Stay Ahead with Expert Football 1. Deild Iceland Betting Predictions
Welcome to the ultimate guide for football enthusiasts and bettors looking to stay ahead in the thrilling world of 1. Deild Iceland. With fresh matches updated daily and expert betting predictions, you're equipped to make informed decisions that can lead to big wins. Dive into our comprehensive analysis and insights as we explore the teams, players, and strategies shaping this exciting league.
Understanding 1. Deild Iceland
1. Deild is the second tier of Icelandic football, serving as a crucial stepping stone for clubs aspiring to reach the top-flight Úrvalsdeild. The league's competitive nature ensures a dynamic and unpredictable season, making it a favorite among football fans and bettors alike.
Key Teams in 1. Deild
- Keflavík: Known for their resilient performances and passionate fan base.
- FH Hafnarfjörður: A club with a rich history and a strong focus on youth development.
- Víkingur Reykjavík: Consistently pushing for promotion with a robust squad.
- Stjarnan: A team that often surprises with their tactical prowess and determination.
Why Watch 1. Deild?
The league's unpredictability is its greatest allure. Matches are often closely contested, with any team capable of pulling off an upset. This makes it an exciting prospect for bettors looking for value bets and sharp odds.
Expert Betting Predictions
Our expert analysts provide daily betting predictions, leveraging data analytics and in-depth knowledge of the teams and players. Here's how you can maximize your betting potential:
Factors Influencing Predictions
- Team Form: Analyzing recent performances to gauge momentum.
- Injuries and Suspensions: Key players' availability can significantly impact match outcomes.
- Historical Head-to-Head: Understanding past encounters can offer valuable insights.
- Home/Away Advantage: Home teams often have an edge due to familiar conditions and fan support.
Betting Tips for Success
- Look for value bets where odds may not fully reflect a team's potential.
- Consider live betting to capitalize on in-game developments.
- Diversify your bets across different markets (e.g., match result, goalscorer, over/under).
- Set a budget and stick to it to ensure responsible gambling.
Sample Prediction: Keflavík vs. FH Hafnarfjörður
In an upcoming clash between Keflavík and FH Hafnarfjörður, our analysts predict a tight contest with a slight edge to Keflavík due to their recent form and home advantage. Key players to watch include Keflavík's striker, who has been in excellent scoring form, and FH Hafnarfjörður's midfield maestro, known for his playmaking abilities.
Possible Betting Markets
- Match Result: Keflavík win (2.5 odds)
- Goalscorer: Keflavík striker (3.0 odds)
- Total Goals Over/Under: Over 2.5 goals (1.8 odds)
In-Depth Team Analysis
Keflavík: The Resilient Contenders
Keflavík has consistently shown resilience in their performances, often bouncing back from setbacks with determination. Their squad depth allows them to rotate effectively, keeping players fresh throughout the season.
Key Players
- Jónas Jónsson: A prolific striker whose finishing ability makes him a constant threat.
- Einar Karlsson: A versatile midfielder known for his defensive contributions and vision on the field.
FH Hafnarfjörður: Youthful Exuberance Meets Experience
FH Hafnarfjörður combines youthful energy with experienced heads, creating a balanced squad capable of challenging any opponent. Their focus on developing young talent ensures a bright future for the club.
Key Players
- Brynjar Bjarki Gunnarsson: A dynamic forward with impressive pace and dribbling skills.
- Ari Þór Ingason: A seasoned defender providing stability at the back.
Víkingur Reykjavík: The Promotion Aspirants
Víkingur Reykjavík remains committed to their goal of returning to the Úrvalsdeild, investing in quality players and a strong coaching staff. Their ambition is evident in every match they play.
Key Players
- Gylfi Einarsson: A creative midfielder whose vision and passing range are crucial for Víkingur's attacking plays.
- Hannes Þór Halldórsson: A reliable goalkeeper known for his shot-stopping ability and leadership qualities.
Stjarnan: The Tactical Surprise Package
Stjarnan often surprises opponents with their tactical acumen, making them a formidable opponent in any fixture. Their strategic approach allows them to exploit weaknesses effectively.
Key Players
- Einar Steingrímsson: An experienced defender whose tactical intelligence is vital for Stjarnan's defensive setup.
- Rúnar Már Sigurjónsson: A playmaker capable of unlocking defenses with his creativity and passing accuracy.
Daily Match Updates and Analysis
<|repo_name|>albertocapecci/multimodal-recognition<|file_sep|>/notebooks/02_bounding-boxes-for-the-images.md
# Bounding Boxes for the Images
In this notebook we will download bounding boxes annotations from [COCO](http://cocodataset.org/#home) dataset using [pycocotools](https://github.com/cocodataset/cocoapi) library.
COCO annotations are available at [http://cocodataset.org/#download](http://cocodataset.org/#download). We will download them from [this link](http://images.cocodataset.org/annotations/annotations_trainval2017.zip). It contains three files:
* **instances_train2017.json**: bounding boxes annotations (we will use this file)
* **captions_train2017.json**: captions annotations
* **person_keypoints_train2017.json**: human keypoints annotations
The annotation file format is explained at [this link](http://cocodataset.org/#format-data).
We will use COCO images from *train2017* set.
## Setup
First we need to install `pycocotools` package.
python
!pip install pycocotools
Then we download COCO annotations.
python
!mkdir -p ~/data/coco/
!wget -nc -P ~/data/coco/ http://images.cocodataset.org/annotations/annotations_trainval2017.zip
!unzip -nq -d ~/data/coco/ ~/data/coco/annotations_trainval2017.zip
Finally we import some libraries.
python
%matplotlib inline
import os
import json
import random
import numpy as np
from matplotlib import pyplot as plt
from pycocotools.coco import COCO
## Download images
We first download COCO images from *train2017* set.
python
!mkdir -p ~/data/coco/train2017/
!wget -nc -P ~/data/coco/train2017/ http://images.cocodataset.org/zips/train2017.zip
!unzip -nq -d ~/data/coco/train2017/ ~/data/coco/train2017.zip
## Load annotations
We load annotations from *instances_train2017.json* file.
python
coco = COCO(os.path.expanduser('~/data/coco/annotations/instances_train2017.json'))
We can obtain some information about this dataset using methods provided by `pycocotools`.
python
print(coco)
print()
print('Categories:', coco.getCatIds())
print('Images:', coco.getImgIds())
print('Annotations:', coco.getAnnIds())
COCO Object Class: 'instance'
APIs:
img : loadImgs imgToAnns annToImgs getCatIds getImgIds getAnnIds loadAnns loadImage showAnns showCat showImg showLoadImgs showLoadAnns showLoadCats download initLicense catToImgs createIndex dataType datasetName versionInfo getAnnByImgId getCatName getCatIdsByImgId getImgIdsByCatId getImgIdsByAnnIds getImgSize getImgSubList getCatSubList loadImgs loadAnns loadCats loadCatsAndSupercats loadCatsAndImgs loadImgsAndAnnIds loadImgsAndAnns loadImgsAndCats loadImgsAndAnnIdsAndAnns convertToOriginalImageSpace
Categories: [1, ... ,90]
Images: [ ... ,253619]
Annotations: [ ... ,2610259]
As you can see there are many categories (90), many images (123287) and many annotations (1178666).
## Explore images
We can obtain image information by calling `coco.loadImgs` method.
python
img = coco.loadImgs(coco.getImgIds()[0])[0]
print(img)
{'license': 'undefined',
'url': 'http://farm6.staticflickr.com/5596/14995926524_6b79a9a19b_z.jpg',
'file_name': '000000000139.jpg',
'height': 427,
'width':640,
'date_captured': '2016-09-03T22:21:17Z',
'id':124850}
The returned dictionary contains information about image such as license information (`license`), URL (`url`), image file name (`file_name`), image height (`height`) width (`width`), date captured (`date_captured`) etc...
We can also obtain annotation ids by calling `coco.getAnnIds` method.
python
ann_ids = coco.getAnnIds(img['id'])
print(ann_ids[:10])
[2610259,
2608068,
2608066,
2608065,
2608062,
2608058,
2608056,
2608053,
2608051,
2608048]
As you can see there are many annotation ids corresponding to one image id.
Finally we can obtain annotation information by calling `coco.loadAnns` method.
python
anns = coco.loadAnns(ann_ids)
print(anns[0])
{'segmentation': [[155.63,
...
,
,
,
,
,
,
,
,
],
...
],
'area': ... ,
'iscrowd': ... ,
'image_id': ... ,
'bbox': [...],
'category_id': ... ,
'id': ... }
The returned dictionary contains information about bounding box such as segmentation coordinates (`segmentation`), area (`area`), whether or not it is crowd (`iscrowd`) etc...
The most important key is `bbox`, which contains bounding box coordinates.
It has four values:
* x-coordinate of upper-left corner of bounding box
* y-coordinate of upper-left corner of bounding box
* width of bounding box
* height of bounding box
Now we plot some random images together with their corresponding bounding boxes.
python
def plot_bbox(img_id):
img = coco.loadImgs(img_id)[0]
ann_ids = coco.getAnnIds(img['id'])
anns = coco.loadAnns(ann_ids)
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(1,1,1)
ax.imshow(coco.loadImgs(img['id'])[0]['coco_url'])
bbox_colors = ['r','g','b','c','m','y']
bbox_color_idx = np.random.randint(0,len(bbox_colors))
for ann in anns:
if ann['iscrowd']:
continue
bbox = ann['bbox']
x,y,w,h = bbox
rect = plt.Rectangle((x,y),
w,h,
fill=False,
linewidth=2,
edgecolor=bbox_colors[bbox_color_idx])
ax.add_patch(rect)
bbox_color_idx +=1
if bbox_color_idx == len(bbox_colors):
bbox_color_idx=0
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
plot_bbox(random.choice(coco.getImgIds()))
## Select classes
As you have seen there are many categories in COCO dataset.
For our problem we will select only few classes that are related to traffic signs.
These classes are:
* traffic light (category id=10)
* stop sign (category id=33)
* speed limit (category id=34)
Let's see how many annotations are available for these classes.
python
class_ids = [10,33,34]
counts = {class_id:coco.getCatCount(class_id) for class_id in class_ids}
for class_id,count in counts.items():
print('Class ID:',class_id,'Count:',count)
Class ID:10 Count:35792
Class ID:33 Count:13526
Class ID:34 Count:43389
As you can see there are many more speed limit signs than stop signs or traffic lights.
Let's also see how many images contain these objects.
python
img_ids_by_class_id = {class_id:coco.getImgIds(catIds=class_id) for class_id in class_ids}
img_counts_by_class_id = {class_id:len(img_ids) for class_id,img_ids in img_ids_by_class_id.items()}
for class_id,count in img_counts_by_class_id.items():
print('Class ID:',class_id,'Count:',count)
Class ID:10 Count:23473
Class ID:33 Count:8769
Class ID:34 Count:26466
Now let's plot some random images containing these objects together with their corresponding bounding boxes.
python
def plot_random_images_by_class(class_ids,n):
n_per_class = int(np.ceil(n / len(class_ids)))
fig = plt.figure(figsize=(20,n_per_class * len(class_ids)))
i=1
for class_id in class_ids:
img_ids = img_ids_by_class_id[class_id]
random_img_ids = np.random.choice(img_ids,n_per_class)
for img_id in random_img_ids:
ax = fig.add_subplot(len(class_ids),n_per_class,i)
ax.set_title(class_id)
plot_bbox(img_id)
i+=1
plot_random_images_by_class(class_ids=class_ids,n=12)
## Download images
Now we will download images containing these objects together with their corresponding annotations.
We will store them at *~/data/coco/class_10*, *~/data/coco/class_33*, *~/data/coco/class_34* directories respectively.
Each directory will contain two files:
* **images.json**: list of downloaded image file names together with their corresponding bounding boxes coordinates (*x*,*y*,*w*,*height*)
* **images.txt**: list of downloaded image file names
Here is how it works:
For each selected class we:
1. obtain all image ids containing this object using `getImgIdByCatId` method;
2. iterate over all image ids;
3. obtain all annotation ids corresponding to this image id using `getAnnIdByImgId` method;
4. iterate over all annotation ids;
5. check if annotation corresponds to current object using `category.id`;
6. if it does then we add this annotation into list;
7. after iterating over all annotation ids we save list into `images.json`;
8. after iterating over all image ids we save list of downloaded image file names into `images.txt`.
Here is the code:
python
def download_images_and_annotations(class_dir,class_id):
# Create directory if