Create README.md

README.md

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+---
+task_categories:
+- object-detection
+language:
+- en
+tags:
+- soccer
+- football
+- pitch
+- ground
+- keypoints
+- camera
+- calibration
+- homography
+pretty_name: Soccana_keyp_v1
+size_categories:
+- 1K<n<10K
+---
+
+# SoccerNet Keypoints Dataset
+
+---
+
+## Introduction
+
+The SoccerNet Keypoints dataset is a comprehensive computer vision dataset for soccer field keypoint detection and pitch object localization. This dataset is derived from the SoccerNet calibration dataset and provides precise field keypoints extracted from line endpoints, enabling accurate field analysis, camera calibration, and tactical analysis in soccer videos.
+
+In order to download the dataset, download Soccernet Calibration Dataset as given in [Soccernet Official Documentation](https://www.soccer-net.org/data). This repository contains keypoints and Object labels for the Soccernet data. (Soccernet by default provides edges.)
+
+The dataset combines computer vision techniques including line intersection calculations, green area detection for pitch objects, and comprehensive keypoint extraction to create a robust dataset for Football Field Keypoint Detection
+
+## Dataset Details
+
+### Overview
+- **Source**: SoccerNet Calibration Dataset
+- **Task**: Keypoint Detection and Pitch Object Detection  
+- **Total Classes**: 1 (Pitch Object)
+- **Total Keypoints**: 29 field keypoints per image
+- **Coordinate System**: Normalized coordinates (0-1 range)
+- **Annotation Format**: JSON + YOLO format
+- **Dataset Splits**: Train, Validation, Test
+
+### Classes and Objects
+
+#### Object Detection Classes
+- **Class 0: Pitch Object**
+  - Complete green field area is termed at pitch object
+  - Bounding box encompasses entire visible pitch
+  - Normalized coordinates with center_x, center_y, width, height format
+
+#### Keypoint Classes (29 Field Keypoints)
+The dataset provides 29 precisely calculated field keypoints covering:
+
+1. **Field Boundaries (4 points)**
+   - `0_sideline_top_left`
+   - `9_sideline_bottom_left` 
+   - `16_sideline_top_right`
+   - `25_sideline_bottom_right`
+
+2. **Penalty Areas - Big Box (8 points)**
+   - Left side: `1_big_rect_left_top_pt1`, `2_big_rect_left_top_pt2`, `3_big_rect_left_bottom_pt1`, `4_big_rect_left_bottom_pt2`
+   - Right side: `17_big_rect_right_top_pt1`, `18_big_rect_right_top_pt2`, `19_big_rect_right_bottom_pt1`, `20_big_rect_right_bottom_pt2`
+
+3. **Goal Areas - Small Box (8 points)**
+   - Left side: `5_small_rect_left_top_pt1`, `6_small_rect_left_top_pt2`, `7_small_rect_left_bottom_pt1`, `8_small_rect_left_bottom_pt2`
+   - Right side: `21_small_rect_right_top_pt1`, `22_small_rect_right_top_pt2`, `23_small_rect_right_bottom_pt1`, `24_small_rect_right_bottom_pt2`
+
+4. **Center Line and Circle (6 points)**
+   - `11_center_line_top`
+   - `12_center_line_bottom`
+   - `13_center_circle_top`
+   - `14_center_circle_bottom`
+   - `15_field_center`
+   - `27_center_circle_left`, `28_center_circle_right`
+
+5. **Semicircles (2 points)**
+   - `10_left_semicircle_right`
+   - `26_right_semicircle_left`
+
+---
+
+## Dataset Preparation
+
+### Download Process
+View [downloader.py](https://github.com/Adit-jain/Soccer_Analysis/blob/main/Data_utils/SoccerNet_Keypoints/downloader.py) for reference on how to download Soccernet Data, or can refer official documentation at [Soccernet](https://www.soccer-net.org/data)
+
+### Processing Pipeline
+
+The dataset preparation follows the following steps:
+
+#### Line-to-Keypoint Conversion (`line_intersections.py`)
+- **Class**: `LineIntersectionCalculator`
+- **Input**: SoccerNet JSON files with line endpoints
+- **Process**: Calculate 29 field keypoints from line intersections using geometric algorithms
+- **Key Methods**:
+  - `line_intersection()`: Calculate intersection points between two lines
+  - `calculate_field_keypoints()`: Generate all 29 keypoints from line data
+  - `point_to_line_distance()`: Calculate perpendicular distances for circle keypoints
+
+#### Pitch Object Detection (`get_pitch_object.py`)
+- **Class**: `PitchDetector`
+- **Process**: Detect complete green field area using HSV color segmentation
+- **Key Methods**:
+  - `detect_green_area()`: HSV-based green area detection with morphological operations
+  - `find_largest_contour()`: Identify the largest contour as the pitch
+  - `get_pitch_bounding_box()`: Calculate normalized bounding box from contour
+
+#### Unified Processing (`process_images.py`)
+- **Function**: `process_unified_soccernet_dataset()`
+- **Output Formats**:
+  - **JSON annotations**: Complete metadata with keypoints and pitch objects
+  - **YOLO labels**: Ultralytics-compatible format for training
+  - **Visualization images**: Annotated images showing detections
+
+#### Dataset Configuration (`create_dataset_yaml.py`)
+- Generate `dataset.yaml` for Ultralytics YOLO training
+- Configure keypoint connections for visualization
+- Set up dataset paths and class definitions
+
+**[Github Repo](https://github.com/Adit-jain/Soccer_Analysis/tree/main/Data_utils/SoccerNet_Keypoints)**
+
+---
+
+## Dataset Format
+
+### Directory Structure
+```
+unified_output/
+├── annotations_json/           # Complete JSON annotations
+│   ├── train/
+│   ├── valid/
+│   └── test/
+├── processed_images/           # Visualization images
+│   ├── train/
+│   ├── valid/
+│   └── test/
+├── yolo_labels/               # Ultralytics YOLO format
+│   ├── train/
+│   ├── valid/
+│   └── test/
+├── dataset.yaml               # YOLO configuration
+└── README.md                  # Usage instructions
+```
+
+### Annotation Formats
+
+#### JSON Format (Complete Annotations)
+```json
+{
+  "image_info": {
+    "file_name": "image.jpg",
+    "path": "/path/to/image.jpg", 
+    "width": 1920,
+    "height": 1080
+  },
+  "pitch_object": {
+    "class_id": 0,
+    "class_name": "pitch",
+    "center_x": 0.5,
+    "center_y": 0.5,
+    "width": 0.8,
+    "height": 0.6,
+    "x_min": 0.1,
+    "y_min": 0.2,
+    "x_max": 0.9,
+    "y_max": 0.8,
+    "area": 0.48,
+    "contour_area": 0.45
+  },
+  "keypoints": {
+    "0_sideline_top_left": [0.1, 0.2],
+    "1_big_rect_left_top_pt1": [0.15, 0.25],
+    ...
+  },
+  "original_lines": {
+    "Side line top": [{"x": 0.1, "y": 0.2}, {"x": 0.9, "y": 0.2}],
+    ...
+  },
+  "dataset_split": "train",
+  "total_keypoints": 29,
+  "annotation_format": "SoccerNet_unified_v1"
+}
+```
+
+#### YOLO Format (Ultralytics Compatible)
+```
+0 0.500000 0.500000 0.800000 0.600000 0.100000 0.200000 2 0.150000 0.250000 2 ... (29 keypoints with visibility)
+```
+
+Format: `<class-index> <center_x> <center_y> <width> <height> <kp1_x> <kp1_y> <kp1_visibility> <kp2_x> <kp2_y> <kp2_visibility> ...`
+
+### Keypoint Visibility
+- **2**: Visible keypoint (calculated successfully)
+- **0**: Not visible/not detected (coordinates set to 0.0, 0.0)
+
+### Coordinate System
+- All coordinates normalized to [0, 1] range
+- Origin (0,0) at top-left corner of image
+- X-axis increases rightward, Y-axis increases downward
+
+---
+
+## Usage Examples
+
+### Training with Ultralytics YOLO
+```python
+from ultralytics import YOLO
+
+# Load pre-trained pose model
+model = YOLO('yolov8n-pose.pt')
+
+# Train on SoccerNet Keypoints
+results = model.train(
+    data='dataset.yaml',
+    epochs=100,
+    imgsz=640,
+    batch=16,
+    name='soccernet_keypoints'
+)
+```
+
+### Custom Processing
+```python
+from get_pitch_object import PitchDetector
+from line_intersections import LineIntersectionCalculator
+
+# Initialize processors
+pitch_detector = PitchDetector()
+keypoint_calculator = LineIntersectionCalculator()
+
+# Process single image
+pitch_result = pitch_detector.detect_pitch_from_image('image.jpg')
+keypoint_calculator.load_soccernet_data('annotations.json')
+keypoints, lines = keypoint_calculator.calculate_field_keypoints()
+```
+
+### Visualization
+```python
+from process_images import create_unified_visualization
+
+create_unified_visualization(
+    image_path='image.jpg',
+    pitch_data=pitch_result['pitch_detection'],
+    keypoints=keypoints,
+    lines=lines,
+    output_path='annotated_image.jpg'
+)
+```
+
+---
+
+## Technical Implementation
+
+### Core Algorithms
+
+#### Line Intersection Mathematics
+Using parametric line representation:
+- Line 1: `P = P1 + t(P2 - P1)`
+- Line 2: `Q = Q1 + u(Q2 - Q1)`  
+- Intersection calculated using determinant method with parallel line detection
+
+#### HSV Color Segmentation
+- Green detection optimized for grass fields
+- Morphological operations for noise reduction
+- Largest contour selection for pitch identification
+
+#### Keypoint Validation
+- Coordinate boundary checking [0,1]
+- Distance-based point selection for circles
+- Error handling for missing line data
+
+### Performance Optimizations
+- Batch processing with progress tracking
+- Efficient contour operations
+- Optimized intersection calculations
+- Memory-efficient image processing
+
+---
+
+## Repository Structure
+
+**GitHub Repository**: [https://github.com/Adit-jain/Soccer_Analysis/tree/main/Data_utils/SoccerNet_Keypoints](https://github.com/Adit-jain/Soccer_Analysis/tree/main/Data_utils/SoccerNet_Keypoints)
+
+### Module Overview
+- `constants.py`: Dataset configuration and field specifications
+- `downloader.py`: SoccerNet data download utilities  
+- `get_pitch_object.py`: Pitch object detection using color segmentation
+- `line_intersections.py`: Geometric keypoint calculation from line endpoints
+- `process_images.py`: Unified processing pipeline for all formats
+- `create_dataset_yaml.py`: YOLO configuration generation
+- `transfer_json_files.py`: Data organization utilities
+
+### Integration
+This dataset preparation module integrates seamlessly with the main Soccer Analysis project, providing field keypoints for:
+- Camera calibration and homography estimation
+- Tactical analysis and player positioning
+- Field coordinate transformations
+- Real-time field understanding in soccer videos
+
+The dataset serves as a foundation for advanced soccer analysis applications including tactical analysis, player tracking calibration, and automated field understanding systems.