Discover the Thrill of Volleyball Mestaruusliiga Finland
Welcome to the ultimate guide for volleyball enthusiasts in Finland, where we delve into the exhilarating world of the Mestaruusliiga. This premier league showcases the finest talent in Finnish volleyball, offering fans a daily dose of excitement with fresh matches and expert betting predictions. Whether you're a seasoned fan or new to the sport, this comprehensive guide will keep you informed and engaged with every spike and serve.
The History and Significance of Mestaruusliiga
The Mestaruusliiga, established as Finland's top-tier volleyball league, has been a cornerstone of Finnish sports culture for decades. It not only provides a platform for local talent to shine but also attracts international players, enhancing the competitive spirit and quality of play. The league's history is rich with memorable matches and legendary players who have left an indelible mark on Finnish volleyball.
Understanding the League Structure
The Mestaruusliiga consists of several teams competing throughout the season, culminating in playoffs that determine the national champion. Each team plays multiple matches against their opponents, making every game crucial for securing a top position in the standings. Fans can follow their favorite teams closely as they battle it out on court.
Daily Updates: Stay Informed with Fresh Matches
One of the key features of our coverage is providing daily updates on all matches in the Mestaruusliiga. Whether it's a nail-biting final set or an unexpected upset, our team ensures you have access to real-time scores and highlights. This keeps fans connected to every moment of action, no matter where they are.
Expert Betting Predictions: Enhance Your Viewing Experience
For those interested in adding an extra layer of excitement to their viewing experience, our expert betting predictions offer valuable insights. Our analysts use advanced statistics and deep knowledge of team dynamics to provide accurate predictions that can help you make informed betting decisions.
Why Trust Our Expert Predictions?
- Data-Driven Analysis: Our predictions are based on comprehensive data analysis, ensuring accuracy and reliability.
- Expert Insights: Seasoned analysts bring years of experience and understanding of volleyball strategies to their predictions.
- Daily Updates: As new information becomes available, our predictions are updated to reflect current conditions.
Key Players to Watch in Mestaruusliiga
Each season brings new talents and rising stars who captivate audiences with their skills. Here are some key players currently making waves in the league:
- Jani Virtanen: Known for his powerful spikes and strategic playmaking.
- Eeva Koskinen: A formidable setter whose precision sets up numerous scoring opportunities.
- Mikko Nieminen: Renowned for his defensive prowess and ability to read opponents' plays.
The Role of Fan Engagement
Fan engagement is vital for maintaining the vibrancy and popularity of Mestaruusliiga. Through social media interactions, live match discussions, and fan events, supporters play an active role in shaping the league's atmosphere.
Fostering Community Among Fans
- Social Media Platforms: Engage with fellow fans through platforms like Twitter and Instagram.
- Fan Forums: Participate in discussions about team strategies and player performances.
- In-Person Events: Attend games at venues or participate in fan meet-ups organized by clubs.
Trends Shaping Future Seasons
<|repo_name|>yinlunxiao/PPD<|file_sep|>/code/cuda/nms_kernel.cu
#include "nms_kernel.h"
template
__global__ void nms_kernel(const int nthreads,
const int num_anchors,
const float nms_overlap_thresh,
const T1 *dets_xyxy_ptr,
const T4 *dets_area_ptr,
const int *inds_ptr,
const int *inds_offset_ptr,
int *keep_inds_ptr) {
#ifdef DEBUG_NMS
printf("nms_kernel <<< %d threads >>>n", nthreads);
#endif
__shared__ float block_nms_overlap_thresh;
__shared__ float block_dets_area[32];
__shared__ unsigned char block_keep_inds[32];
if (threadIdx.x ==0) {
block_nms_overlap_thresh = nms_overlap_thresh;
if (aligned) {
for (int i=0; i= num_anchors)
return;
// #ifdef DEBUG_NMS
// if ((threadIdx.x==0)&&(blockIdx.x==12)) {
// assert(0); // debug purpose only
// }
// #endif
#ifdef DEBUG_NMS
if ((threadIdx.x==0)&&(blockIdx.x==8)) {
assert(0); // debug purpose only
float det_xmin = dets_xyxy_ptr[block_keep_inds[anchor_ind]*4+0];
float det_ymin = dets_xyxy_ptr[block_keep_inds[anchor_ind]*4+1];
float det_xmax = dets_xyxy_ptr[block_keep_inds[anchor_ind]*4+2];
float det_ymax = dets_xyxy_ptr[block_keep_inds[anchor_ind]*4+3];
// check if it is already marked as non-maximal suppression box
unsigned char keep_flag;
keep_flag =
atomicExch(&keep_inds_ptr[block_keep_inds[anchor_ind]], static_cast(-1));
assert(keep_flag != -1);
// compare it with all other anchors from same image
// start from next anchor since we don't need compare itself
// since its overlap ratio will be zero
int start_idx= anchor_ind+1;
for(int j=start_idx; j= inter_xmin &&
inter_ymax >= inter_ymin);
// calculate intersection area
double inter_widht = inter_xmax - inter_xmin +T5(1e-10);
double inter_height= inter_ymax - inter_ymin +T5(1e-10);
double intersection_area=
static_cast(inter_widht)*static_cast(inter_height);
#ifdef DEBUG_NMS
if ((intersection_area > static_cast(T6(-FLT_EPSILON))) &&
((intersection_area > static_cast(T6(block_dets_area[j]))))) {
std::cerr << "intersection area should be smaller than "
<< "the area" << std::endl;
std::cerr << "intersection area:" << intersection_area << std::endl;
std::cerr << "det area:" << block_dets_area[j] << std::endl;
exit(-1);
}
if ((intersection_area > static_cast(T6(-FLT_EPSILON))) &&
((intersection_area > static_cast(T6(block_dets_area[anchor_ind]))))) {
std::cerr << "intersection area should be smaller than "
<< "the area" << std::endl;
std::cerr << "intersection area:" << intersection_area << std::endl;
std::cerr << "det area:" << block_dets_area[anchor_ind] << std::endl;
exit(-1);
}
if ((inter_widht <= static_cast(FLT_EPSILON)) ||
(inter_height<= static_cast(FLT_EPSILON))) {
assert(intersection_area <= static_cast(T6(FLT_EPSILON)));
}
#endif
double union_areas=
static_cast(block_dets_area[j])+
static_cast(block_dets_area[anchor_ind])-
intersection_area;
double overlap_ratio=static_cast(
intersection_area/union_areas);
#ifdef DEBUG_NMS
if((overlap_ratio > static_cast(block_nms_overlap_thresh)) &&
(overlap_ratio <=static_cast((float)T6(FLT_EPSILON)))){
std::cerr <<"overlap ratio should be larger than"
<<"the threshold"<<"n";
exit(-1);
}
#endif
unsigned char flag=
atomicExch(&keep_inds_ptr[block_keep_inds[j]],static_cast(-1));
#ifdef DEBUG_NMS
assert(flag != -1);
#endif
}
keep_flag=
atomicExch(&keep_inds_ptr[block_keep_inds[anchor_ind]],static_cast(-1));
#ifdef DEBUG_NMS
assert(keep_flag != -1);
#endif
return;
}
#endif
/*
* compute overlap between two boxes
*/
inline bool computeOverlap(const float* bbox_a,const int bbox_a_size,const
const float* bbox_b,const int bbox_b_size,float &overlap){
}
/*
* do non-maximum supression on GPU device using CUDA kernel function above
*/
void gpuNMS(float* host_boxes,int num_boxes,int num_classes,float thresh,int top_k){
}
/*
* do non-maximum supression on CPU using C++ code below instead CUDA kernel function above
*/
void cpuNMS(float* host_boxes,int num_boxes,int num_classes,float thresh,int top_k){
}
/*
* do non-maximum supression on GPU device using CUDA kernel function above or C++ code below based on input argument 'use_cpu'
*/
void nms(float* host_boxes,int num_boxes,int num_classes,float thresh,int top_k,bool use_cpu){
}
<|file_sep#include "../include/util.h"
#include "../include/caffe.hpp"
using namespace caffe;
template
void Yolov3Detector::Yolov3Detector() {
}
template
Yolov3Detector::~Yolov3Detector() {}
template
void Yolov3Detector::Init(std::string model_file,std::string trained_file){
}
template
void Yolovv3Detector::Detect(const cv::Mat& img,std::vector>& detections){
}
typedef Yolovv3Detector Yolovv3DetectorFP32;
<|repo_name|>yinlunxiao/PPD<|file_sep.html:
{
"version": "20191207",
"sha256": "be7f22c9b04fd9a52ba5c05c88681cd29b878a33c7eaa049f50e00bb9f65cf62",
"description": {
"title": "[Caffe] Object Detection Toolbox"
},
"homepage": "",
"license": {
"id": "proprietary"
},
"platforms": [
{
"os": "linux",
"architecture": {
"id": "$arch$"
}
}
],
}<|file_sep#ifndef NMS_KERNEL_H_
#define NMS_KERNEL_H_
#include "../include/cuda_util.h"
namespace caffe {
template
__global__ void nms_kernel(const int nthreads,const int num_anchors,const float nms_overlap_thresh,const T1*dets_xyxy,const T4*dets_areas,const int* inds,const int* inds_offsets,int* keep_inxs);
/**
*
*/
inline void gpuNMS(float* host_boxes,int num_boxes,int num_classes,float thresh,int top_k);
/**
*
*/
inline void cpuNMS(float* host_boxes,int num_boxes,int num_classes,float thresh,int top_k);
/**
*
*/
inline void nms(float* host_boxes,int num_boxes,int num_classes,float thresh,int top_k,bool use_cpu=false);
} /* namespace caffe */
#endif /* NMS_KERNEL_H_<|repo_name|>yinlunxiao/PPD<|file_sep Dictionary:
obj_detect.py:
framework: yolovv32000ppdcfg.txt / yolovv32000ppdcfg.txt -> yolovv32000cfg.txt / yolovv32000cfg.txt
detector:
detector_path: ./
deploy_prototxt_path : ./deploy.prototxt
model_path : ./yolovv32000ppdcfg.caffemodel / ./yolovv32000cfg.caffemodel
coco_dataset:
dataset_dir : /home/ubuntu/dataset/coco2017/
annotation_file : annotations/instances_val2017.json
pascal_voc_dataset:
dataset_dir : /home/ubuntu/dataset/pascal_voc2007/
annotation_file : annotations/voc2007.xml
main.py:
coco_dataset:
dataset_dir : /home/ubuntu/dataset/coco2017/
annotation_file : annotations/instances_val2017.json
pascal_voc_dataset:
dataset_dir : /home/ubuntu/dataset/pascal_voc2007/
annotation_file : annotations/voc2007.xml
yolo_dataset :
dataset_dir :
images_dir :
labels_dir :
yolo_detector:
<|repo_name|>yinlunxiao/PPD<|file_sep127m@ubuntu:/media/yilunxiao/DATA/code/research/ppd/code$ cat obj_detect.py | grep coco_dataset.dataset_dir | wc -l
15
127m@ubuntu:/media/yilunxiao/DATA/code/research/ppd/code$ cat obj_detect.py | grep pascal_voc_dataset.dataset_dir | wc -l
13
127m@ubuntu:/media/yilunxiao/DATA/code/research/ppd/code$ cat main.py | grep coco_dataset.dataset_dir | wc -l
14
127m@ubuntu:/media/yilunxiao/DATA/code/research/ppd/code$ cat main.py | grep pascal_voc_dataset.dataset_dir | wc -l
13
# https://github.com/YuwenXiong/Paper-Paper-Detect/blob/master/main.py
# https://github.com/YuwenXiong/Paper-Paper-Detect/blob/master/object_detection/object_detect.py
https://github.com/YuwenXiong/Paper-Paper-Detect/blob/master/object_detection/object_detect.py
https://github.com/YuwenXiong/Paper-Paper-Detect/blob/master/main.py
https://github.com/YuwenXiong/Paper-Paper-Detect/blob/master/data/coco2017.zip
https://github.com/YuwenXiong/Paper-Paper-Detect/tree/master/data/pascal_voc2007
https://github.com/YuwenXiong/Paper-Paper-Detect/tree/master/data/yolo-voc07-test-image-set
# yolo-voc07-test-image-set:
# https://drive.google.com/file/d/18HWR8RZtQJqIzGZbHzjPcLkVzHrWwOgi/view?usp=sharing
# https://www.kaggle.com/sparshmathur/person-detection-dataset/download
# https://drive.google.com/drive/folders/19oVwumUEoAivtVqQGJsjMIdEJbUASQkS?usp=sharing
## coco:
/home/ubuntu/dataset/coco2017/
/home/ubuntu/dataset/coco2017/images/
/home/ubuntu/dataset/coco2017/annotations/
/home/ubuntu/dataset/coco2017/samples/
/home/ubuntu/dataset/coco2017/test-images/
/home/ubuntu/dataset/coco2017/trainval35k/
/home/ubuntu/dataset/coco2017/trainval70k/
/home/ubuntu/dataset/coco2017/trainval35k/results/
/home/ubuntu/dataset/coco2017/trainval70k/results/
/home/ubuntu/dataset/coco2017/images/test2017/
/home/ubuntu/dataset/coco2017/images/trainval35k/
/home/ubuntu/dataset/coco2017/images/trainval70k/
/home/ubuntu/dataset/coco2017/test-images/test100000.jpg
## voc:
### voc2005:
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/VOCtrainval/VOCdevkit/VOC2005/VOCtrainval/VOCdevkit/VOC2005/JPEGImages/
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/ImageSets/Main/
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/ImageSets/Main/aeroplane_test.txt
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/ImageSets/Main/bicycle_test.txt
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/ImageSets/Main/building_test.txt
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/ImageSets/Main/car_test.txt
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/ImageSets/Main/motorbike_test.txt
/media/yilunxiao/DATA/Datasets/VOCdevkit/VOC2005/ImageSets/Main/person_test.txt
### voc07:
/media/yilunxiao/Data01/TensorFlow/models/research/object_detection/data/pascal_voc/voc0712testset.zip
### voc12:
/data01/Data/TensorFlow/models/research/object_detection/data/pascal_voc/vocab12testset.zip
## yolo:
### yolo-voc07-test-image-set:
http://vision.stanford.edu/aditya86/ImageNetDogs/images.tar.gz
http://host.robots.ox.ac.uk/pascal-VOD/ILOVETOFLY_data/ILOVETOFLY_images.tar.gz
## person detection dataset:
https://www.kaggle.com/sparshmathur/person-detection-dataset/download
https://drive.google.com/drive/folders/19oVwumUEoAivtVqQGJsjMIdEJbUASQkS?usp=sharing
## people detection datasets:
https://people.cs.umass.edu/~marlin/pubdata.html
http://www.vision.caltech.edu/html-files/archive.htm
## people detection datasets:
http://vis-www.cs.umass.edu/MPII/index.html
## people detection datasets:
http://vis-www.cs.umass.edu/MPII/Human_Detection.htm
## pedestrian detection datasets:
http://vision.in.tum.de/tvmvl/projects/hdd/
http://vision.in.tum.de/tvmvl/projects/hdd/hdd-data.html
## pedestrian detection datasets:
http://www.cvlibs.net/download.php?file=crowds_train.zip&hp=http%253A%252F%252Fwww.cvlibs.net%252Fdatasets%252Fkitti%252F
## pedestrian detection datasets:
http://ai.stanford.edu/~jkrause/activitynet/video_data/videodata.tar.gz
https://people.eecs.berkeley.edu/~tinghuiz/projects/CULane-release.html
## pedestrian detection datasets:
https://www.cv-foundation.org/openaccess/content_cvpr_workshops/CameraReady/W16/w16_paper09.pdf
## pedestrian detection datasets:
http://host.robots.ox.ac.uk/pascal-VOD/ILOVETOFLY_data/ILOVETOFLY_images.tar.gz
## pedestrian detection datasets:
http:///data01/Data/TensorFlow/models/research/object_detection/data/kitti/kitti_object.zip
## pedestrian detection datasets:
http:///data01/Data/TensorFlow/models/research/object_detection/data/ms-coco/mscoco.zip
## pedestrian detection datasets:
http:///data01/Data/TensorFlow/models/research/object_detection/data/ped_crosswalk_peds.zip
## pedestrian detection datasets:
http:///data01/Data/TensorFlow/models/research/object_detection/data/street_pedestrain/street_pedestrain.zip
<|repo_name|>yinlunxiao/PPD<|file_sep>
import numpy as np
import os
import sys
import cv as cv_lib
import matplotlib.pyplot as plt
from PIL import Image
import time
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
import argparse
from google.protobuf import text_format
def get_arguments():
parser = argparse.ArgumentParser(description='Object Detection')
parser.add_argument('--model', dest='model', type=str,default='./ssdlite_mobilenet_v2_coco/ssdlite_mobilenet_v2_coco.config',help='path/to/model')
parser.add_argument('--image', dest='image', type=str,default='./ssdlite_mobilenet_v2_coco/image.jpg',help='path/to/image')
parser.add_argument('--output', dest='output', type=str,default='./ssdlite_mobilenet_v2_coco/output.jpg',help='path/to/output')
args,_= parser.parse_known_args()
return args
def load_model(model):
config=model_pbtxt=model_pbtxt_from_string(open(model).read())
return config,model_pbtxt
def detect_objects(image_np,model):
detections=model.detect_with_image(image_np)
return detections
def main():
args=get_arguments()
config,model_pbtxt=load_model(args.model)
model=tf.keras.models.load_model(args.model)
image_np=cv_lib.imread(args.image)
detections=detect_objects(image_np,model)
output_image_path=args.output
if not os.path.exists(os.path.dirname(output_image_path)):
os.makedirs(os.path.dirname(output_image_path))
for idx,detection in enumerate(detections):
print('object:',idx,'confidence:',np.max(detection['scores']))
image_np_with_detected_objects=np.copy(image_np)
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detected_objects,
detection['boxes'],
detection['classes'],
detection['scores'],
label_map_util.get_category_index(),
use_normalized_coordinates=True,
line_thickness=8)
plt.figure(figsize=(12.,8.),dpi=100)
plt.imshow(cv_lib.cvtColor(image_np_with_detected_objects,cv_lib.COLOR_BGR_TO_RGB))
plt.savefig(output_image_path)
if __name__=='__main__':
main()
class DetectionModel():
def __init__(self):
self.model=None;
self.config=None;
self.label_map=None;
def load(self,model):
self.model=tf.keras.models.load_model(model);
self.config,self.label_map=self._load_labelmap(self.config);
return self.model,self.config,self.label_map;
def _load_labelmap(self,labelmap):
with open(labelmap,'r')as f:
labelmap_proto=text_format.Parse(f.read(),label_pb.LabelMap());
return label_pb_to_dict(labelmap_proto),labelpb_to_dict(labelmap_proto);
def detect(self,image):
image=image.astype(np.float32);
image=np.expand_dims(image,axis=0);
scores,self.detections=self.model.predict(image)[0];
max_score=np.max(scores);
class_id=np.argmax(scores);
def visualize(self,image,detections,scores,class_id,max_score):
image=image.astype(np.uint8);
output_image=image.copy();
viz_utils.visualize_boxes_and_labels_on_image_array(
output_image,
detections,
class_id,
scores,
self.label_map,
min_score_thresh=.45,
use_normalized_coordinates=True,
line_thickness=8);
def save_output(self,output_image,output_image_path):
if not os.path.exists(os.path.dirname(output_image_path)):
os.makedirs(os.path.dirname(output_image_path));
plt.figure(figsize=(12.,8.),dpi=100);
plt.imshow(cv_lib.cvtColor(output_image,cv_lib.COLOR_BGR_TO_RGB));
plt.savefig(output_image_path);
***** Tag Data *****
ID: 4
description: The `detect` method within `DetectionModel` class processes image data
start line: xx
end line: xx
dependencies:
- type: Class
name: DetectionModel()
start line: xx
end line: xx
context description: This method preprocesses input image data into tensor format,
algorithmic depth: '4'
algorithmic depth external: N
obscurity: '4'
advanced coding concepts: '4'
interesting for students: '5'
self contained: Y
*************
## Suggestions for complexity
Here are five advanced modifications or expansions that could be made specifically relevant to preprocessing image data into tensor format:
1. **Dynamic Preprocessing Pipeline**: Implement a dynamic preprocessing pipeline that adjusts based on metadata associated with each image such as resolution or color space.
2. **Batch Processing**: Modify the method to handle batch processing efficiently by leveraging parallel computing techniques like multi-threading or GPU acceleration.
3. **Augmentation Integration**: Integrate advanced image augmentation techniques directly into preprocessing which could include random cropping, rotation, flipping etc., controlled via configuration parameters.
4. **Custom Tensor Normalization**: Implement custom normalization techniques that go beyond simple min-max scaling or z-score normalization tailored specifically for different types of neural network models.
5. **Error Handling & Logging**: Add sophisticated error handling mechanisms along with detailed logging capabilities that capture preprocessing anomalies such as corrupted images or unsupported formats.
---
## Conversation
<|user|>: I'm working on this project where I need to preprocess input image data into tensor format efficiently but dynamically depending upon metadata associated with each image like resolution or color space [SNIPPET]
Could you help me modify my existing code so it can adjust its preprocessing steps based on these metadata?
<|end|>
<|assistant|>: Absolutely! To make your preprocessing pipeline dynamic based on metadata like resolution or color space, you can introduce conditional logic within your method.
Here’s how you might start:
python
class ImagePreprocessor:
def preprocess(self, image_metadata):
# Extract metadata information
resolution = image_metadata.get('resolution')
color_space = image_metadata.get('color_space')
# Convert color space if necessary
if color_space == 'RGB':
pass # Assuming original is RGB already
elif color_space == 'GRAYSCALE':
image_tensor = self.convert_to_grayscale(image_tensor)
elif color_space == 'HSV':
image_tensor = self.convert_to_hsv(image_tensor)
# Adjust resolution dynamically here...
target_resolution = self.determine_target_resolution(resolution)
image_tensor_resized = self.resize_tensor(image_tensor, target_resolution)
# Convert processed tensor into desired format...
final_tensor_format = self.convert_to_final_format(image_tensor_resized)
return final_tensor_format
def convert_to_grayscale(self, tensor):
# Conversion logic here...
pass
def convert_to_hsv(self, tensor):
# Conversion logic here...
pass
def determine_target_resolution(self,resolution):
# Logic based on metadata...
pass
def resize_tensor(self,tensor,target_resolution):
# Resize logic here...
pass
def convert_to_final_format(self,tensor):
# Final conversion logic here...
This way your preprocessing pipeline dynamically adapts based on provided metadata.
What do you think about this approach?
nterface.md)
##### [Process](process.md)
##### [Control](control.md)
##### [Window](window.md)
#### Text Boxes And Controls That Take Input From Users
##### [TextBox](textbox-controls/textbox.md)
##### [RichTextBox](textbox-controls/richTextBox.md)
##### [MaskedTextBox](textbox-controls/maskedtextbox.md)
##### [ComboBox](textbox-controls/comboBox.md)
##### [ListView](listview-control/listview.md)
#### Buttons And Other Controls That Perform Actions
##### [Button](buttons-and-other-controls-that-perform-actions/button-control/button-control-overview-windows-forms.md)
##### [CheckBox](buttons-and-other-controls-that-perform-actions/check-box-control-windows-forms.md)
##### [RadioButton](buttons-and-other-controls-that-perform-actions/radio-button-control-windows-forms.md)
##### [LinkLabel](buttons-and-other-controls-that-perform-actions/linklabel-control-windows-forms.md)
#### Progress And Status Indicators
##### [ProgressBar](progress-and-status-indicators/progressbar-control/windows-forms-progressbar-control-overview.md)
##### [StatusBar](progress-and-status-indicators/statusbar-control/statusbar-control-overview-windows-forms.md)
#### Web Browser And Navigation Controls
##### [WebBrowser Control Overview Windows Forms](web-browser-and-navigation-controls/webbrowser-control-overview-windows-forms.md)
#### Data Entry And Display Controls
##### [DataGridView Control Windows Forms Overview](data-entry-and-display-controls/datagridview-control-overview-windows-forms.md)
###### [[Column Styles]](/dotnet/framework/winforms/datagridview/how-to-format-a-column-in-the-windows-forms-datagridview-control)
###### [[Formatting Cells]](/dotnet/framework/winforms/datagridview/how-to-format-cells-in-the-windows-forms-datagridview-control)
###### [[Adding Tooltips]](/dotnet/framework/winforms/datagridview/how-to-add-tooltips-to-the-windows-forms-datagridview)
###### [[Cell Types]](/dotnet/framework/winforms/datagridview/customizing-the-appearance-of-cells-in-the-datagrid-using-the-designer)
###### [[Data Types]](/dotnet/framework/winforms/datagridview/customizing-cell-types-with-the-designer)
###### [[Sorting Columns]](/dotnet/framework/winforms/datatable-sorting-data-in-a-wf-datagrid-by-using-columns)
###### [[Filtering Rows]](/dotnet/framework/winforms/filtering-data-in-a-wf-datagrid-using-the-designer)
###### [[Grouping Rows]](/dotnet/framework/winforms/grouping-data-in-a-wf-datagrid-using-the-designer)
#### Data Navigation And Presentation Controls
##### [[BindingNavigator Control Overview Windows Forms]](/dotnet/framework/winforms/windows-forms-data-entry-bindingsnavigator-and-bindingnavigator)
#### Miscellaneous Controls
##### [[ImageList Control Windows Forms Overview]](/dotnet/framework/winforms/image-list-component-overview-windows-forms)
[[ToolTip Control Windows Forms Overview]](/dotnet/framework/winforms/toolstrip-toolstripcontainer-toolstriptextbox-and-tooltip-controls/toolstrip-tooltip-component-overview)
### Layout Controls
[[Layout Guides For User Interfaces In WPF Applications]](layout-guidelines-for-user-interfaces-in-wpf-applications.md)
#### FlowLayoutPanel Control
[[Windows Forms FlowLayoutPanel Control Overview]](flowlayoutpanel-control-overviews/index.md)
#### TableLayoutPanel Control
[[Windows Forms TableLayoutPanel Control Overview]](tablelayoutpanel/index.md)
#### SplitContainer Control
[[Split Container Control Windows Forms Overview]](splitcontainer/control-splitcontainer-overview-windows-forms)**[^][]***
**[^][]:** _See also:_ **[`TableLayoutPanel` control overview windows forms ](tablelayoutpanel/index)**
**[^]:** _See also:_ **[`Split Container` control windows forms overview ](splitcontainer/control-splitcontainer-overview-windows-forms)**
### Menus And Toolbars
#### MenuStrip Control
[[MenuStrip Control Windows Forms Overview]](menustrip/control-menustrip-overview-windows-forms)**[^][]***
**[^][]:** _See also:_ **[`ContextMenuStrip` control windows forms overview ](contextmenustrip/control-contextmenustrip-overview-windows-forms)**
**[^]:** _See also:_ **[`ToolStripDropDownMenu` control windows forms overview ](toolstrippopupmenu/control-toolstrippopupmenu-overview)**
#### ContextMenuStrip Control
[[ContextMenuStrip Control Windows Forms Overview]](contextmenustrip/control-contextmenustrip-overview-windows-forms)**[^][]***
**[^][]:** _See also:_ **[`MenuStrip` control windows forms overview ](menustrip/control-menustrip-overview-windows-forms)**
**[^]:** _See also:_ **[`ToolStripDropDownMenu` control windows forms overview ](toolstrippopupmenu/control-toolstrippopupmenu-overviews)**
#### ToolStripDropDownMenu Control
[[ToolStripDropDownMenu Control Windows Forms Overviews]](toolstrippopupmenu/control-toolstrippopupmenu-overviews)**[^][]***
**[^][]:** _See also:_ **[`MenuStrip` control windows forms overview ](menustrip/control-menustrip-overviews)**
**[^]:** _See also:_ **[`ContextMenuStrip` control windows forms overview ](contextmenustrip/control-contextmenustrip-overviews)**
### Toolbars And Task Panes
#### ToolStripControlAnd ToolStripPanelControlOverviewWindowsFormsOverviews
[[ToolStripControlAnd ToolStripPanelControlOverviewWindowsFormsOverviews ]]([TOOLSTRIP CONTROL AND TOOLSTRIP PANEL OVERVIEW WINDOWS FORMS OVERVIEW)](#TOOLSTRIP CONTROL AND TOOLSTRIP PANEL OVERVIEW WINDOWS FORMS OVERVIEW))
TOOLSTRIP CONTROL AND TOOLSTRIP PANEL OVERVIEW WINDOWS FORMS OVERVIEW:
The ToolStrip control allows developers to create toolbars that contain buttons along with menus derived from older controls such as MenuStrip controls or ContextMenu controls.
The ToolStripPanel control allows developers add toolbars created using ToolStrip controls at run time.
Both controls support themes provided by Microsoft Visual Studio.NET.
In addition both controls allow developers add items that change depending upon user actions.
For example when a user selects an item from a menu strip located within a ToolStrip container then any items within another menu strip located elsewhere may change accordingly.
The following example demonstrates how these two types work together:
Example:
Add two MenuStrip controls named menuStripOneAndTwo respectively onto formOne.
Add one button named buttonOne onto formOne.
Add one TextBox named textBoxOne onto formTwo.<
