Exploring the Excitement of Ligue 1 Benin
The Ligue 1 Benin, a beacon of football passion in West Africa, offers a thrilling spectacle for enthusiasts and bettors alike. With its dynamic matches updated daily, this league promises a fresh dose of excitement each day. Dive into the world of Ligue 1 Benin, where expert betting predictions and match analyses provide an edge to those seeking to maximize their enjoyment and potential winnings.
Understanding the Structure of Ligue 1 Benin
Ligue 1 Benin is structured to foster competitive play and showcase local talent. The league consists of numerous teams that compete throughout the season, with each team vying for the top spot. Matches are held across various stadiums in Benin, bringing together fans from all walks of life to support their favorite teams.
Daily Updates: Keeping You Informed
One of the standout features of following Ligue 1 Benin is the commitment to providing daily updates on matches. This ensures that fans and bettors have access to the latest information, allowing them to make informed decisions. Whether you're tracking team standings or analyzing player performances, these updates are invaluable.
Expert Betting Predictions: Your Guide to Success
Betting on Ligue 1 Benin can be both exciting and rewarding. To enhance your experience, expert betting predictions offer insights into potential outcomes based on statistical analysis and expert opinions. These predictions take into account various factors such as team form, head-to-head records, and player injuries, providing a comprehensive view for those looking to place informed bets.
Key Factors Influencing Match Outcomes
- Team Form: Analyzing recent performances can provide clues about a team's current momentum.
- Head-to-Head Records: Historical matchups between teams can reveal patterns and tendencies.
- Injuries and Suspensions: The availability of key players can significantly impact a team's performance.
- Home Advantage: Teams often perform better on familiar ground, making home matches a critical factor.
Detailed Match Analysis: A Closer Look
For those interested in delving deeper into each match, detailed analyses provide a wealth of information. These analyses cover tactical setups, player roles, and strategic insights that can influence the outcome of a game. By understanding these elements, fans and bettors can gain a deeper appreciation for the intricacies of the sport.
Top Teams to Watch in Ligue 1 Benin
- FC Requins de l'Atlantique: Known for their aggressive playstyle and strong defensive tactics.
- Savanes du Nord: Renowned for their fast-paced attacks and skilled midfielders.
- Jegres de la Marina: A team with a solid track record in recent seasons, boasting a balanced squad.
- Dorados FC: Emerging as a formidable force with their strategic gameplay and young talent.
The Role of Fans in Ligue 1 Benin
Fans play a crucial role in the vibrancy of Ligue 1 Benin. Their support fuels the teams' spirits and creates an electrifying atmosphere during matches. Fan culture in Benin is rich with traditions, chants, and celebrations that add to the overall excitement of the league.
Engaging with Ligue 1 Benin Online
In today's digital age, engaging with Ligue 1 Benin online has never been easier. Numerous platforms offer live streaming services, allowing fans worldwide to watch matches in real-time. Social media channels provide updates, fan interactions, and exclusive content, keeping enthusiasts connected to the league's pulse.
Betting Strategies: Maximizing Your Potential
- Diversify Your Bets: Spread your bets across different types of wagers to manage risk effectively.
- Analyze Odds Carefully: Compare odds from multiple bookmakers to find the best value.
- Stay Informed: Regularly check for updates on team news and match reports to make timely decisions.
- Bet Responsibly: Always gamble within your means and set limits to ensure a positive experience.
The Future of Ligue 1 Benin: Trends and Developments
The future of Ligue 1 Benin looks promising with ongoing developments aimed at enhancing the league's competitiveness and appeal. Initiatives such as improved infrastructure, increased sponsorship deals, and youth development programs are set to elevate the standard of football in Benin. As these efforts unfold, the league is poised for greater recognition both locally and internationally.
Frequently Asked Questions About Ligue 1 Benin
<|repo_name|>richarddejager/CarND-Vehicle-Detection<|file_sep|>/README.md
# **Vehicle Detection Project**
The goals / steps of this project are the following:
* Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
* Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
* Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.
* Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
* Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
* Estimate a bounding box for vehicles detected.
[//]: # (Image References)
[image1]: ./output_images/car_not_car.png
[image2]: ./output_images/HOG_example.png
[image3]: ./output_images/sliding_windows.png
[image4]: ./output_images/bboxes_and_heat.png
[image5]: ./output_images/labels_map.png
[image6]: ./output_images/output_bboxes.png
## [Rubric](https://review.udacity.com/#!/rubrics/513/view) Points
###Here I will consider the rubric points individually and describe how I addressed each point in my implementation.
---
###Writeup / README
####1. Provide a Writeup / README that includes all the rubric points and how you addressed each one.
You're reading it!
###Histogram of Oriented Gradients (HOG)
####1. Explain how (and identify where in your code) you extracted HOG features from the training images.
The code for this step is contained in lines # through # inside `train.py` (and `project_functions.py`).
I started by reading in all the `vehicle` and `non-vehicle` images.
Here is an example of one of each of the `vehicle` and `non-vehicle` classes:
![alt text][image1]
I then explored different color spaces and different `skimage.hog()` parameters (`orientations`, `pixels_per_cell`, and `cells_per_block`).
I grabbed random images from each of the two classes and displayed them to get a feel for what the `skimage.hog()` output looks like.
Here is an example using the `YCrCb` color space and HOG parameters H = `orientations=9`, P = `pixels_per_cell=8x8` , C = `cells_per_block=3x3`, `hog_channel='ALL'`, which gave me an overall accuracy score above .98.
![alt text][image2]
####2. Explain how you settled on your final choice of HOG parameters.
I tried various combinations of parameters on my own but then I also ran through some values suggested by Udacity instructor Alex Krotov.
In particular I found his suggestion below interesting:
python
# Define HOG parameters
orient = [9]
pix_per_cell = [8]
cell_per_block = [3]
hog_channel = "ALL"
This gave me an overall accuracy score above .98 which is more than enough.
###Color Spaces
####3. Describe how (and identify where in your code) you settled on your final choice of color space when extracting HOG features.
I chose YCrCb color space because it separates luminance from chrominance components which means that changes in illumination will not affect chrominance components so much.
In my case it was also because I saw that Alex Krotov suggested it.
###Sliding Window Search
####4. Describe how (and identify where in your code) you implemented a sliding window search. How did you decide what scales to search and how much to overlap windows?
I decided not to implement my own sliding window search but rather use one provided by Udacity instructor Alex Krotov.
In particular I used his function called find_cars() which implements sliding window search over multiple scales.
I used scales ranging from .75x up to full size (i.e., scale=1).
###Video Implementation
####5. Provide a link to your final video output. Your pipeline should perform reasonably well on the entire project video (somewhat wobbly or unstable bounding boxes are ok as long as you are identifying the vehicles most of the time with minimal false positives.)
Here's a [link to my video result](./project_video_out.mp4)
###Discussion
####6. Briefly discuss any problems / issues you faced in your implementation of this project. Where will your pipeline likely fail? What could you do to make it more robust?
The main issue I had was with false positives around light posts when using just one frame from video stream.
To overcome this issue I decided not only look at one frame but also look at previous frames.
This way I was able to average out false positives over several frames.
This did not completely eliminate false positives but did significantly reduce them.
As suggested by Alex Krotov I also implemented heat maps which further reduced false positives.
There were still some issues with light posts but overall results were very good.<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Wed May 31
@author: Richard DeJager
"""
import numpy as np
import cv2
import glob
import matplotlib.image as mpimg
from skimage.feature import hog
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from scipy.ndimage.measurements import label
def get_hog_features(img,
orient,
pix_per_cell,
cell_per_block,
vis=False,
feature_vec=True):
if vis == True:
features,hog_image = hog(img,
orientations=orient,
pixels_per_cell=(pix_per_cell,pix_per_cell),
cells_per_block=(cell_per_block,cell_per_block),
transform_sqrt=True,
visualise=vis,
feature_vector=feature_vec)
return features,hog_image
else:
features = hog(img,
orientations=orient,
pixels_per_cell=(pix_per_cell,pix_per_cell),
cells_per_block=(cell_per_block,cell_per_block),
transform_sqrt=True,
visualise=vis,
feature_vector=feature_vec)
return features
def bin_spatial(img,color_space,size):
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img,cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img,cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img,cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img,cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img,cv2.COLOR_RGB2YCrCb)
else:
feature_image = np.copy(img)
return cv2.resize(feature_image,(size,size)).ravel()
def color_hist(img,nbins):
channel1_hist = np.histogram(img[:,:,0],nbins)
channel2_hist = np.histogram(img[:,:,1],nbins)
channel3_hist = np.histogram(img[:,:,2],nbins)
hist_features = np.concatenate((channel1_hist[0],
channel2_hist[0],
channel3_hist[0]))
return hist_features
def extract_features(image_paths,color_space='RGB',
spatial_size=(32,32),
hist_bins=32,
orient=9,
pix_per_cell=8,
cell_per_block=3,
hog_channel='ALL',
spatial_feat=True,hist_feat=True,hog_feat=True):
features_list=[]
for file_pth in image_paths:
image=cv2.imread(file_pth)
image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
hog_features=[]
if spatial_feat==True:
spatial_features=bin_spatial(image,color_space=spatial_size)
features_list.append(spatial_features)
if hist_feat==True:
hist_features=color_hist(image,nbins=hist_bins)
features_list.append(hist_features)
if hog_feat==True:
if hog_channel=='ALL':
hog_features=hog_channel(image[:,:,0],orient,pix_per_cell,cell_per_block,True)
hog_features=hog_channel(image[:,:,1],orient,pix_per_cell,cell_per_block,True)
hog_features=hog_channel(image[:,:,0],orient,pix_per_cell,cell_per_block,True)
hog_features=np.ravel(hog_features)
features_list.append(hog_features)
else:
hog_features=hog_channel(image[:,:,hog_channel],orient,pix_per_cell,cell_per_block,True)
features_list.append(hog_features)
return np.concatenate(features_list)
def slide_window(img,wins):
windows=[]
for win_size in wins:
x_start_stop=[None,None]
y_start_stop=[None,None]
xy_window=(win_size[0],win_size[1])
if win_size==wins[0]:
x_start_stop=[None,None]
y_start_stop=[int(400/720*img.shape[0]),int(600/720*img.shape[0])]
elif win_size==wins[1]:
x_start_stop=[int(400/1280*img.shape[1]),int(960/1280*img.shape[1])]
y_start_stop=[int(400/720*img.shape[0]),int(640/720*img.shape[0])]
elif win_size==wins[3]:
x_start_stop=[int(400/1280*img.shape[1]),int(960/1280*img.shape[1])]
y_start_stop=[int(400/720*img.shape[0]),int(640/720*img.shape[0])]
elif win_size==wins[-1]:
x_start_stop=[int(400/1280*img.shape[1]),int(960/1280*img.shape[1])]
y_start_stop=[int(440/720*img.shape[0]),int(640/720*img.shape[0])]
window_list=find_windows(img,x_start_stop,y_start_stop,sxy_overlap=(xy_overlap),xy_window=xy_window)
windows.extend(window_list)
return windows
def find_windows(img,x_start_stop,y_start_stop,sxy_overlap=(0.5,0.5),xy_window=(64,64)):
windows=[]
if x_start_stop[0]==None:
x_start=x_start_stop[0]=0
else:
x_start=x_start_stop[0]
if x_start_stop[-1]==None:
x_end=x_start_stop[-1]=img.shape[1]
else:
x_end=x_start_stop[-1]
if y_start_stop[0]==None:
y_start=y_start_stop[0]=0
else:
y_start=y_start_stop[0]
if y_start_stop[-1]==None:
y_end=y_start_stop[-1]=img.shape[0]
else:
y_end=y_start_stop[-1]
nx_pix_step=int(xy_window/sxy_overlap[0])
ny_pix_step=int(xy_window/sxy_overlap[1])
nx_windows=int((x_end-x_start)/nx_pix_step)-y
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Wed May 31
@author: Richard DeJager
"""
import numpy as np
import cv2
import glob
import matplotlib.image as mpimg
from skimage.feature import hog
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from scipy.ndimage.measurements import label
def get_hog_features(img,
orient,
pix_per_cell,
cell_per_block,
vis=False,
feature_vec=True):
# Call with two outputs if vis==True
# Call with one output if vis==False
# Define blocks and steps as above
# Compute individual channel HOG features
# Initialize our HOG feature vector
# Concatenate the HOG features into a single feature vector
# Return either visualization or just the feature vector
def bin_spatial(img,color_space,size):
def color_hist(img,nbins):
def extract_features(image_paths,color_space='RGB',
spatial_size=(32,32),
hist_bins=32,
orient=9,
pix_per_cell=8,
cell_per_block=3,
hog_channel='ALL',
spatial_feat=True,hist_feat=True,hog_feat=True):
def slide_window(img,wins):
def find_windows(img,x_start_stop,y_start_stop,sxy_overlap=(0.5,0.5),xy_window=(64,64)):
<|repo_name
