Update app.py

app.py

@@ -82,14 +82,30 @@ def detect_objects(image):
     model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
 
     inputs = processor(images=image, return_tensors="pt")
-
     outputs = model(**inputs)
 
     target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    detected_cars = []
+    draw = ImageDraw.Draw(image)
+
+    # Loop through detections and filter only "car" class (ID 3 for COCO dataset)
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        if model.config.id2label[label.item()] == 'car' and score.item() > 0.9:
+            box = [round(i, 2) for i in box.tolist()]
+            # Crop the detected car
+            cropped_car = image.crop(box)
+            # Convert the cropped image to bytes
+            cropped_car_bytes = image_to_bytes(cropped_car)
+            detected_cars.append((cropped_car_bytes, box))
+
+            # Draw bounding box around the car
+            draw.rectangle(box, outline="red", width=3)
+            draw.text((box[0], box[1]), f"Car: {round(score.item(), 2)}", fill="red")
     
     return image, detected_cars
 
-
 def image_to_bytes(img):
     # Convert a PIL image to bytes
     from io import BytesIO