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Landesliga Steiermark stats & predictions

Upcoming Football Matches in Landesliga Steiermark, Austria

Get ready for an exciting day of football in the Landesliga Steiermark, Austria, as several thrilling matches are set to take place tomorrow. Fans of local football are eagerly anticipating the action-packed fixtures that promise to deliver both high-octane performances and strategic brilliance. With teams vying for crucial points in the league standings, each match is poised to be a pivotal moment in the season. This article provides a detailed overview of the upcoming matches, expert betting predictions, and key insights into the teams and players to watch.

Austria

Landesliga Steiermark

Match Schedule

The Landesliga Steiermark is known for its competitive spirit and passionate fanbase. Tomorrow's fixtures are no exception, featuring several high-stakes encounters. Here's a rundown of the matches scheduled for tomorrow:

  • 10:00 AM: Team A vs. Team B
  • 12:30 PM: Team C vs. Team D
  • 3:00 PM: Team E vs. Team F
  • 5:30 PM: Team G vs. Team H

Expert Betting Predictions

As the excitement builds for tomorrow's matches, expert analysts have weighed in with their betting predictions. These insights are based on recent performances, head-to-head statistics, and current team form. Here's what the experts are forecasting:

Team A vs. Team B

This match is expected to be a closely contested affair. Team A has been in excellent form recently, winning three of their last four matches. However, Team B has a strong home record and is known for their resilience. Analysts predict a narrow victory for Team A with a scoreline of 2-1.

Team C vs. Team D

Team C enters this match on the back of a solid defensive performance against their last opponent. Meanwhile, Team D has been struggling with injuries but managed to secure a draw in their previous fixture. Experts believe that Team C will edge out a win with a predicted score of 1-0.

Team E vs. Team F

Known for their attacking prowess, Team E is expected to dominate this fixture against Team F, who have been inconsistent this season. The prediction is a high-scoring game with Team E likely to win 3-1.

Team G vs. Team H

Both teams have had mixed results recently, making this match difficult to predict. However, analysts favor Team G due to their superior midfield strength and tactical flexibility. The expected outcome is a 2-2 draw or a narrow win for Team G at 2-1.

Key Players to Watch

Each match features standout players who could make a significant impact on the outcome. Here are some key players to keep an eye on:

  • Team A: Striker John Doe - Known for his clinical finishing and pace, Doe has been instrumental in Team A's recent successes.
  • Team B: Midfielder Jane Smith - Smith's vision and passing ability make her a crucial playmaker for her team.
  • Team C: Defender Mark Johnson - Johnson's defensive acumen and leadership at the back are vital for Team C.
  • Team D: Forward Emily Brown - Brown's agility and goal-scoring instincts could be decisive in this fixture.
  • Team E: Winger Alex Green - Green's dribbling skills and creativity on the wing pose a constant threat to opponents.
  • Team F: Goalkeeper Chris White - White's reflexes and shot-stopping ability have been key in keeping clean sheets.
  • Team G: Captain Lisa Black - Black's experience and leadership qualities make her an influential figure on and off the pitch.
  • Team H: Midfielder Tom Grey - Grey's work rate and ability to break up play are crucial for Team H's defensive setup.

Tactical Analysis

Tomorrow's matches will not only be about individual brilliance but also tactical battles between managers. Here’s an analysis of the tactical approaches expected from each team:

Team A vs. Team B

Coach of Team A is likely to employ a high-pressing strategy to disrupt Team B's build-up play. On the other hand, Team B might opt for a compact defensive shape, looking to exploit counter-attacks.

Team C vs. Team D

Expect Team C to focus on maintaining possession and controlling the tempo of the game. Coach of Team D might set up his team to absorb pressure and hit on the break.

Team E vs. Team F

With their attacking flair, Team E will likely adopt an aggressive formation aiming to overwhelm Team F's defense. Conversely, Team F may prioritize defensive solidity and look for quick transitions.

Team G vs. Team H

Both teams might adopt flexible formations depending on the flow of the game. Tactical adjustments will be key as each manager seeks to exploit weaknesses in their opponent’s setup.

Past Performances and Head-to-Head Records

Historical data can often provide insights into potential outcomes of matches. Here’s a look at past performances and head-to-head records:

  • Team A vs. Team B: In their last five encounters, both teams have won twice each with one draw.
  • Team C vs. Team D: Historically, these matches have been closely contested with most ending in draws or narrow victories.
  • Team E vs. Team F: The previous encounters have favored Team E, who have won three out of four matches.
  • Team G vs. Team H: Both teams have had mixed results in past meetings with each winning two out of four games.

Injury Updates and Suspensions

Injuries and suspensions can significantly impact team dynamics and match outcomes. Here are the latest updates on player availability:

  • Team A: Full squad available; no suspensions or injuries reported.
  • Team B: Defender Jack Blue is sidelined due to a hamstring injury.
  • Team C: Midfielder Sam Red suspended for one match after receiving two yellow cards.
  • Team D:No major injury concerns; all key players fit to play.
  • Team E:Suspension alert: Striker Lucy Orange banned for three matches following a red card incident.
  • Team F:Injury update: Goalkeeper Paul Yellow recovering from ankle sprain; doubtful for tomorrow’s match.
  • Team G:All players fit; no suspensions or injury worries.
  • Team H:Midfielder Kevin Black doubtful due to knee injury sustained last week.
<|repo_name|>syzx2016/Video-Face-Detection<|file_sep|>/src/faceDetect.py #!/usr/bin/env python # -*- coding:utf-8 -*- import cv2 import numpy as np import os import time class FaceDetect(): def __init__(self): self.videoPath = './testVideo' self.videoSavePath = './detectVideo' self.faceCascade = cv2.CascadeClassifier('./haarcascade_frontalface_alt.xml') self.eyeCascade = cv2.CascadeClassifier('./haarcascade_eye.xml') self.smileCascade = cv2.CascadeClassifier('./haarcascade_smile.xml') self.noseCascade = cv2.CascadeClassifier('./haarcascade_mcs_nose.xml') self.fps = None def detect(self): if not os.path.exists(self.videoSavePath): os.makedirs(self.videoSavePath) for videoName in os.listdir(self.videoPath): if videoName.endswith('.mp4'): print 'Detecting ' + videoName + '...' videoFile = os.path.join(self.videoPath, videoName) cap = cv2.VideoCapture(videoFile) fourcc = cv2.VideoWriter_fourcc(*'XVID') fps = cap.get(cv2.CAP_PROP_FPS) size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) saveFile = os.path.join(self.videoSavePath, videoName) out = cv2.VideoWriter(saveFile,fourcc,fps,size) frameNum = cap.get(cv2.CAP_PROP_FRAME_COUNT) curFrameNum = int(0) while True: ret ,frame = cap.read() if ret == False: break curFrameNum +=1 faces ,eyes ,smiles ,noses = self.detectFace(frame) for faceRect in faces: x,y,w,h = faceRect cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),thickness=2) subFrame = frame[y:y+h,x:x+w] for eyeRect in eyes: ex,eY,ew,eH = eyeRect cv2.rectangle(subFrame,(ex,eY),(ex+ew,eY+eH),(0,255,0),thickness=1) for smileRect in smiles: sx,sY,sW,sH = smileRect cv2.rectangle(subFrame,(sx,sY),(sx+sW,sY+sH),(0,0,255),thickness=1) for noseRect in noses: nx,nY,nW,nH = noseRect cv2.rectangle(subFrame,(nx,nY),(nx+nW,nY+nH),(255,255,0),thickness=1) cv2.putText(frame,'Frame ' + str(curFrameNum) + '/' + str(frameNum),(10,int(50)),cv2.FONT_HERSHEY_SIMPLEX ,0.5,(0,0,255),thickness=1,lineType=cv2.LINE_AA) out.write(frame) if curFrameNum %100 ==0: print curFrameNum print 'Detecting ' + videoName + ' finished.' def detectFace(self,img): faces ,eyes ,smiles ,noses = [],[],[],[] faceImgList = self.faceCascade.detectMultiScale(img,scaleFactor=1.05,minNeighbors=5,minSize=(30,30)) for (x,y,w,h) in faceImgList: faces.append((x,y,w,h)) subImg = img[y:y+h,x:x+w] subImgGrayScale = cv2.cvtColor(subImg,cv2.COLOR_BGR2GRAY) subImgEqualizedGrayScale = cv2.equalizeHist(subImgGrayScale) eyeImgList = self.eyeCascade.detectMultiScale(subImgEqualizedGrayScale,scaleFactor=1,minNeighbors=5,minSize=(15,int(h*0.25))) for (ex,eY,eW,eH) in eyeImgList: if eW > eH*1 or eW > w*0.25 or eH > h*0.25 or ex+eW > w*0.9: continue eyes.append((x+ex,y+eY,eW,eH)) smileImgList = self.smileCascade.detectMultiScale(subImgGrayScale,scaleFactor=1,minNeighbors=15,minSize=(15,int(h*0.25))) for (sx,sY,sW,sH) in smileImgList: if sW > w*0.6 or sH > h*0.25 or sx+eW > w*0.9: continue smiles.append((x+sx,y+sY,sW,sH)) noseImgList = self.noseCascade.detectMultiScale(subImgGrayScale,scaleFactor=1,minNeighbors=5,minSize=(15,int(h*0.25))) for (nx,nY,nW,nH) in noseImgList: if nW > w*0.25 or nH > h*0.25 or nx+eW > w*0.9: continue noses.append((x+nx,y+nY,nW,nH)) return faces ,eyes ,smiles ,noses if __name__ == '__main__': FDectectObj = FaceDetect() FDectectObj.detect() <|repo_name|>syzx2016/Video-Face-Detection<|file_sep|>/README.md # Video-Face-Detection ## 基于opencv的视频人脸检测及人脸特征提取算法 ### 算法思路： 使用opencv中的haar特征检测器来实现人脸、眼睛、嘴巴、鼻子的检测，对于每一帧图像，先检测人脸，再对于每一个人脸区域，分别检测眼睛、嘴巴、鼻子。 ### 算法实现： 首先需要准备haar特征模型文件：face.xml、eye.xml、smile.xml、nose.xml。 然后，需要将待检测视频文件放置在testVideo目录下。在执行faceDetect.py文件时，将会自动从该目录中读取视频文件并执行检测，并将检测结果保存到detectVideo目录下。 <|file_sep|>#-*-coding:utf-8-*- from PIL import ImageGrab import numpy as np def getScreenCapture(): imgBox=(10000,-50000,-20000,-10000) #截取桌面图像坐标范围，可以调整这个值为自己所需的区域 im=ImageGrab.grab(bbox=imgBox) #截取桌面图像，bbox是截取的图像范围坐标 im.save("capture.png") #保存为png格式的图像 return im<|repo_name|>marcrobles/marcrobles.github.io<|file_sep|>/_posts/2021-07-09-python-markdown.md --- layout: post title: "Converting Python code into markdown using Sphinx" date: "2021-07-09" tags: [python] --- # Overview I recently wrote my first Python package [pydl](https://github.com/marcrobles/pydl). This package provides tools that can be used by developers who want easy access to Deep Learning models without having to understand all the technical details. The package includes support for many different architectures such as [ResNet](https://github.com/marcrobles/pydl/blob/master/pydl/resnet.py), [VGG](https://github.com/marcrobles/pydl/blob/master/pydl/vgg.py), [SqueezeNet](https://github.com/marcrobles/pydl/blob/master/pydl/squeezenet.py), [InceptionV3](https://github.com/marcrobles/pydl/blob/master/pydl/inceptionv3.py), [InceptionResNetV2](https://github.com/marcrobles/pydl/blob/master/pydl/inceptionresnetv2.py), [DenseNet121](https://github.com/marcrobles/pydl/blob/master/pydl/densenet121.py), [MobileNet](https://github.com/marcrobles/pydl/blob/master/pydl/mobilenet.py), [MobileNetV2](https://github.com/marcrobles/pydl/blob/master/pydl/mobilenetv2.py), [NASNetLarge](https://github.com/marcrobles/pydl/blob/master/pydl/nasnetlarge.py) among others. In order to use this package it is important that users know which models are available as well as what input dimensions they expect so that they can pass images with appropriate sizes. I decided that I wanted users to be able see information about available models by using `pydoc`. `pydoc` comes preinstalled with Python so it would be easy for users if they could just use `pydoc pydl` from any terminal window. The documentation included with `pydoc` looks like this: ![](../assets/img/posts/python-markdown/sphinx-docs.png) This looks good but there were two things that I did not like about it: 1) The output was generated using HTML which meant that I would need some kind of server side software running if I wanted users to view it online. ![](../assets/img/posts/python-markdown/sphinx-html.png) This was undesirable because it would require users install extra software if they wanted information about my package. A solution would be generating markdown instead since it can be hosted on GitHub pages without requiring any additional software. ![](../assets/img/posts/python-markdown/sphinx-markdown.png) This looks great! It can easily be hosted on GitHub pages! ## Generating Markdown Documentation using Sphinx In order to generate documentation like this we need Sphinx installed which can be done using pip: pip install sphinx Once Sphinx is installed we need some kind of Python code that we want documentation generated from. In my case I am