Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2018-09-05 14:59:49 +02:00
parent 0bfc4bcee3
commit af9864de7b
1 changed files with 5 additions and 4 deletions
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detect.py
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@ -18,8 +18,8 @@ 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('-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('-class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('-conf_thres', type=float, default=0.8, help='object confidence threshold') parser.add_argument('-conf_thres', type=float, default=0.99, help='object confidence threshold')
parser.add_argument('-nms_thres', type=float, default=0.5, help='iou threshold for non-maximum suppression') 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') parser.add_argument('-batch_size', type=int, default=1, help='size of the batches')
parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension') parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
opt = parser.parse_args() opt = parser.parse_args()
@ -33,7 +33,8 @@ def detect(opt):
# Load model # Load model
model = Darknet(opt.cfg, opt.img_size) model = Darknet(opt.cfg, opt.img_size)
weights_path = 'checkpoints/yolov3.weights' #weights_path = 'checkpoints/yolov3.weights'
weights_path = 'checkpoints/latest.pt'
if weights_path.endswith('.weights'): # saved in darknet format if weights_path.endswith('.weights'): # saved in darknet format
load_weights(model, weights_path) load_weights(model, weights_path)
else: # endswith('.pt'), saved in pytorch format else: # endswith('.pt'), saved in pytorch format
@ -130,7 +131,7 @@ def detect(opt):
if opt.plot_flag: if opt.plot_flag:
# Add the bbox to the plot # Add the bbox to the plot
label = '%s %.2f' % (classes[int(cls_pred)], cls_conf) if cls_conf > 0.05 else None label = '%s %.2f' % (classes[int(cls_pred)], conf)
color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])] color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
plot_one_box([x1, y1, x2, y2], img, label=label, color=color, line_thickness=3) plot_one_box([x1, y1, x2, y2], img, label=label, color=color, line_thickness=3)