async def dogs(**kwargs):
log.debug("dogs({})".format(kwargs))
model = kwargs.get("model", "ResNet18")
map_names = dogs_map_names
img_path = kwargs.get("img_path", None)
if img_path is None:
raise InvalidParams("\"img_path\" is required")
image_dims = (3, 224, 224)
result = img_recon.image_recognition("dogs", model, map_names, img_path, image_dims)
return {"result": result}
def cars(self, request, context):
self.img_path = request.img_path
self.model = request.model
map_names = cars_map_names
image_dims = (3, 224, 224)
json_result = img_recon.image_recognition("cars", self.model,
map_names, self.img_path,
image_dims)
self.result = Result()
self.result.top_5 = str(json_result["top_5"]).encode("utf-8")
self.result.delta_time = str(json_result["delta_time"]).encode("utf-8")
log.debug("cars({},{})={}".format(self.model, self.img_path,
self.result.top_5))
return self.result
def flowers(self, request, context):
# In our case, request is a Numbers() object (from .proto file)
self.img_path = request.img_path
self.model = request.model
map_names = flowers_map_names
image_dims = (3, 224, 224)
json_result = img_recon.image_recognition("flowers", self.model,
map_names, self.img_path,
image_dims)
# To respond we need to create a Result() object (from .proto file)
self.result = Result()
self.result.top_5 = str(json_result["top_5"]).encode("utf-8")
self.result.delta_time = str(json_result["delta_time"]).encode("utf-8")
log.debug("flowers({},{})={}".format(self.model, self.img_path,
self.result.top_5))
return self.result
def dogs(self, request, context):
self.img_path = request.img_path
self.model = request.model
map_names = dogs_map_names
image_dims = (3, 224, 224)
json_result = img_recon.image_recognition("dogs", self.model,
map_names, self.img_path,
image_dims)
# To respond we need to create a Output() object (from .proto file)
self.response = Output()
self.response.top_5 = str(json_result["top_5"]).encode("utf-8")
self.response.delta_time = str(
json_result["delta_time"]).encode("utf-8")
log.debug("dogs({},{})={}".format(self.model, self.img_path,
self.response.top_5))
return self.response
def mp_image_recognition(method, request, map_names, image_dims, return_dict):
import service.image_recon as img_recon
return_dict["response"] = img_recon.image_recognition(
method, request.model, map_names, request.img_path, image_dims)