From 109173d55519d0497d48249d60652ce4124dc0d5 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Mon, 2 Sep 2019 16:22:13 +0200
Subject: [PATCH] updates

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
---
 detect.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/detect.py b/detect.py
index 43c76761..f48d1c48 100644
--- a/detect.py
+++ b/detect.py
@@ -9,8 +9,8 @@ from utils.utils import *
 
 def detect(save_txt=False, save_img=True, stream_img=False):
     img_size = (320, 192) if ONNX_EXPORT else opt.img_size  # (320, 192) or (416, 256) or (608, 352) for (height, width)
-    webcam = opt.source == '0' or opt.source.startswith('rtsp') or opt.source.startswith('http')
-    out = opt.output
+    out, source, weights, half = opt.output, opt.source, opt.weights, opt.half
+    webcam = source == '0' or source.startswith('rtsp') or source.startswith('http')
 
     # Initialize
     device = torch_utils.select_device(force_cpu=ONNX_EXPORT)
@@ -22,10 +22,10 @@ def detect(save_txt=False, save_img=True, stream_img=False):
     model = Darknet(opt.cfg, img_size)
 
     # Load weights
-    if opt.weights.endswith('.pt'):  # pytorch format
-        model.load_state_dict(torch.load(opt.weights, map_location=device)['model'])
+    if weights.endswith('.pt'):  # pytorch format
+        model.load_state_dict(torch.load(weights, map_location=device)['model'])
     else:  # darknet format
-        _ = load_darknet_weights(model, opt.weights)
+        _ = load_darknet_weights(model, weights)
 
     # Fuse Conv2d + BatchNorm2d layers
     # model.fuse()
@@ -41,8 +41,8 @@ def detect(save_txt=False, save_img=True, stream_img=False):
         return
 
     # Half precision
-    opt.half = opt.half and device.type != 'cpu'  # half precision only supported on CUDA
-    if opt.half:
+    half = half and device.type != 'cpu'  # half precision only supported on CUDA
+    if half:
         model.half()
 
     # Set Dataloader
@@ -50,9 +50,9 @@ def detect(save_txt=False, save_img=True, stream_img=False):
     if webcam:
         save_img = False
         stream_img = True
-        dataset = LoadWebcam(opt.source, img_size=img_size, half=opt.half)
+        dataset = LoadWebcam(source, img_size=img_size, half=half)
     else:
-        dataset = LoadImages(opt.source, img_size=img_size, half=opt.half)
+        dataset = LoadImages(source, img_size=img_size, half=half)
 
     # Get classes and colors
     classes = load_classes(parse_data_cfg(opt.data)['names'])
@@ -93,7 +93,7 @@ def detect(save_txt=False, save_img=True, stream_img=False):
 
         # Stream results
         if stream_img:
-            cv2.imshow(opt.weights, im0)
+            cv2.imshow(weights, im0)
 
         # Save results (image with detections)
         if save_img: