From efd2b7d75dd17ad235f3dd70e27131d1ee9e4a84 Mon Sep 17 00:00:00 2001
From: Steffy-zxf <zhangxuefei@baidu.com>
Date: Tue, 23 Jun 2020 09:39:00 +0800
Subject: [PATCH] fix deeplabv3p_xception65_humanseg postprocess bug

---
 .../deeplabv3p_xception65_humanseg/module.py           | 10 +++++++++-
 .../deeplabv3p_xception65_humanseg/processor.py        |  5 +++--
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/module.py b/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/module.py
index b6f0f227..f2d59772 100644
--- a/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/module.py
+++ b/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/module.py
@@ -22,7 +22,7 @@ from deeplabv3p_xception65_humanseg.data_feed import reader
     author="baidu-vis",
     author_email="",
     summary="DeepLabv3+ is a semantic segmentation model.",
-    version="1.1.0")
+    version="1.1.1")
 class DeeplabV3pXception65HumanSeg(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(
@@ -220,3 +220,11 @@ class DeeplabV3pXception65HumanSeg(hub.Module):
         """
         self.arg_input_group.add_argument(
             '--input_path', type=str, help="path to image.")
+
+
+if __name__ == "__main__":
+    m = DeeplabV3pXception65HumanSeg()
+    import cv2
+    img = cv2.imread('./meditation.jpg')
+    res = m.segmentation(images=[img])
+    print(res[0]['data'])
diff --git a/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/processor.py b/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/processor.py
index 21566ae7..ce070412 100644
--- a/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/processor.py
+++ b/hub_module/modules/image/semantic_segmentation/deeplabv3p_xception65_humanseg/processor.py
@@ -52,8 +52,9 @@ def postprocess(data_out,
     for logit in data_out:
         logit = logit[1] * 255
         logit = cv2.resize(logit, (org_im_shape[1], org_im_shape[0]))
-        ret, logit = cv2.threshold(logit, thresh, 0, cv2.THRESH_TOZERO)
-        logit = 255 * (logit - thresh) / (255 - thresh)
+        logit -= thresh
+        logit[logit < 0] = 0
+        logit = 255 * logit / (255 - thresh)
         rgba = np.concatenate((org_im, np.expand_dims(logit, axis=2)), axis=2)
 
         if visualization:
-- 
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