Fix warning at startup due to uninitilized memory

yoeo/detect.py

@@ -86,18 +86,18 @@
     :type nms_thres: float, optional
     :param robot_class_ids: List of class IDs of robot classes if multiple robot classes exist.
     :type robot_class_ids: List[int], optional
     :return: Detections on image with each detection in the format: [x1, y1, x2, y2, confidence, class], Segmentation as 2d numpy array with the coresponding class id in each cell
     :rtype: nd.array, nd.array
     """
     model.eval()  # Set model to evaluation mode
 
     # Configure input
     input_img = transforms.Compose([
         DEFAULT_TRANSFORMS,
         Resize(img_size)])((
             image,
-            np.empty((1, 5)),
-            np.empty((img_size, img_size), dtype=np.uint8)))[0].unsqueeze(0)
+            np.zeros((1, 5)),
+            np.zeros((img_size, img_size), dtype=np.uint8)))[0].unsqueeze(0)
 
     if torch.cuda.is_available():
         input_img = input_img.to("cuda")
@@ -199,7 +199,7 @@
     """
     # Create plot
     img = cv2.imread(image_path)
     img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
     plt.figure()
     fig, ax = plt.subplots(1)
     # Get segmentation
@@ -211,22 +211,22 @@
     pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape[:2])) // 2
 
 
     seg_map = seg[
                 int(pad_y) : int(img_size - pad_y),
                 int(pad_x) : int(img_size - pad_x),
                 ] * 255
 
 
     ax.imshow(
         SegmentationMapsOnImage(
             seg[
                 int(pad_y) : int(img_size - pad_y),
                 int(pad_x) : int(img_size - pad_x),
                 ], shape=img.shape).draw_on_image(img)[0])
     # Rescale boxes to original image
     detections = rescale_boxes(detections, img_size, img.shape[:2])
     unique_labels = detections[:, -1].cpu().unique()
     n_cls_preds = len(unique_labels)
     # Bounding-box colors
     cmap = plt.get_cmap("tab20b")
     colors = [cmap(i) for i in np.linspace(0, 1, len(classes))]