Чтобы определить, является ли ребенок гуманоидом, вы можете использовать различные методы в зависимости от используемого вами языка программирования или платформы. Вот несколько примеров на разных языках:

1. Python (с использованием библиотеки PyTorch):

   import torch
   from torchvision.models import resnet50
   def is_child_humanoid(image_path):
   # Load pre-trained ResNet50 model
   model = resnet50(pretrained=True)
   # Set the model to evaluation mode
   model.eval()
   # Load and preprocess the image
   image = Image.open(image_path)
   preprocess = transforms.Compose([
       transforms.Resize(256),
       transforms.CenterCrop(224),
       transforms.ToTensor(),
       transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
   ])
   input_tensor = preprocess(image)
   input_batch = input_tensor.unsqueeze(0)
   # Move the input to the GPU if available
   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   input_batch = input_batch.to(device)
   # Perform the inference
   with torch.no_grad():
       output = model(input_batch)
   # Get the predicted label (here assuming index 0 represents humanoid)
   _, predicted_idx = torch.max(output, 1)
   label = predicted_idx.item()
   # Return True if the predicted label is 0, indicating a humanoid
   return label == 0

2. JavaScript (с использованием TensorFlow.js):

   import * as tf from '@tensorflow/tfjs';
   import * as mobilenet from '@tensorflow-models/mobilenet';
   async function isChildHumanoid(imageElement) {
   // Load the MobileNet model
   const model = await mobilenet.load();
   // Preprocess the image
   const image = tf.browser.fromPixels(imageElement);
   const processedImage = tf.image.resizeBilinear(image, [224, 224]);
   const batchedImage = processedImage.expandDims(0).toFloat().div(127.5).sub(1);
   // Perform the inference
   const predictions = await model.classify(batchedImage);
   
   // Get the predicted label (here assuming index 0 represents humanoid)
   const label = predictions[0].className;
   // Return true if the predicted label contains the word "humanoid"
   return label.toLowerCase().includes('humanoid');
   }

3. Java (с использованием библиотеки Deeplearning4j):

   import org.datavec.image.loader.NativeImageLoader;
   import org.deeplearning4j.nn.graph.ComputationGraph;
   import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
   import org.nd4j.linalg.api.ndarray.INDArray;
   public class HumanoidDetector {
   private static final String MODEL_PATH = "path/to/model.h5";
   public static boolean isChildHumanoid(String imagePath) throws Exception {
       // Load the Keras model
       ComputationGraph model = KerasModelImport.importKerasModelAndWeights(MODEL_PATH);
       // Load and preprocess the image
       NativeImageLoader loader = new NativeImageLoader(224, 224, 3);
       INDArray image = loader.asMatrix(imagePath);
       image.divi(255); // Normalize pixel values
       // Perform the inference
       INDArray[] output = model.output(false, image);
       // Get the predicted label (here assuming index 0 represents humanoid)
       int predictedLabel = output[0].argMax().getInt(0);
       // Return true if the predicted label is 0, indicating a humanoid
       return predictedLabel == 0;
   }
   }