Merge pull request #348 from ShenYuhan/serving_mask

update serving mask

Author: sjtubinlong

demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py

@@ -6,7 +6,7 @@
 
 if __name__ == "__main__":
     # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ]
-    file_list = ["../img/man.png"]
+    file_list = ["../../../../docs/imgs/man.png"]
     files = [("image", (open(item, "rb"))) for item in file_list]
     # 为每张图片对应指定info和style
     data = {"info": ["Male,Black_Hair"], "style": ["Bald"]}

demo/serving/module_serving/GAN_stgan_celeba/stgan_output/Bald_man.png

@@ -4,7 +4,9 @@
 
 if __name__ == "__main__":
     # 指定要预测的图片并生成列表[("image", img_1), ("image", img_2), ... ]
-    file_list = ["../img/cat.jpg", "../img/flower.jpg"]
+    file_list = [
+        "../../../../docs/imgs/cat.jpg", "../../../../docs/imgs/flower.jpg"
+    ]
     files = [("image", (open(item, "rb"))) for item in file_list]
     # 指定预测方法为vgg11_imagenet并发送post请求
     url = "http://127.0.0.1:8866/predict/image/vgg11_imagenet"

demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py

@@ -6,7 +6,9 @@
 
 if __name__ == "__main__":
     # 指定要检测的图片并生成列表[("image", img_1), ("image", img_2), ... ]
-    file_list = ["../img/cat.jpg", "../img/dog.jpg"]
+    file_list = [
+        "../../../../docs/imgs/cat.jpg", "../../../../docs/imgs/dog.jpg"
+    ]
     files = [("image", (open(item, "rb"))) for item in file_list]
     # 指定检测方法为yolov3_coco2017并发送post请求
     url = "http://127.0.0.1:8866/predict/image/yolov3_darknet53_coco2017"

demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/yolov3_darknet53_coco2017_serving_demo.py

@@ -6,7 +6,7 @@
 
 if __name__ == "__main__":
     # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ]
-    file_list = ["../img/girl.jpg"]
+    file_list = ["../../../../docs/imgs/girl.jpg"]
     files = [("image", (open(item, "rb"))) for item in file_list]
     # 指定图片分割方法为deeplabv3p_xception65_humanseg并发送post请求
     url = "http://127.0.0.1:8866/predict/image/deeplabv3p_xception65_humanseg"

demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py

@@ -9,7 +9,7 @@
 
 <div align="center">  
 
-<img src="../docs/imgs/bs.png" aligh="center" width="100%" alt="BS流程图" />  
+<img src="../imgs/bs.png" aligh="center" width="100%" alt="BS流程图" />  
 
 </div>  
 
@@ -203,7 +203,7 @@ result = bc.get_result(input_text=input_text)
 ```python
 [[0.9993321895599361, 0.9994612336158751, 0.9999646544456481, 0.732795298099517, -0.34387934207916204, ... ]]
 ```
-客户端代码demo文件见[示例](../demo/serving/bert_service/bert_service_client.py)。  
+客户端代码demo文件见[示例](../../demo/serving/bert_service/bert_service_client.py)。  
 运行命令如下：  
 ```shell
 $ python bert_service_client.py

demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/output/girl.png

@@ -81,7 +81,7 @@ http://0.0.0.0:8866/predict/<CATEGORY\>/\<MODULE>
 
 <p align="center">  
 
-<img src="../docs/imgs/web_demo.png" width="60%" />  
+<img src="../imgs/web_demo.png" width="60%" />  
 
 </p>  
 
@@ -117,7 +117,7 @@ $ hub serving start -c serving_config.json
 
 <p align="center">  
 
-<img src="../docs/imgs/start_serving_lac.png" width="100%" />  
+<img src="../imgs/start_serving_lac.png" width="100%" />  
 
 </p>  
 
@@ -171,41 +171,41 @@ if __name__ == "__main__":
 }
 ```
 
-此Demo的具体信息和代码请参见[LAC Serving](../demo/serving/module_serving/lexical_analysis_lac)。另外，下面展示了一些其他的一键服务部署Demo。
+此Demo的具体信息和代码请参见[LAC Serving](../../demo/serving/module_serving/lexical_analysis_lac)。另外，下面展示了一些其他的一键服务部署Demo。
 
 ## Demo——其他模型的一键部署服务
 
 获取其他PaddleHub Serving的一键服务部署场景示例，可参见下列demo
 
-* [图像分类-基于vgg11_imagent](../demo/serving/module_serving/classification_vgg11_imagenet)  
+* [图像分类-基于vgg11_imagent](../../demo/serving/module_serving/classification_vgg11_imagenet)  
 
 &emsp;&emsp;该示例展示了利用vgg11_imagent完成图像分类服务化部署和在线预测，获取图像分类结果。
 
-* [图像生成-基于stgan_celeba](../demo/serving/module_serving/GAN_stgan_celeba)  
+* [图像生成-基于stgan_celeba](../../demo/serving/module_serving/GAN_stgan_celeba)  
 
 &emsp;&emsp;该示例展示了利用stgan_celeba生成图像服务化部署和在线预测，获取指定风格的生成图像。
 
-* [文本审核-基于porn_detection_lstm](../demo/serving/module_serving/text_censorship_porn_detection_lstm)  
+* [文本审核-基于porn_detection_lstm](../../demo/serving/module_serving/text_censorship_porn_detection_lstm)  
 
 &emsp;&emsp;该示例展示了利用porn_detection_lstm完成中文文本黄色敏感信息鉴定的服务化部署和在线预测，获取文本是否敏感及其置信度。
 
-* [中文词法分析-基于lac](../demo/serving/module_serving/lexical_analysis_lac)
+* [中文词法分析-基于lac](../../demo/serving/module_serving/lexical_analysis_lac)
 
 &emsp;&emsp;该示例展示了利用lac完成中文文本分词服务化部署和在线预测，获取文本的分词结果，并可通过用户自定义词典干预分词结果。
 
-* [目标检测-基于yolov3_darknet53_coco2017](../demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017)  
+* [目标检测-基于yolov3_darknet53_coco2017](../../demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017)  
 
 &emsp;&emsp;该示例展示了利用yolov3_darknet53_coco2017完成目标检测服务化部署和在线预测，获取检测结果和覆盖识别框的图片。
 
-* [中文语义分析-基于simnet_bow](../demo/serving/module_serving/semantic_model_simnet_bow)
+* [中文语义分析-基于simnet_bow](../../demo/serving/module_serving/semantic_model_simnet_bow)
 
 &emsp;&emsp;该示例展示了利用simnet_bow完成中文文本相似度检测服务化部署和在线预测，获取文本的相似程度。  
 
-* [图像分割-基于deeplabv3p_xception65_humanseg](../demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg)
+* [图像分割-基于deeplabv3p_xception65_humanseg](../../demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg)
 
 &emsp;&emsp;该示例展示了利用deeplabv3p_xception65_humanseg完成图像分割服务化部署和在线预测，获取识别结果和分割后的图像。
 
-* [中文情感分析-基于simnet_bow](../demo/serving/module_serving/semantic_model_simnet_bow)
+* [中文情感分析-基于simnet_bow](../../demo/serving/module_serving/semantic_model_simnet_bow)
 
 &emsp;&emsp;该示例展示了利用senta_lstm完成中文文本情感分析服务化部署和在线预测，获取文本的情感分析结果。
 

docs/tutorial/bert_service.md

@@ -103,6 +103,8 @@ def __init__(self, name):
         self.parser.add_argument("--config", "-c", nargs="?")
         self.parser.add_argument("--port", "-p", nargs="?", default=8866)
         self.parser.add_argument("--gpu", "-i", nargs="?", default=0)
+        self.parser.add_argument(
+            "--use_singleprocess", action="store_true", default=False)
 
     def dump_pid_file(self):
         pid = os.getpid()
@@ -336,12 +338,15 @@ def start_multi_app_with_args(self):
 
     def start_serving(self):
         config_file = self.args.config
+        single_mode = self.args.use_singleprocess
         if config_file is not None:
             if os.path.exists(config_file):
                 with open(config_file, "r") as fp:
                     configs = json.load(fp)
                     use_multiprocess = configs.get("use_multiprocess", False)
-                    if platform.system() == "Windows":
+                    if single_mode is True:
+                        ServingCommand.start_single_app_with_file(configs)
+                    elif platform.system() == "Windows":
                         print(
                             "Warning: Windows cannot use multiprocess working "
                             "mode, PaddleHub Serving will switch to single process mode"
@@ -357,7 +362,9 @@ def start_serving(self):
             else:
                 print("config_file ", config_file, "not exists.")
         else:
-            if platform.system() == "Windows":
+            if single_mode is True:
+                self.start_single_app_with_args()
+            elif platform.system() == "Windows":
                 print(
                     "Warning: Windows cannot use multiprocess working "
                     "mode, PaddleHub Serving will switch to single process mode"

docs/tutorial/serving.md

@@ -22,9 +22,12 @@
 import multiprocessing
 import hashlib
 import platform
+import base64
 
 import paddle.fluid as fluid
 import six
+import numpy as np
+import cv2
 
 from paddlehub.module import module_desc_pb2
 from paddlehub.common.logger import logger
@@ -51,6 +54,42 @@ def version_compare(version1, version2):
     return len(version1) > len(version2)
 
 
+def base64s_to_cvmats(base64s):
+    for index, value in enumerate(base64s):
+        value = bytes(value, encoding="utf8")
+        value = base64.b64decode(value)
+        value = np.fromstring(value, np.uint8)
+        value = cv2.imdecode(value, 1)
+
+        base64s[index] = value
+    return base64s
+
+
+def handle_mask_results(results):
+    result = []
+    if len(results) <= 0:
+        return results
+    _id = results[0]["id"]
+    _item = {
+        "data": [],
+        "path": results[0].get("path", ""),
+        "id": results[0]["id"]
+    }
+    for item in results:
+        if item["id"] == _id:
+            _item["data"].append(item["data"])
+        else:
+            result.append(_item)
+            _id = _id + 1
+            _item = {
+                "data": [item["data"]],
+                "path": item.get("path", ""),
+                "id": item.get("id", _id)
+            }
+    result.append(_item)
+    return result
+
+
 def get_platform():
     return platform.platform()
 

paddlehub/commands/serving.py

@@ -18,6 +18,7 @@
 import os
 import base64
 import logging
+import shutil
 
 cv_module_method = {
     "vgg19_imagenet": "predict_classification",
@@ -47,7 +48,9 @@
     "faster_rcnn_coco2017": "predict_object_detection",
     "cyclegan_cityscapes": "predict_gan",
     "deeplabv3p_xception65_humanseg": "predict_semantic_segmentation",
-    "ace2p": "predict_semantic_segmentation"
+    "ace2p": "predict_semantic_segmentation",
+    "pyramidbox_lite_server_mask": "predict_mask",
+    "pyramidbox_lite_mobile_mask": "predict_mask"
 }
 
 
@@ -132,6 +135,59 @@ def predict_gan(module, input_img, id, batch_size, extra={}):
     return results_pack
 
 
+def predict_mask(module, input_img, id, batch_size, extra=None, r_img=False):
+    output_folder = "detection_result"
+    global use_gpu
+    method_name = module.desc.attr.map.data['default_signature'].s
+    predict_method = getattr(module, method_name)
+    try:
+        data = {}
+        if input_img is not None:
+            input_img = {"image": input_img}
+            data.update(input_img)
+        if extra is not None:
+            data.update(extra)
+            r_img = True if "r_img" in extra.keys() else False
+        results = predict_method(
+            data=data, use_gpu=use_gpu, batch_size=batch_size)
+        results = utils.handle_mask_results(results)
+    except Exception as err:
+        curr = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
+        print(curr, " - ", err)
+        return {"result": "Please check data format!"}
+    finally:
+        base64_list = []
+        results_pack = []
+        if input_img is not None:
+            if r_img is False:
+                shutil.rmtree(output_folder)
+                for index in range(len(results)):
+                    results[index]["path"] = ""
+                results_pack = results
+            else:
+                input_img = input_img.get("image", [])
+                for index in range(len(input_img)):
+                    item = input_img[index]
+                    with open(os.path.join(output_folder, item), "rb") as fp:
+                        b_head = "data:image/" + item.split(".")[-1] + ";base64"
+                        b_body = base64.b64encode(fp.read())
+                        b_body = str(b_body).replace("b'", "").replace("'", "")
+                        b_img = b_head + "," + b_body
+                        base64_list.append(b_img)
+                        results[index]["path"] = results[index]["path"].replace(
+                            id + "_", "") if results[index]["path"] != "" \
+                            else ""
+
+                        results[index].update({"base64": b_img})
+                        results_pack.append(results[index])
+                    os.remove(item)
+                    os.remove(os.path.join(output_folder, item))
+        else:
+            results_pack = results
+
+    return results_pack
+
+
 def predict_object_detection(module, input_img, id, batch_size, extra={}):
     output_folder = "detection_result"
     global use_gpu
@@ -253,14 +309,22 @@ def predict_image(module_name):
         extra_info = {}
         for item in list(request.form.keys()):
             extra_info.update({item: request.form.getlist(item)})
+
+        for key in extra_info.keys():
+            if isinstance(extra_info[key], list):
+                extra_info[key] = utils.base64s_to_cvmats(
+                    eval(extra_info[key][0])["b64s"]) if isinstance(
+                        extra_info[key][0], str
+                    ) and "b64s" in extra_info[key][0] else extra_info[key]
+
         file_name_list = []
         if img_base64 != []:
             for item in img_base64:
                 ext = item.split(";")[0].split("/")[-1]
                 if ext not in ["jpeg", "jpg", "png"]:
                     return {"result": "Unrecognized file type"}
                 filename = req_id + "_" \
-                           + utils.md5(str(time.time())+item[0:20]) \
+                           + utils.md5(str(time.time()) + item[0:20]) \
                            + "." \
                            + ext
                 img_data = base64.b64decode(item.split(',')[-1])
@@ -281,6 +345,10 @@ def predict_image(module_name):
             module_type = module.type.split("/")[-1].replace("-", "_").lower()
             predict_func = eval("predict_" + module_type)
         batch_size = batch_size_dict.get(module_name, 1)
+        if file_name_list == []:
+            file_name_list = None
+        if extra_info == {}:
+            extra_info = None
         results = predict_func(module, file_name_list, req_id, batch_size,
                                extra_info)
         r = {"results": str(results)}