Update config.py

config.py

@@ -1,205 +1,205 @@
-"""
-Vehicle Detection Configuration Module
-=======================================
-
-Manages configuration settings for vehicle detection, tracking, and speed estimation.
-
-Authors:
-    - Abhay Gupta (0205CC221005)
-    - Aditi Lakhera (0205CC221011)
-    - Balraj Patel (0205CC221049)
-    - Bhumika Patel (0205CC221050)
-"""
-
-import os
-from dataclasses import dataclass, field
-from typing import List, Tuple, Optional
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class VehicleDetectionConfig:
-    """
-    Configuration class for vehicle detection and speed estimation system.
-    
-    This class encapsulates all configuration parameters needed for the
-    vehicle detection pipeline, including video paths, model settings,
-    detection zones, and perspective transformation parameters.
-    """
-    
-    # Video Configuration
-    input_video: str = "./data/vehicles.mp4"
-    output_video: str = "./data/vehicles_output.mp4"
-    
-    # Model Configuration
-    model_name: str = "yolov8n"
-    model_path: Optional[str] = None
-    confidence_threshold: float = 0.3
-    iou_threshold: float = 0.7
-    
-    # Detection Zone Configuration
-    line_y: int = 480
-    line_offset: int = 55
-    crossing_threshold: int = 1
-    
-    # Perspective Transformation Configuration
-    # Source points define the region in the original video frame
-    source_points: List[List[int]] = field(default_factory=lambda: [
-        [450, 300],   # Top-left
-        [860, 300],   # Top-right
-        [1900, 720],  # Bottom-right
-        [-660, 720]   # Bottom-left
-    ])
-    
-    # Target points define the transformed top-down view dimensions (in meters)
-    target_width_meters: float = 25.0
-    target_height_meters: float = 100.0
-    
-    # Display Configuration
-    window_name: str = "Vehicle Speed Estimation - Traffic Analysis"
-    display_enabled: bool = True
-    
-    # Annotation Configuration
-    enable_boxes: bool = True
-    enable_labels: bool = True
-    enable_traces: bool = True
-    enable_line_zones: bool = True
-    trace_length: int = 20
-    
-    # Speed Estimation Configuration
-    speed_history_seconds: int = 1
-    speed_unit: str = "km/h"  # Options: "km/h", "mph", "m/s"
-    
-    def __post_init__(self):
-        """Validate configuration after initialization."""
-        self._validate_config()
-        self._setup_model_path()
-    
-    def _validate_config(self) -> None:
-        """
-        Validate configuration parameters.
-        
-        Raises:
-            ValueError: If configuration parameters are invalid
-        """
-        # Validate video paths
-        if not self.input_video:
-            raise ValueError("Input video path cannot be empty")
-        
-        # Validate model configuration
-        if not 0.0 <= self.confidence_threshold <= 1.0:
-            raise ValueError(f"Confidence threshold must be between 0 and 1, got {self.confidence_threshold}")
-        
-        if not 0.0 <= self.iou_threshold <= 1.0:
-            raise ValueError(f"IOU threshold must be between 0 and 1, got {self.iou_threshold}")
-        
-        # Validate detection zone
-        if self.line_y < 0:
-            raise ValueError(f"Line Y position must be positive, got {self.line_y}")
-        
-        if self.line_offset < 0:
-            raise ValueError(f"Line offset must be positive, got {self.line_offset}")
-        
-        # Validate perspective transformation
-        if len(self.source_points) != 4:
-            raise ValueError(f"Source points must contain exactly 4 points, got {len(self.source_points)}")
-        
-        for i, point in enumerate(self.source_points):
-            if len(point) != 2:
-                raise ValueError(f"Source point {i} must have 2 coordinates, got {len(point)}")
-        
-        if self.target_width_meters <= 0 or self.target_height_meters <= 0:
-            raise ValueError("Target dimensions must be positive")
-        
-        # Validate speed configuration
-        if self.speed_unit not in ["km/h", "mph", "m/s"]:
-            raise ValueError(f"Invalid speed unit: {self.speed_unit}. Must be 'km/h', 'mph', or 'm/s'")
-        
-        logger.info("Configuration validation successful")
-    
-    def _setup_model_path(self) -> None:
-        """Set up the model path based on model name."""
-        if self.model_path is None:
-            # Try to find model in models directory
-            model_dir = "./models"
-            potential_paths = [
-                f"{model_dir}/{self.model_name}.pt",
-                f"{model_dir}/VisDrone_YOLO_x2.pt",  # Custom trained model
-                self.model_name  # Let ultralytics download from hub
-            ]
-            
-            for path in potential_paths:
-                if os.path.exists(path):
-                    self.model_path = path
-                    logger.info(f"Using model from: {path}")
-                    return
-            
-            # Use model name directly (will be downloaded by ultralytics)
-            self.model_path = self.model_name
-            logger.info(f"Model will be downloaded: {self.model_name}")
-    
-    @property
-    def target_points(self) -> List[List[float]]:
-        """
-        Generate target points for perspective transformation.
-        
-        Returns:
-            List of 4 points defining the target perspective in meters
-        """
-        w, h = self.target_width_meters, self.target_height_meters
-        return [
-            [0, 0],      # Top-left
-            [w, 0],      # Top-right
-            [w, h],      # Bottom-right
-            [0, h]       # Bottom-left
-        ]
-    
-    def get_speed_conversion_factor(self) -> float:
-        """
-        Get conversion factor for speed unit.
-        
-        Returns:
-            Conversion factor from m/s to desired unit
-        """
-        conversions = {
-            "km/h": 3.6,
-            "mph": 2.23694,
-            "m/s": 1.0
-        }
-        return conversions[self.speed_unit]
-    
-    def to_dict(self) -> dict:
-        """
-        Convert configuration to dictionary.
-        
-        Returns:
-            Dictionary representation of configuration
-        """
-        return {
-            "input_video": self.input_video,
-            "output_video": self.output_video,
-            "model_name": self.model_name,
-            "model_path": self.model_path,
-            "confidence_threshold": self.confidence_threshold,
-            "line_y": self.line_y,
-            "speed_unit": self.speed_unit,
-        }
-    
-    def __repr__(self) -> str:
-        """String representation of configuration."""
-        return f"VehicleDetectionConfig(model={self.model_name}, input={self.input_video})"
-
-
-# Default configuration instance for backward compatibility
-DEFAULT_CONFIG = VehicleDetectionConfig()
-
-# Export commonly used configuration values
-IN_VIDEO_PATH = DEFAULT_CONFIG.input_video
-OUT_VIDEO_PATH = DEFAULT_CONFIG.output_video
-YOLO_MODEL_PATH = DEFAULT_CONFIG.model_path
-LINE_Y = DEFAULT_CONFIG.line_y
-SOURCE_POINTS = DEFAULT_CONFIG.source_points
-TARGET_POINTS = DEFAULT_CONFIG.target_points
-WINDOW_NAME = DEFAULT_CONFIG.window_name
+"""
+Vehicle Detection Configuration Module
+=======================================
+
+Manages configuration settings for vehicle detection, tracking, and speed estimation.
+
+Authors:
+    - Abhay Gupta (0205CC221005)
+    - Aditi Lakhera (0205CC221011)
+    - Balraj Patel (0205CC221049)
+    - Bhumika Patel (0205CC221050)
+"""
+
+import os
+from dataclasses import dataclass, field
+from typing import List, Tuple, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class VehicleDetectionConfig:
+    """
+    Configuration class for vehicle detection and speed estimation system.
+    
+    This class encapsulates all configuration parameters needed for the
+    vehicle detection pipeline, including video paths, model settings,
+    detection zones, and perspective transformation parameters.
+    """
+    
+    # Video Configuration
+    input_video: str = "./data/vehicles.mp4"
+    output_video: str = "./data/vehicles_output.mp4"
+    
+    # Model Configuration
+    model_name: str = "yolov8n"
+    model_path: Optional[str] = None
+    confidence_threshold: float = 0.3
+    iou_threshold: float = 0.7
+    
+    # Detection Zone Configuration
+    line_y: int = 480
+    line_offset: int = 55
+    crossing_threshold: int = 1
+    
+    # Perspective Transformation Configuration
+    # Source points define the region in the original video frame
+    source_points: List[List[int]] = field(default_factory=lambda: [
+        [450, 300],   # Top-left
+        [860, 300],   # Top-right
+        [1900, 720],  # Bottom-right
+        [-660, 720]   # Bottom-left
+    ])
+    
+    # Target points define the transformed top-down view dimensions (in meters)
+    target_width_meters: float = 25.0
+    target_height_meters: float = 100.0
+    
+    # Display Configuration (disabled by default for headless environments like HF Spaces)
+    window_name: str = "Vehicle Speed Estimation - Traffic Analysis"
+    display_enabled: bool = False
+    
+    # Annotation Configuration
+    enable_boxes: bool = True
+    enable_labels: bool = True
+    enable_traces: bool = True
+    enable_line_zones: bool = True
+    trace_length: int = 20
+    
+    # Speed Estimation Configuration
+    speed_history_seconds: int = 1
+    speed_unit: str = "km/h"  # Options: "km/h", "mph", "m/s"
+    
+    def __post_init__(self):
+        """Validate configuration after initialization."""
+        self._validate_config()
+        self._setup_model_path()
+    
+    def _validate_config(self) -> None:
+        """
+        Validate configuration parameters.
+        
+        Raises:
+            ValueError: If configuration parameters are invalid
+        """
+        # Validate video paths
+        if not self.input_video:
+            raise ValueError("Input video path cannot be empty")
+        
+        # Validate model configuration
+        if not 0.0 <= self.confidence_threshold <= 1.0:
+            raise ValueError(f"Confidence threshold must be between 0 and 1, got {self.confidence_threshold}")
+        
+        if not 0.0 <= self.iou_threshold <= 1.0:
+            raise ValueError(f"IOU threshold must be between 0 and 1, got {self.iou_threshold}")
+        
+        # Validate detection zone
+        if self.line_y < 0:
+            raise ValueError(f"Line Y position must be positive, got {self.line_y}")
+        
+        if self.line_offset < 0:
+            raise ValueError(f"Line offset must be positive, got {self.line_offset}")
+        
+        # Validate perspective transformation
+        if len(self.source_points) != 4:
+            raise ValueError(f"Source points must contain exactly 4 points, got {len(self.source_points)}")
+        
+        for i, point in enumerate(self.source_points):
+            if len(point) != 2:
+                raise ValueError(f"Source point {i} must have 2 coordinates, got {len(point)}")
+        
+        if self.target_width_meters <= 0 or self.target_height_meters <= 0:
+            raise ValueError("Target dimensions must be positive")
+        
+        # Validate speed configuration
+        if self.speed_unit not in ["km/h", "mph", "m/s"]:
+            raise ValueError(f"Invalid speed unit: {self.speed_unit}. Must be 'km/h', 'mph', or 'm/s'")
+        
+        logger.info("Configuration validation successful")
+    
+    def _setup_model_path(self) -> None:
+        """Set up the model path based on model name."""
+        if self.model_path is None:
+            # Try to find model in models directory
+            model_dir = "./models"
+            potential_paths = [
+                f"{model_dir}/{self.model_name}.pt",
+                f"{model_dir}/VisDrone_YOLO_x2.pt",  # Custom trained model
+                self.model_name  # Let ultralytics download from hub
+            ]
+            
+            for path in potential_paths:
+                if os.path.exists(path):
+                    self.model_path = path
+                    logger.info(f"Using model from: {path}")
+                    return
+            
+            # Use model name directly (will be downloaded by ultralytics)
+            self.model_path = self.model_name
+            logger.info(f"Model will be downloaded: {self.model_name}")
+    
+    @property
+    def target_points(self) -> List[List[float]]:
+        """
+        Generate target points for perspective transformation.
+        
+        Returns:
+            List of 4 points defining the target perspective in meters
+        """
+        w, h = self.target_width_meters, self.target_height_meters
+        return [
+            [0, 0],      # Top-left
+            [w, 0],      # Top-right
+            [w, h],      # Bottom-right
+            [0, h]       # Bottom-left
+        ]
+    
+    def get_speed_conversion_factor(self) -> float:
+        """
+        Get conversion factor for speed unit.
+        
+        Returns:
+            Conversion factor from m/s to desired unit
+        """
+        conversions = {
+            "km/h": 3.6,
+            "mph": 2.23694,
+            "m/s": 1.0
+        }
+        return conversions[self.speed_unit]
+    
+    def to_dict(self) -> dict:
+        """
+        Convert configuration to dictionary.
+        
+        Returns:
+            Dictionary representation of configuration
+        """
+        return {
+            "input_video": self.input_video,
+            "output_video": self.output_video,
+            "model_name": self.model_name,
+            "model_path": self.model_path,
+            "confidence_threshold": self.confidence_threshold,
+            "line_y": self.line_y,
+            "speed_unit": self.speed_unit,
+        }
+    
+    def __repr__(self) -> str:
+        """String representation of configuration."""
+        return f"VehicleDetectionConfig(model={self.model_name}, input={self.input_video})"
+
+
+# Default configuration instance for backward compatibility
+DEFAULT_CONFIG = VehicleDetectionConfig()
+
+# Export commonly used configuration values
+IN_VIDEO_PATH = DEFAULT_CONFIG.input_video
+OUT_VIDEO_PATH = DEFAULT_CONFIG.output_video
+YOLO_MODEL_PATH = DEFAULT_CONFIG.model_path
+LINE_Y = DEFAULT_CONFIG.line_y
+SOURCE_POINTS = DEFAULT_CONFIG.source_points
+TARGET_POINTS = DEFAULT_CONFIG.target_points
+WINDOW_NAME = DEFAULT_CONFIG.window_name