detection/object_detection.py

# Standard library imports
# (Add any necessary imports for future object detection implementation)

# Third-party imports
from PIL import Image
import numpy as np

# Local imports
from utils.image_utils import load_image, preprocess_image

def object_detection(input_type, uploaded_image, image_url, base64_string):
    """
    Performs object detection on the image from various input types.

    Args:
        input_type (str): The selected input method ("Upload File", "Enter URL", "Enter Base64").
        uploaded_image (PIL.Image.Image): The uploaded image (if input_type is "Upload File").
        image_url (str): The image URL (if input_type is "Enter URL").
        base64_string (str): The image base64 string (if input_type is "Enter Base64").

    Returns:
        numpy.ndarray: The image with detected objects, or None if an error occurred.
    """
    image = None
    input_value = None

    if input_type == "Upload File" and uploaded_image is not None:
        image = uploaded_image # This is a PIL Image
        print("Using uploaded image (PIL) for object detection") # Debug print

    elif input_type == "Enter URL" and image_url and image_url.strip():
        input_value = image_url
        print(f"Using URL for object detection: {input_value}") # Debug print

    elif input_type == "Enter Base64" and base64_string and base64_string.strip():
        input_value = base64_string
        print(f"Using Base64 string for object detection") # Debug print

    else:
        print("No valid input provided for object detection based on selected type.")
        return None # No valid input

    # If input_value is set (URL or Base64), use load_image
    if input_value:
        image = load_image(input_value)
        if image is None:
            return None # load_image failed

    # Now 'image' should be a PIL Image or None
    if image is None:
        print("Image is None after loading/selection for object detection.")
        return None

    try:
        # Preprocess the image (convert PIL to numpy, ensure RGB)
        # preprocess_image expects a PIL Image or something convertible by Image.fromarray
        processed_image = preprocess_image(image)

        # TODO: Implement object detection logic here
        # Currently just returns the processed image
        print("Object detection logic placeholder executed.")
        return processed_image
    except Exception as e:
        print(f"Error in object detection processing: {e}")
        return None