From 7283f52d0c4ddbdca33411a8fcaa1fd7a38c4eb2 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Wed, 21 Nov 2018 19:10:10 +0100
Subject: [PATCH] updates

---
 detect.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/detect.py b/detect.py
index 90edd2c0..827389bd 100755
--- a/detect.py
+++ b/detect.py
@@ -7,6 +7,7 @@ from utils.utils import *
 
 cuda = torch.cuda.is_available()
 device = torch.device('cuda:0' if cuda else 'cpu')
+f_path = os.path.dirname(os.path.realpath(__file__)) + '/'
 
 parser = argparse.ArgumentParser()
 # Get data configuration
@@ -16,8 +17,8 @@ parser.add_argument('-output_folder', type=str, default='output', help='path to
 parser.add_argument('-plot_flag', type=bool, default=True)
 parser.add_argument('-txt_out', type=bool, default=False)
 
-parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
-parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
+parser.add_argument('-cfg', type=str, default=f_path + 'cfg/yolov3.cfg', help='cfg file path')
+parser.add_argument('-class_path', type=str, default=f_path + 'data/coco.names', help='path to class label file')
 parser.add_argument('-conf_thres', type=float, default=0.50, help='object confidence threshold')
 parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
 parser.add_argument('-batch_size', type=int, default=1, help='size of the batches')
@@ -25,6 +26,7 @@ parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each i
 opt = parser.parse_args()
 print(opt)
 
+
 def main(opt):
     os.system('rm -rf ' + opt.output_folder)
     os.makedirs(opt.output_folder, exist_ok=True)
@@ -32,12 +34,12 @@ def main(opt):
     # Load model
     model = Darknet(opt.cfg, opt.img_size)
 
-    weights_path = 'weights/yolov3.pt'
+    weights_path = f_path + 'weights/yolov3.pt'
     if weights_path.endswith('.weights'):  # saved in darknet format
         load_weights(model, weights_path)
     else:  # endswith('.pt'), saved in pytorch format
-        if weights_path == 'weights/yolov3.pt' and not os.path.isfile(weights_path):
-            os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -P weights')
+        if weights_path.endswith('weights/yolov3.pt') and not os.path.isfile(weights_path):
+            os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights_path)
 
         checkpoint = torch.load(weights_path, map_location='cpu')
         model.load_state_dict(checkpoint['model'])
@@ -63,8 +65,8 @@ def main(opt):
     imgs = []  # Stores image paths
     img_detections = []  # Stores detections for each image index
     prev_time = time.time()
-    for batch_i, (img_paths, img) in enumerate(dataloader):
-        print(batch_i, img.shape, end=' ')
+    for i, (img_paths, img) in enumerate(dataloader):
+        print('%g/%g' % (i + 1, len(dataloader)), end=' ')
 
         # Get detections
         with torch.no_grad():
@@ -76,7 +78,7 @@ def main(opt):
                 img_detections.extend(detections)
                 imgs.extend(img_paths)
 
-        print('Batch %d... (Done %.3f s)' % (batch_i, time.time() - prev_time))
+        print('Batch %d... Done. (%.3fs)' % (i, time.time() - prev_time))
         prev_time = time.time()
 
     # Bounding-box colors