add serving demo

deploy/paddle-serving/README.md

@@ -6,7 +6,7 @@ PaddleServing是Paddle的在线预测服务框架，可以快速部署训练好
 
 ## 2.安装Paddle Serving
 
-目前PaddleServing的正式版本为0.3.2版本，本文中的示例需要develop版本的paddle_serving_app，请从[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md#app)中下载并安装。
+目前PaddleServing的正式版本为0.3.2版本。
 
 服务端安装：
 
@@ -47,41 +47,18 @@ python pdseg/export_serving_model.py --cfg configs/unet_optic.yaml TEST.TEST_MOD
 
 `freeze_model/serving_server`目录下包含了模型文件和serving server端配置文件，`freeze_model/serving_client`目录下包含了serving client端配置文件。
 
+分别将serving_server和serving_client复制到server和client启动的路径下。
+
 ## 4.部署预测服务
 
 ```shell
-python -m paddle_serving_server.serve --model unet_model/ --port 9494 # CPU
-python -m paddle_serving_server_gpu.serve --model unet_model --port 9494 --gpu_ids 0 #GPU
+python -m paddle_serving_server.serve --model serving_server/ --port 9494 # CPU
+python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0 #GPU
 ```
 
 ## 5.执行预测
-
-```python
-#seg_client.py
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, File2Image, Resize, Transpose, BGR2RGB, SegPostprocess, Normalize, Div
-import sys
-import cv2
-
-client = Client()
-client.load_client_config("unet_client/serving_client_conf.prototxt")
-client.connect(["127.0.0.1:9494"])
-
-preprocess = Sequential([
-    File2Image(), Resize(
-        (512, 512), interpolation=cv2.INTER_LINEAR), Div(255.0),
-    Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5], False), Transpose((2, 0, 1))
-])
-
-postprocess = SegPostprocess(2)
-
-filename = "N0060.jpg"
-im = preprocess(filename)
-fetch_map = client.predict(feed={"image": im}, fetch=["transpose_1.tmp_0"])
-fetch_map["filename"] = filename
-postprocess(fetch_map)
+```shell
+python seg_client.py ../../dataset/optic_disc_seg/JPEGImages/N0060.jpg
 ```
-
-脚本执行之后，当前目录下生成处理后的图片。
-
-完整的部署示例请参考PaddleServing的[unet示例](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/unet_for_image_seg)。
+脚本执行之后，会在输入图片所在的目录下生成处理后的图片
+示例中为`../../dataset/optic_disc_seg/JPEGImages/N0060_jpg_mask.png`和`../../dataset/optic_disc_seg/JPEGImages/N0060_jpg_result.png`

deploy/paddle-serving/postprocess.py

+import cv2
+import numpy as np
+
+
+def generate_colormap(num_classes):
+    color_map = num_classes * [0, 0, 0]
+    for i in range(0, num_classes):
+        j = 0
+        lab = i
+        while lab:
+            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+            j += 1
+            lab >>= 3
+    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
+    return color_map
+
+
+class SegPostprocess(object):
+    def __init__(self, class_num):
+        self.class_num = class_num
+
+    def __call__(self, image_with_result):
+        if "filename" not in image_with_result:
+            raise ("filename should be specified in postprocess")
+        img_name = image_with_result["filename"]
+        ori_img = cv2.imread(img_name, -1)
+        ori_shape = ori_img.shape
+        mask = None
+        for key in image_with_result:
+            if ".lod" in key or "filename" in key:
+                continue
+            mask = image_with_result[key][0]
+        if mask is None:
+            raise ("segment mask should be specified in postprocess")
+        mask = np.argmax(mask, axis=0)
+        mask = mask.astype("uint8")
+        mask_png = mask
+        score_png = mask_png[:, :, np.newaxis]
+        score_png = np.concatenate([score_png] * 3, axis=2)
+
+        color_map = generate_colormap(self.class_num)
+        for i in range(score_png.shape[0]):
+            for j in range(score_png.shape[1]):
+                score_png[i, j] = color_map[score_png[i, j, 0]]
+        ext_pos = img_name.rfind(".")
+        img_name_fix = img_name[:ext_pos] + "_" + img_name[ext_pos + 1:]
+        mask_save_name = img_name_fix + "_mask.png"
+        cv2.imwrite(mask_save_name, mask_png, [cv2.CV_8UC1])
+        vis_result_name = img_name_fix + "_result.png"
+        result_png = score_png
+
+        result_png = cv2.resize(result_png, (ori_shape[1], ori_shape[0]),
+                                fx=0,
+                                fy=0,
+                                interpolation=cv2.INTER_CUBIC)
+        cv2.imwrite(vis_result_name, result_png, [cv2.CV_8UC1])

deploy/paddle-serving/seg_client.py

+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize, Transpose, BGR2RGB, Normalize, Div
+import sys
+import cv2
+from postprocess import SegPostprocess
+
+client = Client()
+client.load_client_config("serving_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:9494"])
+
+preprocess = Sequential([
+    File2Image(),
+    Resize((512, 512), interpolation=cv2.INTER_LINEAR),
+    Div(255.0),
+    Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5], False),
+    Transpose((2, 0, 1))
+])
+
+postprocess = SegPostprocess(2)
+
+filename = sys.argv[1]
+im = preprocess(filename)
+fetch_map = client.predict(feed={"image": im}, fetch=["transpose_1.tmp_0"])
+fetch_map["filename"] = filename
+postprocess(fetch_map)