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import cv2
import numpy as np
from sklearn.cluster import KMeans
import warnings
import time

import torch
from torchvision.ops import batched_nms
from numpy import ndarray
# Suppress ALL runtime and sklearn warnings
warnings.filterwarnings('ignore', category=RuntimeWarning)
warnings.filterwarnings('ignore', category=FutureWarning) 
warnings.filterwarnings('ignore', category=UserWarning)

# Suppress sklearn warnings specifically
import logging
logging.getLogger('sklearn').setLevel(logging.ERROR)

def get_grass_color(img):
    # Convert image to HSV color space
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    # Define range of green color in HSV
    lower_green = np.array([30, 40, 40])
    upper_green = np.array([80, 255, 255])

    # Threshold the HSV image to get only green colors
    mask = cv2.inRange(hsv, lower_green, upper_green)

    # Calculate the mean value of the pixels that are not masked
    masked_img = cv2.bitwise_and(img, img, mask=mask)
    grass_color = cv2.mean(img, mask=mask)
    return grass_color[:3]

def get_players_boxes(frame, result):
    players_imgs = []
    players_boxes = []
    for (box, score, cls) in result:
        label = int(cls)
        if label == 0:
            x1, y1, x2, y2 = box.astype(int)
            player_img = frame[y1: y2, x1: x2]
            players_imgs.append(player_img)
            players_boxes.append([box, score, cls])
    return players_imgs, players_boxes

def get_kits_colors(players, grass_hsv=None, frame=None):
    kits_colors = []
    if grass_hsv is None:
        grass_color = get_grass_color(frame)
        grass_hsv = cv2.cvtColor(np.uint8([[list(grass_color)]]), cv2.COLOR_BGR2HSV)

    for player_img in players:
        # Skip empty or invalid images
        if player_img is None or player_img.size == 0 or len(player_img.shape) != 3:
            continue
            
        # Convert image to HSV color space
        hsv = cv2.cvtColor(player_img, cv2.COLOR_BGR2HSV)

        # Define range of green color in HSV
        lower_green = np.array([grass_hsv[0, 0, 0] - 10, 40, 40])
        upper_green = np.array([grass_hsv[0, 0, 0] + 10, 255, 255])

        # Threshold the HSV image to get only green colors
        mask = cv2.inRange(hsv, lower_green, upper_green)

        # Bitwise-AND mask and original image
        mask = cv2.bitwise_not(mask)
        upper_mask = np.zeros(player_img.shape[:2], np.uint8)
        upper_mask[0:player_img.shape[0]//2, 0:player_img.shape[1]] = 255
        mask = cv2.bitwise_and(mask, upper_mask)

        kit_color = np.array(cv2.mean(player_img, mask=mask)[:3])

        kits_colors.append(kit_color)
    return kits_colors

def get_kits_classifier(kits_colors):
    if len(kits_colors) == 0:
        return None
    if len(kits_colors) == 1:
        # Only one kit color, create a dummy classifier
        return None
    kits_kmeans = KMeans(n_clusters=2)
    kits_kmeans.fit(kits_colors)
    return kits_kmeans

def classify_kits(kits_classifer, kits_colors):
    if kits_classifer is None or len(kits_colors) == 0:
        return np.array([0])  # Default to team 0
    team = kits_classifer.predict(kits_colors)
    return team

def get_left_team_label(players_boxes, kits_colors, kits_clf):
    left_team_label = 0
    team_0 = []
    team_1 = []

    for i in range(len(players_boxes)):
        x1, y1, x2, y2 = players_boxes[i][0].astype(int)
        team = classify_kits(kits_clf, [kits_colors[i]]).item()
        if team == 0:
            team_0.append(np.array([x1]))
        else:
            team_1.append(np.array([x1]))

    team_0 = np.array(team_0)
    team_1 = np.array(team_1)

    # Safely calculate averages with fallback for empty arrays
    avg_team_0 = np.average(team_0) if len(team_0) > 0 else 0
    avg_team_1 = np.average(team_1) if len(team_1) > 0 else 0
    
    if avg_team_0 - avg_team_1 > 0:
        left_team_label = 1

    return left_team_label

def check_box_boundaries(boxes, img_height, img_width):
    """
    Check if bounding boxes are within image boundaries and clip them if necessary.
    
    Args:
        boxes: numpy array of shape (N, 4) with [x1, y1, x2, y2] format
        img_height: height of the image
        img_width: width of the image
    
    Returns:
        valid_boxes: numpy array of valid boxes within boundaries
        valid_indices: indices of valid boxes
    """
    x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
    
    # Check if boxes are within boundaries
    valid_mask = (x1 >= 0) & (y1 >= 0) & (x2 < img_width) & (y2 < img_height) & (x1 < x2) & (y1 < y2)
    
    if not np.any(valid_mask):
        return np.array([]), np.array([])
    
    valid_boxes = boxes[valid_mask]
    valid_indices = np.where(valid_mask)[0]
    
    # Clip boxes to image boundaries
    valid_boxes[:, 0] = np.clip(valid_boxes[:, 0], 0, img_width - 1)   # x1
    valid_boxes[:, 1] = np.clip(valid_boxes[:, 1], 0, img_height - 1)  # y1
    valid_boxes[:, 2] = np.clip(valid_boxes[:, 2], 0, img_width - 1)   # x2
    valid_boxes[:, 3] = np.clip(valid_boxes[:, 3], 0, img_height - 1)  # y2
    
    return valid_boxes, valid_indices

def process_team_identification_batch(frames, results, kits_clf, left_team_label, grass_hsv):
    """
    Process team identification and label formatting for batch results.
    
    Args:
        frames: list of frames
        results: list of detection results for each frame
        kits_clf: trained kit classifier
        left_team_label: label for left team
        grass_hsv: grass color in HSV format
    
    Returns:
        processed_results: list of processed results with team identification
    """
    processed_results = []
    
    for frame_idx, frame in enumerate(frames):
        frame_results = []
        frame_detections = results[frame_idx]
        
        if not frame_detections:
            processed_results.append([])
            continue
            
        # Extract player boxes and images
        players_imgs = []
        players_boxes = []
        player_indices = []
        
        for idx, (box, score, cls) in enumerate(frame_detections):
            label = int(cls)
            if label == 0:  # Player detection
                x1, y1, x2, y2 = box.astype(int)
                
                # Check boundaries
                if (x1 >= 0 and y1 >= 0 and x2 < frame.shape[1] and y2 < frame.shape[0] and x1 < x2 and y1 < y2):
                    player_img = frame[y1:y2, x1:x2]
                    if player_img.size > 0:  # Ensure valid image
                        players_imgs.append(player_img)
                        players_boxes.append([box, score, cls])
                        player_indices.append(idx)
        
        # Initialize player team mapping
        player_team_map = {}
        
        # Process team identification if we have players
        if players_imgs and kits_clf is not None:
            kits_colors = get_kits_colors(players_imgs, grass_hsv)
            teams = classify_kits(kits_clf, kits_colors)
            
            # Create mapping from player index to team
            for i, team in enumerate(teams):
                player_team_map[player_indices[i]] = team.item()
        
        id = 0
        # Process all detections with team identification
        for idx, (box, score, cls) in enumerate(frame_detections):
            label = int(cls)
            x1, y1, x2, y2 = box.astype(int)
            
            # Check boundaries
            valid_boxes, valid_indices = check_box_boundaries(
                np.array([[x1, y1, x2, y2]]), frame.shape[0], frame.shape[1]
            )
            
            if len(valid_boxes) == 0:
                continue
                
            x1, y1, x2, y2 = valid_boxes[0].astype(int)
            
            # Apply team identification logic
            if label == 0:  # Player
                if players_imgs and kits_clf is not None and idx in player_team_map:
                    team = player_team_map[idx]
                    if team == left_team_label:
                        final_label = 6  # Player-L (Left team)
                    else:
                        final_label = 7  # Player-R (Right team)
                else:
                    final_label = 6  # Default player label
                    
            elif label == 1:  # Goalkeeper
                final_label = 1  # GK
                
            elif label == 2:  # Ball
                final_label = 0  # Ball
                
            elif label == 3 or label == 4:  # Referee or other
                final_label = 3  # Referee
                
            else:
                continue
                # final_label = int(label)  # Keep original label, ensure it's int
            
            frame_results.append({
                "id": int(id),
                "bbox": [int(x1), int(y1), int(x2), int(y2)],
                "class_id": int(final_label),
                "conf": float(score)
            })
            id = id + 1
        
        processed_results.append(frame_results)
    
    return processed_results

def convert_numpy_types(obj):
    """Convert numpy types to native Python types for JSON serialization."""
    if isinstance(obj, np.integer):
        return int(obj)
    elif isinstance(obj, np.floating):
        return float(obj)
    elif isinstance(obj, np.ndarray):
        return obj.tolist()
    elif isinstance(obj, dict):
        return {key: convert_numpy_types(value) for key, value in obj.items()}
    elif isinstance(obj, list):
        return [convert_numpy_types(item) for item in obj]
    else:
        return obj

def pre_process_img(frames, scale):
    imgs = np.stack([cv2.resize(frame, (int(scale), int(scale))) for frame in frames])
    imgs = imgs.transpose(0, 3, 1, 2)
    imgs = imgs.astype(np.float32) / 255.0  # Normalize
    return imgs

def post_process_output(outputs, x_scale, y_scale, conf_thresh=0.6, nms_thresh=0.75):
    B, C, N = outputs.shape
    outputs = torch.from_numpy(outputs)
    outputs = outputs.permute(0, 2, 1)
    boxes = outputs[..., :4]
    class_scores = 1 / (1 + torch.exp(-outputs[..., 4:]))
    conf, class_id = class_scores.max(dim=2)

    mask = conf > conf_thresh
    
    for i in range(class_id.shape[0]):  # loop over batch
    # Find detections that are balls
        ball_idx = np.where(class_id[i] == 2)[0]
        if ball_idx.size > 0:
            # Pick the one with the highest confidence
            top = ball_idx[np.argmax(conf[i, ball_idx])]
            if conf[i, top] > 0.55:  # apply confidence threshold
                mask[i, top] = True
                
    # ball_mask = (class_id == 2) & (conf > 0.51)
    # mask = mask | ball_mask
    
    batch_idx, pred_idx = mask.nonzero(as_tuple=True)

    if len(batch_idx) == 0:
        return [[] for _ in range(B)]
    
    boxes = boxes[batch_idx, pred_idx]
    conf = conf[batch_idx, pred_idx]
    class_id = class_id[batch_idx, pred_idx]

    x, y, w, h = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
    x1 = (x - w / 2) * x_scale
    y1 = (y - h / 2) * y_scale
    x2 = (x + w / 2) * x_scale
    y2 = (y + h / 2) * y_scale
    boxes_xyxy = torch.stack([x1, y1, x2, y2], dim=1)

    max_coord = 1e4
    offset = batch_idx.to(boxes_xyxy) * max_coord
    boxes_for_nms = boxes_xyxy + offset[:, None]

    keep = batched_nms(boxes_for_nms, conf, batch_idx, nms_thresh)

    boxes_final = boxes_xyxy[keep]
    conf_final = conf[keep]
    class_final = class_id[keep]
    batch_final = batch_idx[keep]

    results = [[] for _ in range(B)]
    for b in range(B):
        mask_b = batch_final == b
        if mask_b.sum() == 0:
            continue
        results[b] = list(zip(boxes_final[mask_b].numpy(),
                              conf_final[mask_b].numpy(),
                              class_final[mask_b].numpy()))
    return results

def player_detection_result(frames: list[ndarray], batch_size, model, kits_clf=None, left_team_label=None, grass_hsv=None):
    start_time = time.time()
    # input_layer = model.input(0)
    # output_layer = model.output(0)
    height, width = frames[0].shape[:2]
    scale = 640.0
    x_scale = width / scale
    y_scale = height / scale

    # infer_queue = AsyncInferQueue(model, len(frames))
    
    infer_time = time.time()
    kits_clf = kits_clf
    left_team_label = left_team_label
    grass_hsv = grass_hsv
    results = []
    for i in range(0, len(frames), batch_size):
        if i + batch_size > len(frames):
            batch_size = len(frames) - i
        batch_frames = frames[i:i + batch_size]
        imgs = pre_process_img(batch_frames, scale)

        input_name = model.get_inputs()[0].name
        outputs = model.run(None, {input_name: imgs})[0]
        raw_results = post_process_output(np.array(outputs), x_scale, y_scale)

        if kits_clf is None or left_team_label is None or grass_hsv is None:
            # Use first frame to initialize team classification
            first_frame = batch_frames[0]
            first_frame_results = raw_results[0] if raw_results else []
            
            if first_frame_results:
                players_imgs, players_boxes = get_players_boxes(first_frame, first_frame_results)
                if players_imgs:
                    grass_color = get_grass_color(first_frame)
                    grass_hsv = cv2.cvtColor(np.uint8([[list(grass_color)]]), cv2.COLOR_BGR2HSV)
                    kits_colors = get_kits_colors(players_imgs, grass_hsv)
                    if kits_colors:  # Only proceed if we have valid kit colors
                        kits_clf = get_kits_classifier(kits_colors)
                        if kits_clf is not None:
                            left_team_label = int(get_left_team_label(players_boxes, kits_colors, kits_clf))

        # Process team identification and boundary checking
        processed_results = process_team_identification_batch(
            batch_frames, raw_results, kits_clf, left_team_label, grass_hsv
        )
    
        processed_results = convert_numpy_types(processed_results)
        results.extend(processed_results)

    # Return the same format as before for compatibility
    return results, kits_clf, left_team_label, grass_hsv

	import cv2
	import numpy as np
	from sklearn.cluster import KMeans
	import warnings
	import time

	import torch
	from torchvision.ops import batched_nms
	from numpy import ndarray
	# Suppress ALL runtime and sklearn warnings
	warnings.filterwarnings('ignore', category=RuntimeWarning)
	warnings.filterwarnings('ignore', category=FutureWarning)
	warnings.filterwarnings('ignore', category=UserWarning)

	# Suppress sklearn warnings specifically
	import logging
	logging.getLogger('sklearn').setLevel(logging.ERROR)

	def get_grass_color(img):
	# Convert image to HSV color space
	hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

	# Define range of green color in HSV
	lower_green = np.array([30, 40, 40])
	upper_green = np.array([80, 255, 255])

	# Threshold the HSV image to get only green colors
	mask = cv2.inRange(hsv, lower_green, upper_green)

	# Calculate the mean value of the pixels that are not masked
	masked_img = cv2.bitwise_and(img, img, mask=mask)
	grass_color = cv2.mean(img, mask=mask)
	return grass_color[:3]

	def get_players_boxes(frame, result):
	players_imgs = []
	players_boxes = []
	for (box, score, cls) in result:
	label = int(cls)
	if label == 0:
	x1, y1, x2, y2 = box.astype(int)
	player_img = frame[y1: y2, x1: x2]
	players_imgs.append(player_img)
	players_boxes.append([box, score, cls])
	return players_imgs, players_boxes

	def get_kits_colors(players, grass_hsv=None, frame=None):
	kits_colors = []
	if grass_hsv is None:
	grass_color = get_grass_color(frame)
	grass_hsv = cv2.cvtColor(np.uint8([[list(grass_color)]]), cv2.COLOR_BGR2HSV)

	for player_img in players:
	# Skip empty or invalid images
	if player_img is None or player_img.size == 0 or len(player_img.shape) != 3:
	continue

	# Convert image to HSV color space
	hsv = cv2.cvtColor(player_img, cv2.COLOR_BGR2HSV)

	# Define range of green color in HSV
	lower_green = np.array([grass_hsv[0, 0, 0] - 10, 40, 40])
	upper_green = np.array([grass_hsv[0, 0, 0] + 10, 255, 255])

	# Threshold the HSV image to get only green colors
	mask = cv2.inRange(hsv, lower_green, upper_green)

	# Bitwise-AND mask and original image
	mask = cv2.bitwise_not(mask)
	upper_mask = np.zeros(player_img.shape[:2], np.uint8)
	upper_mask[0:player_img.shape[0]//2, 0:player_img.shape[1]] = 255
	mask = cv2.bitwise_and(mask, upper_mask)

	kit_color = np.array(cv2.mean(player_img, mask=mask)[:3])

	kits_colors.append(kit_color)
	return kits_colors

	def get_kits_classifier(kits_colors):
	if len(kits_colors) == 0:
	return None
	if len(kits_colors) == 1:
	# Only one kit color, create a dummy classifier
	return None
	kits_kmeans = KMeans(n_clusters=2)
	kits_kmeans.fit(kits_colors)
	return kits_kmeans

	def classify_kits(kits_classifer, kits_colors):
	if kits_classifer is None or len(kits_colors) == 0:
	return np.array([0]) # Default to team 0
	team = kits_classifer.predict(kits_colors)
	return team

	def get_left_team_label(players_boxes, kits_colors, kits_clf):
	left_team_label = 0
	team_0 = []
	team_1 = []

	for i in range(len(players_boxes)):
	x1, y1, x2, y2 = players_boxes[i][0].astype(int)
	team = classify_kits(kits_clf, [kits_colors[i]]).item()
	if team == 0:
	team_0.append(np.array([x1]))
	else:
	team_1.append(np.array([x1]))

	team_0 = np.array(team_0)
	team_1 = np.array(team_1)

	# Safely calculate averages with fallback for empty arrays
	avg_team_0 = np.average(team_0) if len(team_0) > 0 else 0
	avg_team_1 = np.average(team_1) if len(team_1) > 0 else 0

	if avg_team_0 - avg_team_1 > 0:
	left_team_label = 1

	return left_team_label

	def check_box_boundaries(boxes, img_height, img_width):
	"""
	Check if bounding boxes are within image boundaries and clip them if necessary.

	Args:
	boxes: numpy array of shape (N, 4) with [x1, y1, x2, y2] format
	img_height: height of the image
	img_width: width of the image

	Returns:
	valid_boxes: numpy array of valid boxes within boundaries
	valid_indices: indices of valid boxes
	"""
	x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]

	# Check if boxes are within boundaries
	valid_mask = (x1 >= 0) & (y1 >= 0) & (x2 < img_width) & (y2 < img_height) & (x1 < x2) & (y1 < y2)

	if not np.any(valid_mask):
	return np.array([]), np.array([])

	valid_boxes = boxes[valid_mask]
	valid_indices = np.where(valid_mask)[0]

	# Clip boxes to image boundaries
	valid_boxes[:, 0] = np.clip(valid_boxes[:, 0], 0, img_width - 1) # x1
	valid_boxes[:, 1] = np.clip(valid_boxes[:, 1], 0, img_height - 1) # y1
	valid_boxes[:, 2] = np.clip(valid_boxes[:, 2], 0, img_width - 1) # x2
	valid_boxes[:, 3] = np.clip(valid_boxes[:, 3], 0, img_height - 1) # y2

	return valid_boxes, valid_indices

	def process_team_identification_batch(frames, results, kits_clf, left_team_label, grass_hsv):
	"""
	Process team identification and label formatting for batch results.

	Args:
	frames: list of frames
	results: list of detection results for each frame
	kits_clf: trained kit classifier
	left_team_label: label for left team
	grass_hsv: grass color in HSV format

	Returns:
	processed_results: list of processed results with team identification
	"""
	processed_results = []

	for frame_idx, frame in enumerate(frames):
	frame_results = []
	frame_detections = results[frame_idx]

	if not frame_detections:
	processed_results.append([])
	continue

	# Extract player boxes and images
	players_imgs = []
	players_boxes = []
	player_indices = []

	for idx, (box, score, cls) in enumerate(frame_detections):
	label = int(cls)
	if label == 0: # Player detection
	x1, y1, x2, y2 = box.astype(int)

	# Check boundaries
	if (x1 >= 0 and y1 >= 0 and x2 < frame.shape[1] and y2 < frame.shape[0] and x1 < x2 and y1 < y2):
	player_img = frame[y1:y2, x1:x2]
	if player_img.size > 0: # Ensure valid image
	players_imgs.append(player_img)
	players_boxes.append([box, score, cls])
	player_indices.append(idx)

	# Initialize player team mapping
	player_team_map = {}

	# Process team identification if we have players
	if players_imgs and kits_clf is not None:
	kits_colors = get_kits_colors(players_imgs, grass_hsv)
	teams = classify_kits(kits_clf, kits_colors)

	# Create mapping from player index to team
	for i, team in enumerate(teams):
	player_team_map[player_indices[i]] = team.item()

	id = 0
	# Process all detections with team identification
	for idx, (box, score, cls) in enumerate(frame_detections):
	label = int(cls)
	x1, y1, x2, y2 = box.astype(int)

	# Check boundaries
	valid_boxes, valid_indices = check_box_boundaries(
	np.array([[x1, y1, x2, y2]]), frame.shape[0], frame.shape[1]
	)

	if len(valid_boxes) == 0:
	continue

	x1, y1, x2, y2 = valid_boxes[0].astype(int)

	# Apply team identification logic
	if label == 0: # Player
	if players_imgs and kits_clf is not None and idx in player_team_map:
	team = player_team_map[idx]
	if team == left_team_label:
	final_label = 6 # Player-L (Left team)
	else:
	final_label = 7 # Player-R (Right team)
	else:
	final_label = 6 # Default player label

	elif label == 1: # Goalkeeper
	final_label = 1 # GK

	elif label == 2: # Ball
	final_label = 0 # Ball

	elif label == 3 or label == 4: # Referee or other
	final_label = 3 # Referee

	else:
	continue
	# final_label = int(label) # Keep original label, ensure it's int

	frame_results.append({
	"id": int(id),
	"bbox": [int(x1), int(y1), int(x2), int(y2)],
	"class_id": int(final_label),
	"conf": float(score)
	})
	id = id + 1

	processed_results.append(frame_results)

	return processed_results

	def convert_numpy_types(obj):
	"""Convert numpy types to native Python types for JSON serialization."""
	if isinstance(obj, np.integer):
	return int(obj)
	elif isinstance(obj, np.floating):
	return float(obj)
	elif isinstance(obj, np.ndarray):
	return obj.tolist()
	elif isinstance(obj, dict):
	return {key: convert_numpy_types(value) for key, value in obj.items()}
	elif isinstance(obj, list):
	return [convert_numpy_types(item) for item in obj]
	else:
	return obj

	def pre_process_img(frames, scale):
	imgs = np.stack([cv2.resize(frame, (int(scale), int(scale))) for frame in frames])
	imgs = imgs.transpose(0, 3, 1, 2)
	imgs = imgs.astype(np.float32) / 255.0 # Normalize
	return imgs

	def post_process_output(outputs, x_scale, y_scale, conf_thresh=0.6, nms_thresh=0.75):
	B, C, N = outputs.shape
	outputs = torch.from_numpy(outputs)
	outputs = outputs.permute(0, 2, 1)
	boxes = outputs[..., :4]
	class_scores = 1 / (1 + torch.exp(-outputs[..., 4:]))
	conf, class_id = class_scores.max(dim=2)

	mask = conf > conf_thresh

	for i in range(class_id.shape[0]): # loop over batch
	# Find detections that are balls
	ball_idx = np.where(class_id[i] == 2)[0]
	if ball_idx.size > 0:
	# Pick the one with the highest confidence
	top = ball_idx[np.argmax(conf[i, ball_idx])]
	if conf[i, top] > 0.55: # apply confidence threshold
	mask[i, top] = True

	# ball_mask = (class_id == 2) & (conf > 0.51)
	# mask = mask \| ball_mask

	batch_idx, pred_idx = mask.nonzero(as_tuple=True)

	if len(batch_idx) == 0:
	return [[] for _ in range(B)]

	boxes = boxes[batch_idx, pred_idx]
	conf = conf[batch_idx, pred_idx]
	class_id = class_id[batch_idx, pred_idx]

	x, y, w, h = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
	x1 = (x - w / 2) * x_scale
	y1 = (y - h / 2) * y_scale
	x2 = (x + w / 2) * x_scale
	y2 = (y + h / 2) * y_scale
	boxes_xyxy = torch.stack([x1, y1, x2, y2], dim=1)

	max_coord = 1e4
	offset = batch_idx.to(boxes_xyxy) * max_coord
	boxes_for_nms = boxes_xyxy + offset[:, None]

	keep = batched_nms(boxes_for_nms, conf, batch_idx, nms_thresh)

	boxes_final = boxes_xyxy[keep]
	conf_final = conf[keep]
	class_final = class_id[keep]
	batch_final = batch_idx[keep]

	results = [[] for _ in range(B)]
	for b in range(B):
	mask_b = batch_final == b
	if mask_b.sum() == 0:
	continue
	results[b] = list(zip(boxes_final[mask_b].numpy(),
	conf_final[mask_b].numpy(),
	class_final[mask_b].numpy()))
	return results

	def player_detection_result(frames: list[ndarray], batch_size, model, kits_clf=None, left_team_label=None, grass_hsv=None):
	start_time = time.time()
	# input_layer = model.input(0)
	# output_layer = model.output(0)
	height, width = frames[0].shape[:2]
	scale = 640.0
	x_scale = width / scale
	y_scale = height / scale

	# infer_queue = AsyncInferQueue(model, len(frames))

	infer_time = time.time()
	kits_clf = kits_clf
	left_team_label = left_team_label
	grass_hsv = grass_hsv
	results = []
	for i in range(0, len(frames), batch_size):
	if i + batch_size > len(frames):
	batch_size = len(frames) - i
	batch_frames = frames[i:i + batch_size]
	imgs = pre_process_img(batch_frames, scale)

	input_name = model.get_inputs()[0].name
	outputs = model.run(None, {input_name: imgs})[0]
	raw_results = post_process_output(np.array(outputs), x_scale, y_scale)

	if kits_clf is None or left_team_label is None or grass_hsv is None:
	# Use first frame to initialize team classification
	first_frame = batch_frames[0]
	first_frame_results = raw_results[0] if raw_results else []

	if first_frame_results:
	players_imgs, players_boxes = get_players_boxes(first_frame, first_frame_results)
	if players_imgs:
	grass_color = get_grass_color(first_frame)
	grass_hsv = cv2.cvtColor(np.uint8([[list(grass_color)]]), cv2.COLOR_BGR2HSV)
	kits_colors = get_kits_colors(players_imgs, grass_hsv)
	if kits_colors: # Only proceed if we have valid kit colors
	kits_clf = get_kits_classifier(kits_colors)
	if kits_clf is not None:
	left_team_label = int(get_left_team_label(players_boxes, kits_colors, kits_clf))

	# Process team identification and boundary checking
	processed_results = process_team_identification_batch(
	batch_frames, raw_results, kits_clf, left_team_label, grass_hsv
	)

	processed_results = convert_numpy_types(processed_results)
	results.extend(processed_results)

	# Return the same format as before for compatibility
	return results, kits_clf, left_team_label, grass_hsv