AI for Satellite-Based Crop Classification and Yield Forecasting

# Calculate PCK
def calculate_pck(predicted_keypoints, ground_truth_keypoints, threshold=0.2):
    # Calculate Euclidean distance between predicted and ground truth keypoints
    distances = torch.linalg.norm(predicted_keypoints - ground_truth_keypoints, dim=2)
    
    # Determine correct keypoints (distance < threshold)
    correct_keypoints = (distances < threshold).float()
    
    # Calculate PCK as mean of correct keypoints
    pck = correct_keypoints.mean().item()
    
    return pck

# Example usage
predicted = torch.rand(16, 17, 2)  # batch_size, num_keypoints, coordinates
ground_truth = torch.rand(16, 17, 2)
    
accuracy = calculate_pck(predicted, ground_truth)
print(f"PCK Accuracy: {accuracy:.4f}")