image_classification.py

# Build Semantic Segmentation
import torch
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image

# Load the pre-trained ResNet-50 model trained on ImageNet
model = models.resnet50(pretrained=True) # replace with face mask detection, gesture detection

# Set the model to evaluation mode
model.eval()

# Define the transformations for preprocessing the input image
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]),
])

# Load and preprocess the input image
input_image = Image.open("input_image.jpg")
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)  # Add a batch dimension

# Check if GPU is available and move the input batch to the GPU if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')

# Perform inference
with torch.no_grad():
    output = model(input_batch)

# Load the labels for ImageNet classes
with open("imagenet_classes.txt") as f:
    labels = [line.strip() for line in f.readlines()]

# Get the predicted class index
predicted_index = torch.argmax(output[0]).item()
predicted_label = labels[predicted_index]

# Print the predicted label
print("Predicted label:", predicted_label)