def _dump_processor(self):
import inspect
pymodule = inspect.getmodule(self.processor)
pycode = inspect.getsource(pymodule)
processor_path = self.helper.processor_path()
processor_md5 = utils.md5(pycode)
processor_md5 += str(time.time())
processor_name = utils.md5(processor_md5)
output_file = os.path.join(processor_path, processor_name + ".py")
utils.mkdir(processor_path)
with open(output_file, "w") as file:
file.write(pycode)
utils.from_pyobj_to_module_attr(
processor_name, self.desc.attr.map.data['processor_info'])
def get_hub_name(self):
if self.hub_name is None:
self.hub_name = md5(str(uuid.uuid1())[-12:]) + "-" + str(
int(time.time()))
with open(self.filepath, "w") as fp:
fp.write(json.dumps(default_server_config))
return self.hub_name
def predict_image(module_name):
if request.path.split("/")[-1] not in cv_module:
return {"error": "Module {} is not available.".format(module_name)}
req_id = request.data.get("id")
global use_gpu, batch_size_dict
img_base64 = request.form.getlist("image")
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]) \
+ "." \
+ ext
img_data = base64.b64decode(item.split(',')[-1])
file_name_list.append(filename)
with open(filename, "wb") as fp:
fp.write(img_data)
else:
file = request.files.getlist("image")
for item in file:
file_name = req_id + "_" + item.filename
item.save(file_name)
file_name_list.append(file_name)
module = default_module_manager.get_module(module_name)
predict_func_name = cv_module_method.get(module_name, "")
if predict_func_name != "":
predict_func = eval(predict_func_name)
else:
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)}
return r
def predict_iamge(module_name):
global results_dict
req_id = request.data.get("id")
img_base64 = request.form.get("image", "")
if img_base64 != "":
img_base64 = request.form.get("image", "")
ext = img_base64.split(";")[0].split("/")[-1]
if ext not in ["jpeg", "jpg", "png"]:
return {"result": "Unrecognized file type"}
filename = utils.md5(str(time.time()) +
str(img_base64)) + "." + ext
base64_head = img_base64.split(',')[0]
img_data = base64.b64decode(img_base64.split(',')[-1])
with open(filename, "wb") as fp:
fp.write(img_data)
else:
file = request.files["image"]
filename = file.filename
ext = file.filename.split(".")[-1]
if ext not in ["jpeg", "jpg", "png"]:
return {"result": "Unrecognized file type"}
base64_head = "data:image/" + ext + ";base64"
filename = utils.md5(filename) + '.' + ext
file.save(filename)
score = time.time()
file_list = [filename]
if queues_dict[module_name].qsize(
) + 1 > queues_dict[module_name].get_attribute("maxsize"):
return {"result": "Too many visitors now, please come back later."}
data_2_item(file_list, req_id, score, module_name)
data_num = len(file_list)
results = []
result_len = 0
start_time = time.time()
while result_len != data_num:
result_len = len(results_dict.get(req_id, []))
if time.time() - start_time > time_out:
results_dict.pop(req_id, None)
return {"result": "Request time out."}
results = results_dict.get(req_id)
results_dict.pop(req_id, None)
results = [i[1] for i in sorted(results, key=lambda k: k[0])]
filename = results[0].get("path")
ext = filename.split(".")[-1]
if filename is not None:
output_file = os.path.join("./output", filename)
if output_file is not None and os.path.exists(output_file):
with open(output_file, "rb") as fp:
output_img_base64 = base64.b64encode(fp.read())
os.remove(filename)
os.remove(output_file)
results = {
"desc":
str(results[0]["data"]),
"output_img":
base64_head + "," +
str(output_img_base64).replace("b'", "").replace("'", "")
}
return {"result": results}
return {"result": str(results)}
def before_request():
request.data = {
"id": utils.md5(request.remote_addr + str(time.time()))
}
print(request.remote_addr)
pass
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import uuid
from paddlehub.common.utils import md5
HUB_SERVERS = ["http://paddlepaddle.org.cn/paddlehub"]
hub_name = md5(str(uuid.uuid1())[-12:]) + "-" + str(int(time.time()))
default_server_config = {
"server_url": HUB_SERVERS,
"resource_storage_server_url": "https://bj.bcebos.com/paddlehub-data/",
"debug": False,
"log_level": "DEBUG",
"hub_name": hub_name
}
def predict_image(module_name):
if request.path.split("/")[-1] not in cv_module_info.modules_info:
return {"error": "Module {} is not available.".format(module_name)}
module_info = cv_module_info.get_module_info(module_name)
if module_info["code_version"] == "v2":
results = {}
# results = predict_v2(module_info, inputs)
results.update({
"Warnning":
"This usage is out of date, please "
"use 'application/json' as "
"content-type to post to "
"/predict/%s. See "
"'https://github.com/PaddlePaddle/PaddleHub/blob/release/v1.6/docs/tutorial/serving.md' for more details."
% (module_name)
})
return gen_result("-1", results, "")
req_id = request.data.get("id")
img_base64 = request.form.getlist("image")
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 gen_result("-1", "Unrecognized file type", "")
filename = req_id + "_" \
+ utils.md5(str(time.time()) + item[0:20]) \
+ "." \
+ ext
img_data = base64.b64decode(item.split(',')[-1])
file_name_list.append(filename)
with open(filename, "wb") as fp:
fp.write(img_data)
else:
file = request.files.getlist("image")
for item in file:
file_name = req_id + "_" + item.filename
item.save(file_name)
file_name_list.append(file_name)
module = module_info["module"]
predict_func_name = cv_module_info.cv_module_method.get(module_name, "")
if predict_func_name != "":
predict_func = eval(predict_func_name)
else:
module_type = module.type.split("/")[-1].replace("-", "_").lower()
predict_func = eval("predict_" + module_type)
if file_name_list == []:
file_name_list = None
if extra_info == {}:
extra_info = None
results = predict_func(module_info, file_name_list, req_id, extra_info)
return results
