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import os
import sys
from PIL import Image
# Append the path to the inference script's directory
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'slimface', 'inference')))
from end2end_inference import cinference_and_confirm
def run_inference(image, reference_dict_path, index_to_class_mapping_path, model_path,
edgeface_model_name="edgeface_base", edgeface_model_dir="ckpts/idiap",
algorithm="yolo", accelerator="auto", resolution=224, similarity_threshold=0.6):
# Save uploaded image temporarily in apps/gradio_app/
temp_image_path = os.path.join(os.path.dirname(__file__), "temp_image.jpg")
image.save(temp_image_path)
# Create args object to mimic command-line arguments
class Args:
def __init__(self):
self.unknown_image_path = temp_image_path
self.reference_dict_path = reference_dict_path.name if reference_dict_path else None
self.index_to_class_mapping_path = index_to_class_mapping_path.name if index_to_class_mapping_path else None
self.model_path = model_path.name if model_path else None
self.edgeface_model_name = edgeface_model_name
self.edgeface_model_dir = edgeface_model_dir
self.algorithm = algorithm
self.accelerator = accelerator
self.resolution = resolution
self.similarity_threshold = similarity_threshold
args = Args()
# Validate inputs
if not all([args.reference_dict_path, args.index_to_class_mapping_path, args.model_path]):
return "Error: Please provide all required files (reference dict, index-to-class mapping, and model)."
try:
# Call the inference function from end2end_inference.py
results = cinference_and_confirm(args)
# Format output
output = ""
for result in results:
output += f"Image: {result['image_path']}\n"
output += f"Predicted Class: {result['predicted_class']}\n"
output += f"Confidence: {result['confidence']:.4f}\n"
output += f"Similarity: {result.get('similarity', 'N/A'):.4f}\n"
output += f"Confirmed: {result.get('confirmed', 'N/A')}\n\n"
return output
except Exception as e:
return f"Error: {str(e)}"
finally:
# Clean up temporary image
if os.path.exists(temp_image_path):
os.remove(temp_image_path) |