From 1930a23507e96c1e68b5b3c657cd21b765c7ee95 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=99=BD=E9=B9=AD=E5=85=88=E7=94=9F?= <766529835@qq.com>
Date: Wed, 8 Feb 2023 12:07:23 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E6=98=BE=E7=A4=BA?=
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---
 utils/callbacks.py | 4 ++--
 yolo.py            | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/utils/callbacks.py b/utils/callbacks.py
index a703585..b866781 100644
--- a/utils/callbacks.py
+++ b/utils/callbacks.py
@@ -148,9 +148,9 @@ class EvalCallback():
             top_label   = np.array(results[0][:, 7], dtype = 'int32')
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
-            top_rboxes[:, [0, 2]]  *= image_shape[1]
-            top_rboxes[:, [1, 3]]  *= image_shape[0]
             top_polys   = rbox2poly(top_rboxes)
+            top_polys[:, 0::2] *= image_shape[1]
+            top_polys[:, 1::2] *= image_shape[0]
             top_hbbs    = poly2hbb(top_polys)
         top_100     = np.argsort(top_conf)[::-1][:self.max_boxes]
         top_hbbs    = top_hbbs[top_100]
diff --git a/yolo.py b/yolo.py
index 28bb9dd..d538f58 100644
--- a/yolo.py
+++ b/yolo.py
@@ -157,9 +157,9 @@ class YOLO(object):
             top_label   = np.array(results[0][:, 7], dtype = 'int32')
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
-            top_rboxes[:, [0, 2]]  *= image_shape[1]
-            top_rboxes[:, [1, 3]]  *= image_shape[0]
             top_polys   = rbox2poly(top_rboxes)
+            top_polys[:, 0::2] *= image_shape[1]
+            top_polys[:, 1::2] *= image_shape[0]
         #---------------------------------------------------------#
         #   设置字体与边框厚度
         #---------------------------------------------------------#
@@ -378,9 +378,9 @@ class YOLO(object):
             top_label   = np.array(results[0][:, 7], dtype = 'int32')
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
-            top_rboxes[:, [0, 2]]  *= image_shape[1]
-            top_rboxes[:, [1, 3]]  *= image_shape[0]
             top_polys   = rbox2poly(top_rboxes)
+            top_polys[:, 0::2] *= image_shape[1]
+            top_polys[:, 1::2] *= image_shape[0]
             top_hbbs    = poly2hbb(top_polys)
         for i, c in list(enumerate(top_label)):
             predicted_class = self.class_names[int(c)]
-- 
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