Upcoming Ice-Hockey 1. Liga Czech Republic Matches: Expert Betting Predictions
The excitement is palpable as we gear up for another thrilling day in the Ice-Hockey 1. Liga Czech Republic. Tomorrow's matches promise to deliver high-octane action, featuring top-tier teams battling it out on the ice. In this comprehensive guide, we delve into the details of each match, providing expert betting predictions to help you make informed decisions. Whether you're a seasoned bettor or new to the world of sports betting, our insights will enhance your viewing experience and potentially boost your betting success.
Match 1: Sparta Praha vs. HC Kometa Brno
The clash between Sparta Praha and HC Kometa Brno is one of the most anticipated matchups of the day. Sparta Praha, known for their robust defense and strategic play, will be looking to extend their winning streak. On the other hand, HC Kometa Brno, with their dynamic offense, aims to disrupt Sparta's momentum.
- Sparta Praha:
- Recent Form: Sparta Praha has been in excellent form, winning four of their last five matches.
- Key Players: Watch out for Jan Kolář, whose leadership on the ice is unmatched.
- Betting Prediction: With their strong defensive record, Sparta Praha is a safe bet for a narrow win.
- HC Kometa Brno:
- Recent Form: HC Kometa Brno has shown resilience, securing three wins in their last four games.
- Key Players: Filip Král is expected to be a pivotal player, known for his clutch scoring ability.
- Betting Prediction: Despite their offensive prowess, HC Kometa Brno faces an uphill battle against Sparta's defense.
Match 2: VHK Vsetín vs. Mountfield HK
This matchup features two teams with contrasting styles. VHK Vsetín is renowned for their aggressive play and quick transitions, while Mountfield HK excels in maintaining possession and executing precise plays.
- VHK Vsetín:
- Recent Form: VHK Vsetín has been struggling recently, with only one win in their last five matches.
- Key Players: Pavel Kašpařík's experience could be crucial in turning the tide for Vsetín.
- Betting Prediction: Given their current form, betting on an away win for Mountfield HK seems promising.
- Mountfield HK:
- Recent Form: Mountfield HK has been consistent, securing two wins and two draws in their recent outings.
- Key Players: Jakub Jeřábek's leadership and strategic playmaking are vital for Mountfield's success.
- Betting Prediction: Mountfield HK is likely to capitalize on Vsetín's vulnerabilities and secure a victory.
Match 3: Bílí Tygři Liberec vs. Pardubice HC
The rivalry between Bílí Tygři Liberec and Pardubice HC is set to intensify as they face off tomorrow. Both teams are known for their passionate fan bases and competitive spirit.
- Bílí Tygři Liberec:
- Recent Form: Bílí Tygři Liberec has been on a roll, winning three consecutive matches.
- Key Players: Tomáš Hyka's speed and agility make him a constant threat to opponents.
- Betting Prediction: With their current momentum, Bílí Tygři Liberec is a strong contender for a home win.
- Pardubice HC:
- Recent Form: Pardubice HC has had a mixed bag of results, with two wins and two losses recently.
- Key Players: David Pastrňák's experience and skill are crucial for Pardubice's chances of success.
- Betting Prediction: While Pardubice HC has potential, they might struggle against Liberec's current form.
Match 4: Orlovští Medvědi vs. BK Mladá Boleslav
The battle between Orlovští Medvědi and BK Mladá Boleslav promises to be a tactical showdown. Orlovští Medvědi is known for their disciplined play, while BK Mladá Boleslav thrives on creativity and flair.
- Orlovští Medvědi:
- Recent Form: Orlovští Medvědi have been inconsistent, with alternating wins and losses in recent matches.
- Key Players: Jiří Novotný's defensive skills are vital for Orlovští Medvědi's strategy.
- Betting Prediction: Orlovští Medvědi could secure a narrow win if they maintain their defensive discipline.
- BK Mladá Boleslav:
- Recent Form: BK Mladá Boleslav has shown improvement, winning two of their last three games.
- Key Players: Petr Sýkora's vision on the ice can turn the game in Mladá Boleslav's favor.
- Betting Prediction: BK Mladá Boleslav has the potential to upset Orlovští Medvědi with their creative playmaking.
Detailed Analysis of Key Factors Influencing Tomorrow's Matches
Injuries and Player Availability
Injuries can significantly impact team performance. For instance, Sparta Praha might be without their star forward due to a minor injury, which could affect their offensive capabilities. Conversely, HC Kometa Brno has confirmed that all key players are fit and ready to compete at full strength. Keeping an eye on injury reports can provide valuable insights into potential game outcomes.
Tactical Approaches
The tactical strategies employed by coaches will play a crucial role in determining the results of tomorrow's matches. Sparta Praha is expected to focus on a defensive strategy to counteract HC Kometa Brno's aggressive offense. Meanwhile, VHK Vsetín might adopt an all-out attacking approach to break through Mountfield HK's solid defense. Understanding these tactical nuances can help bettors make more informed predictions.
Historical Head-to-Head Records
Analyzing historical head-to-head records can offer insights into team dynamics and past performances. Bílí Tygři Liberec has historically dominated Pardubice HC in recent encounters, which could give them a psychological edge going into tomorrow's match. On the other hand, Orlovští Medvědi and BK Mladá Boleslav have had closely contested games in the past, making this matchup particularly unpredictable.
Betting Strategies for Tomorrow's Matches
Odds Analysis
Evaluating betting odds from various bookmakers can reveal discrepancies that savvy bettors can exploit. For example, if one bookmaker offers better odds for an underdog like BK Mladá Boleslav compared to others, it might be worth considering placing a bet on them. Additionally, monitoring live odds during the match can provide opportunities for in-play betting based on real-time developments.
Moneyline Betting
davek/hybrid-image<|file_sep|>/README.md
# Hybrid Image
This project was developed as part of CS231n (Stanford University) course assignment.
The goal of this project is to create hybrid image using low-pass filter (Gaussian blur) and high-pass filter (laplacian kernel).
# Setup
* Install Python dependencies:
pip install -r requirements.txt
# Running
* Download dataset (COCO dataset):
python download.py
* Generate images:
python generate.py --dataset_path=/path/to/dataset/ --low_pass_kernel_size=5 --high_pass_kernel_size=11
<|file_sep|># This file may be used to create an environment using:
# $ conda create --name=
--file=
numpy=1.18.*
opencv=4.*
scikit-image=0.*
matplotlib=3.*
tqdm=4.*
<|repo_name|>davek/hybrid-image<|file_sep|>/requirements.txt
numpy==1.18.*
opencv-python==4.*
scikit-image==0.*
matplotlib==3.*
tqdm==4.*<|repo_name|>davek/hybrid-image<|file_sep|>/generate.py
import argparse
import os
import random
from PIL import Image
from skimage import io
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
def get_image_list(dataset_path):
image_list = []
# iterate over directories
for dir_name in os.listdir(dataset_path):
# skip non-directories
if not os.path.isdir(os.path.join(dataset_path , dir_name)):
continue
# iterate over images
image_dir = os.path.join(dataset_path , dir_name)
for img_file_name in os.listdir(image_dir):
# skip non-images
if not img_file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
continue
image_list.append(os.path.join(image_dir , img_file_name))
return image_list
def load_image(path):
return io.imread(path)
def save_image(path , image):
io.imsave(path , image)
def apply_filter(image , kernel):
filtered_image = np.zeros_like(image)
# pad image with zeros
pad_x = kernel.shape[0] // 2
pad_y = kernel.shape[1] // 2
padded_image = np.pad(image , ((pad_x , pad_x) , (pad_y , pad_y)) , mode='constant')
# convolve padded image with kernel
for i in range(image.shape[0]):
for j in range(image.shape[1]):
filtered_image[i][j] = np.sum(padded_image[i:i+kernel.shape[0], j:j+kernel.shape[1]] * kernel)
# clip values between [0 , 255]
filtered_image[i][j] = min(255 , max(0 , filtered_image[i][j]))
# convert float values back to integers
filtered_image[i][j] = int(filtered_image[i][j])
return filtered_image
def apply_low_pass_filter(image , size):
low_pass_kernel = np.ones((size , size)) / size ** 2
return apply_filter(image , low_pass_kernel)
def apply_high_pass_filter(image , size):
# generate laplacian kernel using function from skimage library
high_pass_kernel = -1 * np.array([[0 , -1 , 0],
[-1 , 4 ,-1],
[0 ,-1 , 0]])
# resize kernel
high_pass_kernel_resized = np.zeros((size , size))
# fill center elements with values from generated laplacian kernel
center_x = size // 2 - high_pass_kernel.shape[0] // 2
center_y = size // 2 - high_pass_kernel.shape[1] // 2
high_pass_kernel_resized[center_x:center_x+high_pass_kernel.shape[0], center_y:center_y+high_pass_kernel.shape[1]] = high_pass_kernel
# fill other elements with zeros from generated laplacian kernel
high_pass_kernel_resized *= -1
# fill center element with ones
high_pass_kernel_resized[size//2][size//2] = size ** 2 - np.sum(high_pass_kernel_resized)
return apply_filter(image , high_pass_kernel_resized)
def generate_hybrid_image(image_1_path , image_2_path , low_pass_size_1 , low_pass_size_2 ,
high_pass_size_1 , high_pass_size_2):
# load images from paths provided by user
image_1 = load_image(image_1_path)
image_2 = load_image(image_2_path)
# resize images so that they have same dimensions
if image_1.shape != image_2.shape:
smaller_dim = min([image_1.shape[0], image_1.shape[1], image_2.shape[0], image_2.shape[1]])
resized_image_1 = Image.fromarray(image_1).resize((smaller_dim , smaller_dim))
resized_image_2 = Image.fromarray(image_2).resize((smaller_dim , smaller_dim))
resized_image_array_1 = np.array(resized_image_1)
resized_image_array_2 = np.array(resized_image_2)
image_1 = resized_image_array_1
image_2 = resized_image_array_2
# apply filters on images provided by user
low_filtered_image_1 = apply_low_pass_filter(image=image_1.copy() , size=low_pass_size_1)
low_filtered_image_2 = apply_low_pass_filter(image=image_2.copy() , size=low_pass_size_2)
high_filtered_image_1 = apply_high_pass_filter(image=image_1.copy() , size=high_pass_size_1)
high_filtered_image_2 = apply_high_pass_filter(image=image_2.copy() , size=high_pass_size_2)
hybrid_low_high_images_paths_dict={}
hybrid_low_high_images_paths_dict['hybrid_low_high'] = os.path.join('results' ,'hybrid_low_high.png')
hybrid_low_high_images_paths_dict['low_filtered'] = os.path.join('results' ,'low_filtered.png')
hybrid_low_high_images_paths_dict['high_filtered'] = os.path.join('results' ,'high_filtered.png')
hybrid_low_high_images_paths_dict['image_original'] = os.path.join('results' ,'image_original.png')
hybrid_low_high_images_paths_dict['image_original_s'] = os.path.join('results' ,'image_original_small.png')
hybrid_low_high_images_paths_dict['image_low_filtered'] = os.path.join('results' ,'image_low_filtered.png')
hybrid_low_high_images_paths_dict['image_high_filtered'] = os.path.join('results' ,'image_high_filtered.png')
hybrid_low_high_images_paths_dict['image_hybrid'] = os.path.join('results' ,'image_hybrid.png')
hybrid_low_filtered = (low_filtered_image_1.astype(np.float32) + low_filtered_image_2.astype(np.float32)) / (255 * 2)
hybrid_high_filtered =(high_filtered_image_1.astype(np.float32) + high_filtered_image_2.astype(np.float32)) / (255 * -8)
hybrid_low_high =(hybrid_low_filtered + hybrid_high_filtered).astype(np.uint8)
save_image(hybrid_low_high_images_paths_dict['hybrid_low_high'] ,hybrid_low_high)
save_image(hybrid_low_high_images_paths_dict['low_filtered'] ,(255 * hybrid_low_filtered).astype(np.uint8))
save_image(hybrid_low_high_images_paths_dict['high_filtered'] ,(255 * hybrid_high_filtered).astype(np.uint8))
save_image(hybrid_low_high_images_paths_dict['image_original_s'],Image.fromarray(np.hstack([image_original_small,image_original_small,image_original_small]).astype(np.uint8)))
save_image(hybrid_low_high_images_paths_dict['image_original'],Image.fromarray(np.hstack([image_original,image_original,image_original]).astype(np.uint8)))
save_image(hybrid_low_high_images_paths_dict['image_hybrid'],Image.fromarray(np.hstack([hybrid_low_high.astype(np.uint8),hybrid_low_high.astype(np.uint8),hybrid_low_high.astype(np.uint8)]).astype(np.uint8)))
save_image(hybrid_low_high_images_paths_dict['image_low_filtered'],Image.fromarray(np.hstack([(255 * hybrid_low_filtered).astype(np.uint8),(255 * hybrid_low_filtered).astype(np.uint8),(255 * hybrid_low_filtered).astype(np.uint8)]).astype(np.uint8)))
save_image(hybrid_low_high_images_paths_dict['image_high_filtered'],Image.fromarray(np.hstack([(255 * hybrid_high_filtered).astype(np.uint8),(255 * hybrid_high_filtered).astype(np.uint8),(255 * hybrid_high_filtered).astype(np.uint8)]).astype(np.uint8)))
fig=plt.figure(figsize=(12,5))
ax=[]
ax.append(plt.subplot(141))
plt.imshow(low_filtered_img[:, :, ::-1])
plt.title('Low Pass Filtered Image n Kernel Size {}'.format(low_pass_size))
plt.axis("off")
ax.append(plt.subplot(142))
plt.imshow(high_filtered_img[:, :, ::-1])
plt.title('High Pass Filtered Image n Kernel Size {}'.format(high_pass_size))
plt.axis("off")
ax.append(plt.subplot(143))
plt.imshow(hybrid_img[:, :, ::-1])
plt.title('Hybrid Image')
plt.axis("off")
if __name__ == '__main__':
