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Running
on
Zero
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import torch
from .model import load_model
from utils.image_utils import preprocess_image, get_image_from_input
from utils.face_detector import load_face_detector
from .predict import predict_age
def age_estimation(input_type, uploaded_image, image_url, base64_string):
"""
Estimates the age from an image input via file, URL, or base64 string.
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:
tuple: A tuple containing:
- str: A summary string of the estimated ages, or an error message.
- list: A list of dictionaries, where each dictionary represents the age
estimation data for a detected face, or an empty list if no faces
were detected or an error occurred.
"""
# Use the centralized function to get the image
image = get_image_from_input(input_type, uploaded_image, image_url, base64_string)
if image is None:
print("Image is None after loading/selection for age estimation.")
return "Error: Image processing failed or no valid input provided.", []
try:
face_detector = load_face_detector()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model(device)
# Preprocess the image (convert PIL to numpy, ensure RGB)
processed_image = preprocess_image(image)
# Call predict_age with the processed image (NumPy array)
age_data = predict_age(processed_image, model, face_detector, device)
if age_data:
# Create a summary string of all estimated ages
age_summary = "Estimated Ages: " + ", ".join(
[str(face["age"]) for face in age_data]
)
return age_summary, age_data
else:
return "No faces detected", []
except Exception as e:
print(f"Error in age estimation: {e}")
return f"Error in age estimation: {e}", []
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