"""
Frame Detection Module
Automatically detects and extracts sprite frames from sprite sheets
"""

import cv2
import numpy as np
from typing import List, Tuple
from scipy import ndimage
from skimage import measure

class FrameDetector:
    """Detector for finding sprite frames in sprite sheets"""
    
    def __init__(self):
        self.min_frame_size = 16  # Minimum frame size in pixels
        self.max_frame_size = 512  # Maximum frame size in pixels
    
    def detect_frames_auto(self, image: np.ndarray, padding: int = 2) -> Tuple[List[np.ndarray], List[Tuple]]:
        """
        Automatically detect frames in a sprite sheet
        
        Args:
            image: Input sprite sheet image
            padding: Padding to add around each frame
        
        Returns:
            Tuple of (list of frame images, list of bounding boxes)
        """
        # Handle alpha channel
        if len(image.shape) == 3 and image.shape[2] == 4:
            # Use alpha channel for detection
            alpha = image[:, :, 3]
            # Create binary mask where alpha > 0
            _, binary = cv2.threshold(alpha, 10, 255, cv2.THRESH_BINARY)
        else:
            # Convert to grayscale and create mask
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            _, binary = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
        
        # Find connected components
        labels = measure.label(binary, connectivity=2)
        regions = measure.regionprops(labels)
        
        # Filter regions by size
        valid_regions = []
        for region in regions:
            minr, minc, maxr, maxc = region.bbox
            width = maxc - minc
            height = maxr - minr
            
            if (self.min_frame_size <= width <= self.max_frame_size and
                self.min_frame_size <= height <= self.max_frame_size):
                valid_regions.append(region)
        
        # If no valid regions found, try grid-based detection
        if len(valid_regions) == 0:
            return self._detect_grid_based(image, padding)
        
        # Sort regions by x-coordinate (left to right)
        valid_regions.sort(key=lambda r: r.bbox[1])
        
        # Extract frames
        frames = []
        frame_boxes = []
        
        for region in valid_regions:
            minr, minc, maxr, maxc = region.bbox
            
            # Add padding
            minr = max(0, minr - padding)
            minc = max(0, minc - padding)
            maxr = min(image.shape[0], maxr + padding)
            maxc = min(image.shape[1], maxc + padding)
            
            # Extract frame
            frame = image[minr:maxr, minc:maxc]
            
            frames.append(frame)
            frame_boxes.append((minr, minc, maxr, maxc))
        
        # If too many small regions, use grid-based approach
        if len(frames) > 20:
            return self._detect_grid_based(image, padding)
        
        return frames, frame_boxes
    
    def _detect_grid_based(self, image: np.ndarray, padding: int = 2) -> Tuple[List[np.ndarray], List[Tuple]]:
        """
        Detect frames using grid-based approach with improved filtering
        
        Args:
            image: Input sprite sheet image
            padding: Padding to add around each frame
        
        Returns:
            Tuple of (list of frame images, list of bounding boxes)
        """
        # Handle alpha channel
        if len(image.shape) == 3 and image.shape[2] == 4:
            alpha = image[:, :, 3]
            _, binary = cv2.threshold(alpha, 10, 255, cv2.THRESH_BINARY)
        else:
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            _, binary = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
        
        # Apply morphological operations to clean up noise
        kernel = np.ones((3, 3), np.uint8)
        binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
        binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
        
        # Find horizontal and vertical projections
        h_proj = np.sum(binary, axis=1)
        v_proj = np.sum(binary, axis=0)
        
        # Find gaps (low values in projection)
        h_threshold = np.max(h_proj) * 0.1
        v_threshold = np.max(v_proj) * 0.1
        
        h_gaps = h_proj < h_threshold
        v_gaps = v_proj < v_threshold
        
        # Find row boundaries
        row_boundaries = []
        in_gap = True
        for i, is_gap in enumerate(h_gaps):
            if in_gap and not is_gap:
                row_boundaries.append(i)
                in_gap = False
            elif not in_gap and is_gap:
                row_boundaries.append(i)
                in_gap = True
        
        # Find column boundaries
        col_boundaries = []
        in_gap = True
        for i, is_gap in enumerate(v_gaps):
            if in_gap and not is_gap:
                col_boundaries.append(i)
                in_gap = False
            elif not in_gap and is_gap:
                col_boundaries.append(i)
                in_gap = True
        
        # Ensure even number of boundaries
        if len(row_boundaries) % 2 != 0:
            row_boundaries.append(binary.shape[0])
        if len(col_boundaries) % 2 != 0:
            col_boundaries.append(binary.shape[1])
        
        # Extract frames
        frames = []
        frame_boxes = []
        
        # If we have valid boundaries
        if len(row_boundaries) >= 2 and len(col_boundaries) >= 2:
            for i in range(0, len(row_boundaries), 2):
                for j in range(0, len(col_boundaries), 2):
                    minr = row_boundaries[i]
                    maxr = row_boundaries[i + 1]
                    minc = col_boundaries[j]
                    maxc = col_boundaries[j + 1]
                    
                    # Add padding
                    minr_p = max(0, minr - padding)
                    minc_p = max(0, minc - padding)
                    maxr_p = min(image.shape[0], maxr + padding)
                    maxc_p = min(image.shape[1], maxc + padding)
                    
                    # Extract frame
                    frame = image[minr_p:maxr_p, minc_p:maxc_p]
                    
                    # Check if frame has content and meets size requirements
                    frame_width = maxc - minc
                    frame_height = maxr - minr
                    
                    # Filter out very small frames (likely effects/particles)
                    min_content_width = max(15, image.shape[1] // 50)  # At least 15px or 2% of image width
                    min_content_height = max(20, image.shape[0] // 3)  # At least 20px or 33% of image height
                    
                    has_content = np.sum(frame) > 0
                    has_valid_size = (frame_width >= min_content_width and 
                                     frame_height >= min_content_height)
                    
                    if has_content and has_valid_size:
                        frames.append(frame)
                        frame_boxes.append((minr_p, minc_p, maxr_p, maxc_p))
        
        # If still no frames, use equal division
        if len(frames) == 0:
            return self._detect_equal_division(image, padding)
        
        return frames, frame_boxes
    
    def _detect_equal_division(self, image: np.ndarray, padding: int = 2, 
                               num_frames: int = 8) -> Tuple[List[np.ndarray], List[Tuple]]:
        """
        Detect frames by equal division
        
        Args:
            image: Input sprite sheet image
            padding: Padding to add around each frame
            num_frames: Number of frames to divide into
        
        Returns:
            Tuple of (list of frame images, list of bounding boxes)
        """
        frames = []
        frame_boxes = []
        
        img_width = image.shape[1]
        img_height = image.shape[0]
        
        # Assume horizontal layout
        frame_width = img_width // num_frames
        frame_height = img_height
        
        for i in range(num_frames):
            minc = i * frame_width
            maxc = (i + 1) * frame_width if i < num_frames - 1 else img_width
            minr = 0
            maxr = img_height
            
            # Add padding
            minc_p = max(0, minc - padding)
            minr_p = max(0, minr - padding)
            maxc_p = min(img_width, maxc + padding)
            maxr_p = min(img_height, maxr + padding)
            
            frame = image[minr_p:maxr_p, minc_p:maxc_p]
            frames.append(frame)
            frame_boxes.append((minr_p, minc_p, maxr_p, maxc_p))
        
        return frames, frame_boxes
    
    def detect_frames_manual(self, image: np.ndarray, num_frames: int, 
                            padding: int = 2) -> Tuple[List[np.ndarray], List[Tuple]]:
        """
        Manually specify number of frames
        
        Args:
            image: Input sprite sheet image
            num_frames: Number of frames
            padding: Padding to add around each frame
        
        Returns:
            Tuple of (list of frame images, list of bounding boxes)
        """
        return self._detect_equal_division(image, padding, num_frames)
    
    def refine_frame_boundaries(self, image: np.ndarray, frame: np.ndarray, 
                                bbox: Tuple) -> Tuple[np.ndarray, Tuple]:
        """
        Refine frame boundaries to remove excess transparent space
        
        Args:
            image: Original image
            frame: Extracted frame
            bbox: Bounding box (minr, minc, maxr, maxc)
        
        Returns:
            Refined frame and bounding box
        """
        minr, minc, maxr, maxc = bbox
        
        # Handle alpha channel
        if len(frame.shape) == 3 and frame.shape[2] == 4:
            alpha = frame[:, :, 3]
            # Find non-transparent pixels
            rows = np.any(alpha > 10, axis=1)
            cols = np.any(alpha > 10, axis=0)
        else:
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            rows = np.any(gray > 10, axis=1)
            cols = np.any(gray > 10, axis=0)
        
        # Find bounds
        row_indices = np.where(rows)[0]
        col_indices = np.where(cols)[0]
        
        if len(row_indices) > 0 and len(col_indices) > 0:
            # Calculate new bounds
            new_minr = minr + row_indices[0]
            new_maxr = minr + row_indices[-1] + 1
            new_minc = minc + col_indices[0]
            new_maxc = minc + col_indices[-1] + 1
            
            # Extract refined frame
            refined_frame = image[new_minr:new_maxr, new_minc:new_maxc]
            
            return refined_frame, (new_minr, new_minc, new_maxr, new_maxc)
        
        return frame, bbox
    
    def detect_frame_size(self, image: np.ndarray) -> Tuple[int, int]:
        """
        Detect the size of individual frames
        
        Args:
            image: Input sprite sheet image
        
        Returns:
            Tuple of (frame_width, frame_height)
        """
        # Handle alpha channel
        if len(image.shape) == 3 and image.shape[2] == 4:
            alpha = image[:, :, 3]
            _, binary = cv2.threshold(alpha, 10, 255, cv2.THRESH_BINARY)
        else:
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            _, binary = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
        
        # Find vertical projection
        v_proj = np.sum(binary, axis=0)
        
        # Find gaps
        threshold = np.max(v_proj) * 0.1
        gaps = v_proj < threshold
        
        # Find gap positions
        gap_starts = []
        gap_ends = []
        
        in_gap = False
        for i, is_gap in enumerate(gaps):
            if not in_gap and is_gap:
                gap_starts.append(i)
                in_gap = True
            elif in_gap and not is_gap:
                gap_ends.append(i)
                in_gap = False
        
        # Calculate frame width from gap positions
        if len(gap_starts) > 0:
            # Average distance between gaps
            if len(gap_starts) > 1:
                frame_width = int(np.mean(np.diff(gap_starts)))
            else:
                frame_width = gap_starts[0]
        else:
            # No gaps found, assume single frame
            frame_width = image.shape[1]
        
        frame_height = image.shape[0]
        
        return frame_width, frame_height