From e6874b52073a225df7b5f033309d1b0e2216308b Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Mon, 12 Jul 2021 19:48:47 +0200
Subject: [PATCH] Alert (no detections) (#3984)

* `Detections()` class `print()` overload

* Update common.py
---
 models/common.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/models/common.py b/models/common.py
index 418034ddeaac..05372ae149f5 100644
--- a/models/common.py
+++ b/models/common.py
@@ -307,7 +307,7 @@ def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
     def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
         for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
             str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
-            if pred is not None:
+            if pred.shape[0]:
                 for c in pred[:, -1].unique():
                     n = (pred[:, -1] == c).sum()  # detections per class
                     str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
@@ -318,6 +318,8 @@ def display(self, pprint=False, show=False, save=False, crop=False, render=False
                             save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
                         else:  # all others
                             plot_one_box(box, im, label=label, color=colors(cls))
+            else:
+                str += '(no detections)'
 
             im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if pprint: