betting Tip: Under 2.5 Goals - Given Team E's defensive struggles, this match might see fewer goals than anticipated.

Detailed Analysis of Key Matches

Team A vs. Team B

This match features two of the most defensively robust teams in the league. Team A's recent performances have been characterized by their ability to withstand pressure and capitalize on counter-attacks. On the other hand, Team B has been prolific in front of goal, thanks to their creative midfielders and speedy forwards.

• Key Players to Watch:
• Player X (Team A) - Known for his defensive leadership and ability to intercept passes.
• Player Y (Team B) - A prolific goal scorer who has been in excellent form this season.
• Tactical Insights:
• Team A is likely to employ a deep-lying defensive strategy, focusing on maintaining a solid backline while looking for opportunities to counter-attack.
• Team B will aim to dominate possession and create scoring opportunities through quick transitions and wide play.

Team C vs. Team D

This rivalry match is set to be one of the highlights of the day. Both teams have a rich history of intense encounters, often resulting in high-scoring games. The atmosphere at the stadium is expected to be electric, with fans from both sides adding to the fervor.

• Key Players to Watch:
• Player Z (Team C) - A versatile midfielder known for his ability to control the tempo of the game.
• Player W (Team D) - A forward with exceptional pace and dribbling skills, capable of breaking down defenses.
• Tactical Insights:
• Team C will look to exploit their home advantage by pressing high up the pitch and disrupting Team D's build-up play.
• Team D will focus on maintaining composure and exploiting spaces left by Team C's aggressive pressing.

Team E vs. Team F

This match features a clash between a team seeking redemption and a team aiming to consolidate their position at the top of the table. Team E's recent defeat has left them hungry for a win, while Team F will be determined to continue their winning streak.

• Key Players to Watch:
• Player V (Team E) - An experienced defender who can organize the backline effectively.
• Player U (Team F) - The playmaker who orchestrates most of Team F's attacking moves.
• Tactical Insights:
• Team E will need to tighten their defense and rely on quick counter-attacks to unsettle Team F.
• Team F will aim to control possession and patiently break down Team E's defense through sustained pressure.

Betting Strategies for Tomorrow's Matches

Betting on football can be both exciting and rewarding if approached strategically. Here are some tips for placing bets on tomorrow's matches:

• Analyze Recent Form: Consider how each team has performed in their recent matches. Look for patterns such as consistent victories or frequent draws that might influence the outcome.
• Evaluate Head-to-Head Records: Historical data can provide valuable insights into how teams perform against each other. Teams with strong head-to-head records may have an edge in upcoming encounters.
• Carefully Read Pre-Match Reports: Pre-match reports from reliable sources can offer detailed analysis and expert opinions that might not be immediately apparent from statistics alone.
• Diversify Your Bets: Avoid placing all your bets on a single outcome. Consider spreading your bets across different markets such as total goals, first-half results, or player performances to increase your chances of success.

Injury Updates and Squad News

Injuries and squad changes can significantly impact team performance. Here are some updates on key players for tomorrow's matches:

Injury Concerns for Team A vs. Team B

• Suspensions:
• No suspensions reported for either team.
<|repo_name|>jungkim/SEEDS<|file_sep|>/src/seed/fusion/visualizer.py # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import numpy as np import os from PIL import Image from seed.fusion.common import util class Visualizer: def __init__(self): self._output_dir = None def set_output_dir(self, output_dir): self._output_dir = output_dir def get_output_dir(self): return self._output_dir def visualize_lidar_point_cloud(self, lidar_points, lidar_color, view_points=None, output_path=None, view_num=5, view_range=[-20., -20., -20., 20., 20., 20.]): if view_points is None: view_points = self.get_default_view_points(view_num) if output_path is None: if self._output_dir is None: raise ValueError("Please specify an output directory") output_path = os.path.join(self._output_dir, 'lidar_point_cloud.png') util.visualize_lidar_point_cloud(lidar_points, lidar_color, view_points, output_path=output_path, view_range=view_range) def visualize_image(self, image_array, boxes=None, segms=None, keypoints=None, save_path=None): """ image_array: [H x W x C], RGB channel order boxes: [N x (y1, x1, y2, x2)] segms: [N x M x M] or [N x M x M x C] keypoints: [N x K x (x,y,score)] """ if save_path is None: if self._output_dir is None: raise ValueError("Please specify an output directory") save_path = os.path.join(self._output_dir, 'image.png') # import pycocotools.mask as mask_util # image_array = Image.fromarray(image_array) # import matplotlib.pyplot as plt # fig = plt.figure(figsize=(15,10)) # ax = fig.add_subplot(111) # ax.imshow(image_array) # if boxes is not None: # print(boxes.shape) # print(segms.shape) # print(keypoints.shape) # colors = util.random_colors(len(boxes)) # for i in range(len(boxes)): # box = boxes[i] # color = colors[i] # mask = segms[i] # # if mask.ndim == 2: # mask = mask_util.decode(mask) # # image_array_with_mask_and_bbox_and_keypoints(image_array.copy(), # box.astype(np.int32), # color=color, # label=label[i], # mask=mask, # keypoints=keypoints[i]) # # def image_array_with_mask_and_bbox_and_keypoints(image_array, box=None, color=None, label=None, mask=None, keypoints=None): """Apply bbox mask and bbox annotation onto image. Args: image_array: numpy array with shape [H,W,C]. box: [y1,x1,y2,x2]. color: RGB tuple. label: string. mask: numpy array with shape [H,W] or [H,W,C]. keypoints: numpy array with shape [K,(x,y,score)]. Returns: image_array_with_mask_and_bbox_and_keypoints: numpy array with shape [H,W,C]. Raises: ValueError: On incorrect data format. """ if __name__ == "__main__": visualizer = Visualizer() <|file_sep|># Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import numpy as np from seed.utils import common_utils class LIDARInstance3DBoxes(object): """Class for LiDAR-based instance boxes. Args: box_dim (int): dimension of each box. box_mode (str): coordinate mode. origin (list): origin point coordinates. dtype (type): data type. box_margin (float): margin ratio used when expanding boxes. **kwargs: Attributes: tensor (numpy.ndarray): instance boxes coordinates. num_boxes (int): number of instance boxes. dtype (type): data type. """ def __init__(self, box_dim=9, box_mode='lidar', origin=(0., 0., 0.,), dtype=np.float32, box_margin=0., **kwargs): self.tensor = kwargs.get('tensor', None) <|file_sep|># Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import os import pickle import numpy as np from collections import OrderedDict from seed.core.box_np_ops import box_np_ops class ObjectManager(object): """Class for managing objects in simulation environment. Args: env_obs_dict_template(dict): template dict used in observation. root_path(str): path where we store datasets. dataset_cfg(DatasetConfig): config object used in Dataset class. Attributes: env_obs_dict_template(dict): template dict used in observation. root_path(str): path where we store datasets. dataset_cfg(DatasetConfig): config object used in Dataset class. """ <|repo_name|>jungkim/SEEDS<|file_sep|>/src/seed/evaluator/object_evaluator.py # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import copy import logging import os import pickle from collections import OrderedDict from seed.core.box_np_ops import box_np_ops from seed.core.box_torch_ops import box_torch_ops from seed.core.class_names import CLASS_NAMES from seed.core.common import BoxMode from seed.core.evaluation.bbox_overlaps import bbox_overlaps_batch from seed.core.evaluation.mean_ap import mean_ap from seed.utils import common_utils class ObjectEvaluator(object): <|repo_name|>jungkim/SEEDS<|file_sep|>/src/seed/dataset/dataset_config.py # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import copy import logging from easydict import EasyDict logger = logging.getLogger(__name__) class DatasetConfig(object): <|file_sep|># Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import torch def collate_fn(batch): <|file_sep|># SEEDS [](https://travis-ci.com/Microsoft/SEEDS) SEEDS (**S**ensor **E**nvironment **E**mulation **D**ata **S**imulator) is an open-source platform designed primarily for autonomous driving research using simulated environments provided by CARLA simulator [[1](https://carla.readthedocs.io/en/latest/)]. ## Introduction The main features include: * Autonomous driving simulation environment provided by CARLA simulator [[1](https://carla.readthedocs.io/en/latest/)]. * Data generation pipeline that enables users collect large-scale annotated data using simulated environments provided by CARLA simulator [[1](https://carla.readthedocs.io/en/latest/)]. * Modular neural network architecture based on PyTorch [[2](https://pytorch.org/docs/stable/index.html)] that allows users flexibly combine different neural network models such as perception model [[3](https://arxiv.org/pdf/1906.04009.pdf)], prediction model [[4](https://arxiv.org/pdf/1809.10647.pdf)], planning model [[5](https://arxiv.org/pdf/1808.05417.pdf)], etc., into one integrated autonomous driving model. ## Getting Started ### Installation #### Install Python dependencies * Clone this repo: bash git clone https://github.com/Microsoft/SEEDS.git cd SEEDS * Install Python dependencies: bash pip install -r requirements.txt #### Install CARLA simulator [[1](https://carla.readthedocs.io/en/latest/)] * Download CARLA simulator v0.9.x release from [releases page](https://github.com/carla-simulator/carla/releases). Note that version v0.9.x supports Python API which allows us interact with CARLA simulator via Python scripts. * Extract downloaded CARLA release: bash tar xvzf carla_0_9_x_x86_64.tar.gz * Add CARLA binaries path into PATH environment variable: bash export PATH=$PATH:/path/to/carla_0_9_x/LinuxNoEditor/bin ### Quick Start To run SEEDS using CARLA simulator [[1](https://carla.readthedocs.io/en/latest/)], you need download CARLA simulator v0.9.x release from [releases page](https://github.com/carla-simulator/carla/releases). #### Train autonomous driving model To train autonomous driving model using SEEDS framework with simulated data generated from CARLA simulator [[1](https://carla.readthedocs.io/en/latest/)], you can follow below steps: 1) Create new project folder inside `projects` folder: bash mkdir projects/my_project/ 2) Create new configuration file `config.yaml` inside `projects/my_project` folder: yaml dataset_cfg: root_path: "/path/to/dataset" version: "v1" pipeline_file_train: "pipeline_train.yaml" pipeline_file_val: "pipeline_val.yaml" workers_per_gpu: "auto" num_workers: "auto" local_ranking_offset: "auto" model_cfg: model_type: "example_model" model_params: param1 : value1 param2 : value2 train_cfg: gpus : "0" batch_size_per_gpu : "8" num_epochs : "100" resume_from : "" Note that you need change `root_path` parameter into path where you want store dataset generated using SEEDS framework. Note also that you need create `pipeline_train.yaml` & `pipeline_val.yaml` files inside `projects/my_project` folder which specifies training & validation pipelines respectively. You can find example training & validation pipelines at `configs/pipelines/example_pipeline_train.yaml` & `configs/pipelines/example_pipeline_val.yaml` respectively. For more information about creating training & validation pipelines please refer our [tutorial notebook](./tutorials/tutorial_create_pipeline.ipynb). Note also that you need specify model configuration parameters at `model_cfg.model_params`. You can find example model configuration parameters at `configs/models/example_model_config.yaml`. For more information about creating models please refer our [tutorial notebook](./tutorials/tutorial_create_model.ipynb). 3) Train autonomous driving model using SEEDS framework: bash python tools/train.py --config_file projects/my_project/config.yaml --launcher pytorch --log_level INFO --seed=12345 #### Evaluate autonomous driving model To evaluate autonomous driving model trained using SEEDS