Update app.py

app.py

@@ -1,44 +1,32 @@
 import gradio as gr
 import torch
-import torchvision.transforms as transforms
-from torchvision.models.detection import detr
 from PIL import Image
-import cv2
-import numpy as np
+from torchvision.transforms import functional as F
+from transformers import DetrImageProcessor, DetrForObjectDetection
 
 # Load the pretrained DETR model
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = detr.DETR(resnet50=True)
-model = model.to(device).eval()
-
-# Define the transformation for the input image
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Resize((800, 800)),
-])
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
 
 # Define the object detection function
 def detect_objects(frame):
     # Convert the frame to PIL image
     image = Image.fromarray(frame)
 
-    # Apply the transformation
-    image = transform(image).unsqueeze(0).to(device)
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
 
     # Perform object detection
-    with torch.no_grad():
-        outputs = model(image)
+    outputs = model(**inputs)
 
-    # Get the bounding boxes and labels
-    boxes = outputs['pred_boxes'][0].cpu().numpy()
-    labels = outputs['pred_classes'][0].cpu().numpy()
+    # Convert outputs to COCO API format
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
 
     # Draw bounding boxes on the frame
-    for box, label in zip(boxes, labels):
-        box = [int(coord) for coord in box]
-        frame = cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
-        frame = cv2.putText(frame, f'Class: {label}', (box[0], box[1] - 10),
-                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA)
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        frame = gr.draw_box(frame, box, label=model.config.id2label[label.item()], color=(0, 255, 0))
 
     return frame