scorevision: push artifact

pitch.py

@@ -0,0 +1,669 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import time
+from typing import List, Optional, Tuple
+
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as f
+from pydantic import BaseModel
+
+import logging
+logger = logging.getLogger(__name__)
+
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox]
+    keypoints: list[tuple[int, int]]
+
+BatchNorm2d = nn.BatchNorm2d
+BN_MOMENTUM = 0.1
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
+                     stride=stride, padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
+                               bias=False)
+        self.bn3 = BatchNorm2d(planes * self.expansion,
+                               momentum=BN_MOMENTUM)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class HighResolutionModule(nn.Module):
+    def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
+                 num_channels, fuse_method, multi_scale_output=True):
+        super(HighResolutionModule, self).__init__()
+        self._check_branches(
+            num_branches, blocks, num_blocks, num_inchannels, num_channels)
+
+        self.num_inchannels = num_inchannels
+        self.fuse_method = fuse_method
+        self.num_branches = num_branches
+
+        self.multi_scale_output = multi_scale_output
+
+        self.branches = self._make_branches(
+            num_branches, blocks, num_blocks, num_channels)
+        self.fuse_layers = self._make_fuse_layers()
+        self.relu = nn.ReLU(inplace=True)
+
+    def _check_branches(self, num_branches, blocks, num_blocks,
+                        num_inchannels, num_channels):
+        if num_branches != len(num_blocks):
+            error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
+                num_branches, len(num_blocks))
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        if num_branches != len(num_channels):
+            error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
+                num_branches, len(num_channels))
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        if num_branches != len(num_inchannels):
+            error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
+                num_branches, len(num_inchannels))
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+    def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
+                         stride=1):
+        downsample = None
+        if stride != 1 or \
+                self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.num_inchannels[branch_index],
+                          num_channels[branch_index] * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                BatchNorm2d(num_channels[branch_index] * block.expansion,
+                            momentum=BN_MOMENTUM),
+            )
+
+        layers = []
+        layers.append(block(self.num_inchannels[branch_index],
+                            num_channels[branch_index], stride, downsample))
+        self.num_inchannels[branch_index] = \
+            num_channels[branch_index] * block.expansion
+        for i in range(1, num_blocks[branch_index]):
+            layers.append(block(self.num_inchannels[branch_index],
+                                num_channels[branch_index]))
+
+        return nn.Sequential(*layers)
+
+    def _make_branches(self, num_branches, block, num_blocks, num_channels):
+        branches = []
+
+        for i in range(num_branches):
+            branches.append(
+                self._make_one_branch(i, block, num_blocks, num_channels))
+
+        return nn.ModuleList(branches)
+
+    def _make_fuse_layers(self):
+        if self.num_branches == 1:
+            return None
+
+        num_branches = self.num_branches
+        num_inchannels = self.num_inchannels
+        fuse_layers = []
+        for i in range(num_branches if self.multi_scale_output else 1):
+            fuse_layer = []
+            for j in range(num_branches):
+                if j > i:
+                    fuse_layer.append(nn.Sequential(
+                        nn.Conv2d(num_inchannels[j],
+                                  num_inchannels[i],
+                                  1,
+                                  1,
+                                  0,
+                                  bias=False),
+                        BatchNorm2d(num_inchannels[i], momentum=BN_MOMENTUM)))
+                    # nn.Upsample(scale_factor=2**(j-i), mode='nearest')))
+                elif j == i:
+                    fuse_layer.append(None)
+                else:
+                    conv3x3s = []
+                    for k in range(i - j):
+                        if k == i - j - 1:
+                            num_outchannels_conv3x3 = num_inchannels[i]
+                            conv3x3s.append(nn.Sequential(
+                                nn.Conv2d(num_inchannels[j],
+                                          num_outchannels_conv3x3,
+                                          3, 2, 1, bias=False),
+                                BatchNorm2d(num_outchannels_conv3x3, momentum=BN_MOMENTUM)))
+                        else:
+                            num_outchannels_conv3x3 = num_inchannels[j]
+                            conv3x3s.append(nn.Sequential(
+                                nn.Conv2d(num_inchannels[j],
+                                          num_outchannels_conv3x3,
+                                          3, 2, 1, bias=False),
+                                BatchNorm2d(num_outchannels_conv3x3,
+                                            momentum=BN_MOMENTUM),
+                                nn.ReLU(inplace=True)))
+                    fuse_layer.append(nn.Sequential(*conv3x3s))
+            fuse_layers.append(nn.ModuleList(fuse_layer))
+
+        return nn.ModuleList(fuse_layers)
+
+    def get_num_inchannels(self):
+        return self.num_inchannels
+
+    def forward(self, x):
+        if self.num_branches == 1:
+            return [self.branches[0](x[0])]
+
+        for i in range(self.num_branches):
+            x[i] = self.branches[i](x[i])
+
+        x_fuse = []
+        for i in range(len(self.fuse_layers)):
+            y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
+            for j in range(1, self.num_branches):
+                if i == j:
+                    y = y + x[j]
+                elif j > i:
+                    y = y + F.interpolate(
+                        self.fuse_layers[i][j](x[j]),
+                        size=[x[i].shape[2], x[i].shape[3]],
+                        mode='bilinear')
+                else:
+                    y = y + self.fuse_layers[i][j](x[j])
+            x_fuse.append(self.relu(y))
+
+        return x_fuse
+
+
+blocks_dict = {
+    'BASIC': BasicBlock,
+    'BOTTLENECK': Bottleneck
+}
+
+
+class HighResolutionNet(nn.Module):
+
+    def __init__(self, config, **kwargs):
+        self.inplanes = 64
+        extra = config['MODEL']['EXTRA']
+        super(HighResolutionNet, self).__init__()
+
+        # stem net
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=2, padding=1,
+                               bias=False)
+        self.bn1 = BatchNorm2d(self.inplanes, momentum=BN_MOMENTUM)
+        self.conv2 = nn.Conv2d(self.inplanes, self.inplanes, kernel_size=3, stride=2, padding=1,
+                               bias=False)
+        self.bn2 = BatchNorm2d(self.inplanes, momentum=BN_MOMENTUM)
+        self.relu = nn.ReLU(inplace=True)
+        self.sf = nn.Softmax(dim=1)
+        self.layer1 = self._make_layer(Bottleneck, 64, 64, 4)
+
+        self.stage2_cfg = extra['STAGE2']
+        num_channels = self.stage2_cfg['NUM_CHANNELS']
+        block = blocks_dict[self.stage2_cfg['BLOCK']]
+        num_channels = [
+            num_channels[i] * block.expansion for i in range(len(num_channels))]
+        self.transition1 = self._make_transition_layer(
+            [256], num_channels)
+        self.stage2, pre_stage_channels = self._make_stage(
+            self.stage2_cfg, num_channels)
+
+        self.stage3_cfg = extra['STAGE3']
+        num_channels = self.stage3_cfg['NUM_CHANNELS']
+        block = blocks_dict[self.stage3_cfg['BLOCK']]
+        num_channels = [
+            num_channels[i] * block.expansion for i in range(len(num_channels))]
+        self.transition2 = self._make_transition_layer(
+            pre_stage_channels, num_channels)
+        self.stage3, pre_stage_channels = self._make_stage(
+            self.stage3_cfg, num_channels)
+
+        self.stage4_cfg = extra['STAGE4']
+        num_channels = self.stage4_cfg['NUM_CHANNELS']
+        block = blocks_dict[self.stage4_cfg['BLOCK']]
+        num_channels = [
+            num_channels[i] * block.expansion for i in range(len(num_channels))]
+        self.transition3 = self._make_transition_layer(
+            pre_stage_channels, num_channels)
+        self.stage4, pre_stage_channels = self._make_stage(
+            self.stage4_cfg, num_channels, multi_scale_output=True)
+
+        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
+        final_inp_channels = sum(pre_stage_channels) + self.inplanes
+
+        self.head = nn.Sequential(nn.Sequential(
+            nn.Conv2d(
+                in_channels=final_inp_channels,
+                out_channels=final_inp_channels,
+                kernel_size=1),
+            BatchNorm2d(final_inp_channels, momentum=BN_MOMENTUM),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(
+                in_channels=final_inp_channels,
+                out_channels=config['MODEL']['NUM_JOINTS'],
+                kernel_size=extra['FINAL_CONV_KERNEL']),
+            nn.Softmax(dim=1)))
+
+
+
+    def _make_head(self, x, x_skip):
+        x = self.upsample(x)
+        x = torch.cat([x, x_skip], dim=1)
+        x = self.head(x)
+
+        return x
+
+    def _make_transition_layer(
+            self, num_channels_pre_layer, num_channels_cur_layer):
+        num_branches_cur = len(num_channels_cur_layer)
+        num_branches_pre = len(num_channels_pre_layer)
+
+        transition_layers = []
+        for i in range(num_branches_cur):
+            if i < num_branches_pre:
+                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
+                    transition_layers.append(nn.Sequential(
+                        nn.Conv2d(num_channels_pre_layer[i],
+                                  num_channels_cur_layer[i],
+                                  3,
+                                  1,
+                                  1,
+                                  bias=False),
+                        BatchNorm2d(
+                            num_channels_cur_layer[i], momentum=BN_MOMENTUM),
+                        nn.ReLU(inplace=True)))
+                else:
+                    transition_layers.append(None)
+            else:
+                conv3x3s = []
+                for j in range(i + 1 - num_branches_pre):
+                    inchannels = num_channels_pre_layer[-1]
+                    outchannels = num_channels_cur_layer[i] \
+                        if j == i - num_branches_pre else inchannels
+                    conv3x3s.append(nn.Sequential(
+                        nn.Conv2d(
+                            inchannels, outchannels, 3, 2, 1, bias=False),
+                        BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
+                        nn.ReLU(inplace=True)))
+                transition_layers.append(nn.Sequential(*conv3x3s))
+
+        return nn.ModuleList(transition_layers)
+
+    def _make_layer(self, block, inplanes, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
+            )
+
+        layers = []
+        layers.append(block(inplanes, planes, stride, downsample))
+        inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def _make_stage(self, layer_config, num_inchannels,
+                    multi_scale_output=True):
+        num_modules = layer_config['NUM_MODULES']
+        num_branches = layer_config['NUM_BRANCHES']
+        num_blocks = layer_config['NUM_BLOCKS']
+        num_channels = layer_config['NUM_CHANNELS']
+        block = blocks_dict[layer_config['BLOCK']]
+        fuse_method = layer_config['FUSE_METHOD']
+
+        modules = []
+        for i in range(num_modules):
+            # multi_scale_output is only used last module
+            if not multi_scale_output and i == num_modules - 1:
+                reset_multi_scale_output = False
+            else:
+                reset_multi_scale_output = True
+            modules.append(
+                HighResolutionModule(num_branches,
+                                     block,
+                                     num_blocks,
+                                     num_inchannels,
+                                     num_channels,
+                                     fuse_method,
+                                     reset_multi_scale_output)
+            )
+            num_inchannels = modules[-1].get_num_inchannels()
+
+        return nn.Sequential(*modules), num_inchannels
+
+    def forward(self, x):
+        # h, w = x.size(2), x.size(3)
+        x = self.conv1(x)
+        x_skip = x.clone()
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+        x = self.layer1(x)
+
+        x_list = []
+        for i in range(self.stage2_cfg['NUM_BRANCHES']):
+            if self.transition1[i] is not None:
+                x_list.append(self.transition1[i](x))
+            else:
+                x_list.append(x)
+        y_list = self.stage2(x_list)
+
+        x_list = []
+        for i in range(self.stage3_cfg['NUM_BRANCHES']):
+            if self.transition2[i] is not None:
+                x_list.append(self.transition2[i](y_list[-1]))
+            else:
+                x_list.append(y_list[i])
+        y_list = self.stage3(x_list)
+
+        x_list = []
+        for i in range(self.stage4_cfg['NUM_BRANCHES']):
+            if self.transition3[i] is not None:
+                x_list.append(self.transition3[i](y_list[-1]))
+            else:
+                x_list.append(y_list[i])
+        x = self.stage4(x_list)
+
+        # Head Part
+        height, width = x[0].size(2), x[0].size(3)
+        x1 = F.interpolate(x[1], size=(height, width), mode='bilinear', align_corners=False)
+        x2 = F.interpolate(x[2], size=(height, width), mode='bilinear', align_corners=False)
+        x3 = F.interpolate(x[3], size=(height, width), mode='bilinear', align_corners=False)
+        x = torch.cat([x[0], x1, x2, x3], 1)
+        x = self._make_head(x, x_skip)
+
+        return x
+
+    def init_weights(self, pretrained=''):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                #nn.init.normal_(m.weight, std=0.001)
+                #nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+        if pretrained != '':
+            if os.path.isfile(pretrained):
+                pretrained_dict = torch.load(pretrained)
+                model_dict = self.state_dict()
+                pretrained_dict = {k: v for k, v in pretrained_dict.items()
+                                   if k in model_dict.keys()}
+                model_dict.update(pretrained_dict)
+                self.load_state_dict(model_dict)
+            else:
+                sys.exit(f'Weights {pretrained} not found.')
+
+
+def get_cls_net(config, pretrained='', **kwargs):
+    """Create keypoint detection model with softmax activation"""
+    model = HighResolutionNet(config, **kwargs)
+    model.init_weights(pretrained)
+    return model
+
+
+def get_cls_net_l(config, pretrained='', **kwargs):
+    """Create line detection model with sigmoid activation"""
+    model = HighResolutionNet(config, **kwargs)
+    model.init_weights(pretrained)
+    
+    # After loading weights, replace just the activation function
+    # The saved model expects the nested Sequential structure
+    inner_seq = model.head[0]
+    # Replace softmax (index 4) with sigmoid
+    model.head[0][4] = nn.Sigmoid()
+    
+    return model
+
+# Simplified utility functions - removed complex Gaussian generation functions
+# These were mainly used for training data generation, not inference
+
+
+
+# generate_gaussian_array_vectorized_dist_l function removed - not used in current implementation
+@torch.inference_mode()
+def run_inference(model, input_tensor: torch.Tensor, device):
+    input_tensor = input_tensor.to(device).to(memory_format=torch.channels_last)
+    output = model.module().forward(input_tensor)
+    return output
+
+def preprocess_batch_fast(frames):
+    """Ultra-fast batch preprocessing using optimized tensor operations"""
+    target_size = (540, 960)  # H, W format for model input
+    batch = []
+    for i, frame in enumerate(frames):
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img = cv2.resize(frame_rgb, (target_size[1], target_size[0]))
+        img = img.astype(np.float32) / 255.0
+        img = np.transpose(img, (2, 0, 1))  # HWC -> CHW
+        batch.append(img)
+    batch = torch.from_numpy(np.stack(batch)).float()
+
+    return batch
+
+def extract_keypoints_from_heatmap(heatmap: torch.Tensor, scale: int = 2, max_keypoints: int = 1):
+    """Optimized keypoint extraction from heatmaps"""
+    batch_size, n_channels, height, width = heatmap.shape
+    
+    # Find local maxima using max pooling (keep on GPU)
+    kernel = 3
+    pad = 1
+    max_pooled = F.max_pool2d(heatmap, kernel, stride=1, padding=pad)
+    local_maxima = (max_pooled == heatmap)
+    heatmap = heatmap * local_maxima
+    
+    # Get top keypoints (keep on GPU longer)
+    scores, indices = torch.topk(heatmap.view(batch_size, n_channels, -1), max_keypoints, sorted=False)
+    y_coords = torch.div(indices, width, rounding_mode="floor")
+    x_coords = indices % width
+    
+    # Optimized tensor operations
+    x_coords = x_coords * scale
+    y_coords = y_coords * scale
+    
+    # Create result tensor directly on GPU
+    results = torch.stack([x_coords.float(), y_coords.float(), scores], dim=-1)
+    
+    return results
+
+
+def extract_keypoints_from_heatmap_fast(heatmap: torch.Tensor, scale: int = 2, max_keypoints: int = 1):
+    """Ultra-fast keypoint extraction optimized for speed"""
+    batch_size, n_channels, height, width = heatmap.shape
+    
+    # Simplified local maxima detection (faster but slightly less accurate)
+    max_pooled = F.max_pool2d(heatmap, 3, stride=1, padding=1)
+    local_maxima = (max_pooled == heatmap)
+    
+    # Apply mask and get top keypoints in one go
+    masked_heatmap = heatmap * local_maxima
+    flat_heatmap = masked_heatmap.view(batch_size, n_channels, -1)
+    scores, indices = torch.topk(flat_heatmap, max_keypoints, dim=-1, sorted=False)
+    
+    # Vectorized coordinate calculation
+    y_coords = torch.div(indices, width, rounding_mode="floor") * scale
+    x_coords = (indices % width) * scale
+    
+    # Stack results efficiently
+    results = torch.stack([x_coords.float(), y_coords.float(), scores], dim=-1)
+    return results
+
+
+def process_keypoints_vectorized(kp_coords, kp_threshold, w, h, batch_size):
+    """Ultra-fast vectorized keypoint processing"""
+    batch_results = []
+    
+    # Convert to numpy once for faster CPU operations
+    kp_np = kp_coords.cpu().numpy()
+    
+    for batch_idx in range(batch_size):
+        kp_dict = {}
+        # Vectorized threshold check
+        valid_kps = kp_np[batch_idx, :, 0, 2] > kp_threshold
+        valid_indices = np.where(valid_kps)[0]
+        
+        for ch_idx in valid_indices:
+            x = float(kp_np[batch_idx, ch_idx, 0, 0]) / w
+            y = float(kp_np[batch_idx, ch_idx, 0, 1]) / h
+            p = float(kp_np[batch_idx, ch_idx, 0, 2])
+            kp_dict[ch_idx + 1] = {'x': x, 'y': y, 'p': p}
+        
+        batch_results.append(kp_dict)
+    
+    return batch_results
+
+def inference_batch(frames, model, kp_threshold, device, batch_size=8):
+    """Optimized batch inference for multiple frames"""
+    results = []
+    num_frames = len(frames)
+    
+    # Get the device from the model itself
+    model_device = next(model.parameters()).device
+    
+    # Process all frames in optimally-sized batches
+    for i in range(0, num_frames, batch_size):
+        current_batch_size = min(batch_size, num_frames - i)
+        batch_frames = frames[i:i + current_batch_size]
+        
+        # Fast preprocessing - create on CPU first
+        batch = preprocess_batch_fast(batch_frames)
+        b, c, h, w = batch.size()
+        
+        # Move batch to model device
+        batch = batch.to(model_device)
+
+        with torch.no_grad():
+            heatmaps = model(batch)
+
+        # Ultra-fast keypoint extraction
+        kp_coords = extract_keypoints_from_heatmap_fast(heatmaps[:,:-1,:,:], scale=2, max_keypoints=1)
+        
+        # Vectorized batch processing - no loops
+        batch_results = process_keypoints_vectorized(kp_coords, kp_threshold, 960, 540, current_batch_size)
+        results.extend(batch_results)
+        
+        # Minimal cleanup
+        del heatmaps, kp_coords, batch
+    
+    return results
+
+# Keypoint mapping from detection indices to standard football pitch keypoint IDs
+map_keypoints = {
+    1: 1, 2: 14, 3: 25, 4: 2, 5: 10, 6: 18, 7: 26, 8: 3, 9: 7, 10: 23, 
+    11: 27, 20: 4, 21: 8, 22: 24, 23: 28, 24: 5, 25: 13, 26: 21, 27: 29, 
+    28: 6, 29: 17, 30: 30, 31: 11, 32: 15, 33: 19, 34: 12, 35: 16, 36: 20, 
+    45: 9, 50: 31, 52: 32, 57: 22
+}
+
+def get_mapped_keypoints(kp_points):
+    """Apply keypoint mapping to detection results"""
+    mapped_points = {}
+    for key, value in kp_points.items():
+        if key in map_keypoints:
+            mapped_key = map_keypoints[key]
+            mapped_points[mapped_key] = value
+        # else:
+            # Keep unmapped keypoints with original key
+            # mapped_points[key] = value
+    return mapped_points
+
+def process_batch_input(frames, model, kp_threshold, device, batch_size=16):
+    """Process multiple input images in batch"""
+    # Batch inference
+    kp_results = inference_batch(frames, model, kp_threshold, device, batch_size)
+    kp_results = [get_mapped_keypoints(kp) for kp in kp_results]
+
+    return kp_results