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from transformers import AutoModelForObjectDetection, AutoImageProcessor
import torch
from PIL import Image
def load_huggingface_model():
"""
Load a pre-trained object detection model from Hugging Face.
For example, we are using Facebook's DETR (Detection Transformer).
"""
# Load a Hugging Face pre-trained model for object detection
model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")
processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
return model, processor
def detect_faults_from_huggingface(image_path):
"""
Detect faults in the given image using Hugging Face's model (DETR in this case).
Args:
- image_path (str): Path to the image file
Returns:
- results (list): Detected objects and their confidence scores.
"""
model, processor = load_huggingface_model()
# Load image
image = Image.open(image_path)
# Preprocess the image
inputs = processor(images=image, return_tensors="pt")
# Run the model
outputs = model(**inputs)
# Post-process the output to get detections
target_sizes = torch.tensor([image.size[::-1]]) # Reversing the image size (height, width)
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
return results
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