Update models/object_detection.py

models/object_detection.py

@@ -1,33 +1,40 @@
-from transformers import pipeline
-import cv2
+from transformers import AutoModelForObjectDetection, AutoImageProcessor
+import torch
+from PIL import Image
 
-# Use Hugging Face's pipeline for object detection
-def load_yolov5():
-    # Load pre-trained YOLOv5 model for object detection (use Hugging Face-compatible model if YOLOv5 is available)
-    model = pipeline("object-detection", model="ultralytics/yolov5")  # Hugging Face hosted YOLOv5 model (if available)
-    return model
+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(video):
+def detect_faults_from_huggingface(image_path):
     """
-    Detect faults like cracks, dirt, etc. in the given video using YOLOv5 model.
+    Detect faults in the given image using Hugging Face's model (DETR in this case).
     Args:
-    - video (cv2.VideoCapture): Input video or image file
-
+    - image_path (str): Path to the image file
+    
     Returns:
-    - result (list): List of detected faults with confidence scores
+    - results (list): Detected objects and their confidence scores.
     """
-    model = load_yolov5()  # Load the model
-    faults = []
-    while True:
-        ret, frame = video.read()
-        if not ret:
-            break
-        # Run the model on the frame
-        results = model(frame)
-        result = results  # Assuming the result contains detected faults with bounding boxes and labels
-        faults.append(result)
+    model, processor = load_huggingface_model()
+    
+    # Load image
+    image = Image.open(image_path)
+
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
     
-    video.release()
-    return faults
+    # 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