From a50f98679f9084b39f6d45d0ef87e086e1eca077 Mon Sep 17 00:00:00 2001 From: LutaoChu <30695251+LutaoChu@users.noreply.github.com> Date: Wed, 4 Dec 2019 19:57:21 +0800 Subject: [PATCH] update all docs involving color label (#109) --- ...63d7e6db0d03137883772c246c6761fc201059.png | Bin 4277 -> 5356 bytes .../labelme_demo/annotations/2011_000025.png | Bin 1852 -> 2713 bytes docs/data_prepare.md | 9 +++++---- docs/imgs/annotation/image-7.png | Bin 158599 -> 165132 bytes docs/imgs/annotation/jingling-5.png | Bin 115167 -> 130478 bytes docs/imgs/tensorboard_image.JPG | Bin 122983 -> 642267 bytes docs/imgs/tensorboard_scalar.JPG | Bin 198077 -> 355409 bytes docs/imgs/usage_vis_demo.jpg | Bin 66550 -> 1948808 bytes docs/imgs/usage_vis_demo2.jpg | Bin 34246 -> 0 bytes docs/imgs/usage_vis_demo3.jpg | Bin 93622 -> 0 bytes docs/usage.md | 7 ++----- pdseg/vis.py | 4 ++-- 12 files changed, 9 insertions(+), 11 deletions(-) delete mode 100644 docs/imgs/usage_vis_demo2.jpg delete mode 100644 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saved_model/unet_pet/final ``` -执行上述脚本后,会在主目录下产生一个visual/visual_results文件夹,里面存放着测试集图片的预测结果,我们选择其中几张图片进行查看,可以看到,在测试集中的图片上的预测效果已经很不错: +执行上述脚本后,会在主目录下产生一个visual文件夹,里面存放着测试集图片的预测结果,我们选择其中几张图片进行查看,可以看到,在测试集中的图片上的预测效果已经很不错: ![](./imgs/usage_vis_demo.jpg) -![](./imgs/usage_vis_demo2.jpg) -![](./imgs/usage_vis_demo3.jpg) `NOTE` -1. 可视化的图片会默认保存在visual/visual_results目录下,可以通过`--vis_dir`来指定输出目录 +1. 可视化的图片会默认保存在visual目录下,可以通过`--vis_dir`来指定输出目录 2. 训练过程中会使用DATASET.VIS_FILE_LIST中的图片进行可视化显示,而vis.py则会使用DATASET.TEST_FILE_LIST diff --git a/pdseg/vis.py b/pdseg/vis.py index fc524d91..d94221c0 100644 --- a/pdseg/vis.py +++ b/pdseg/vis.py @@ -87,7 +87,7 @@ def to_png_fn(fn): def visualize(cfg, vis_file_list=None, use_gpu=False, - vis_dir="visual_predict", + vis_dir="visual", ckpt_dir=None, log_writer=None, local_test=False, @@ -117,7 +117,7 @@ def visualize(cfg, fluid.io.load_params(exe, ckpt_dir, main_program=test_prog) - save_dir = os.path.join('visual', vis_dir) + save_dir = vis_dir makedirs(save_dir) fetch_list = [pred.name] -- GitLab

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