def create_graph_with_dvpp(self, resize_cfg):
'''
Create graph with dvpp
Args:
resize_cfg: resize parameter, (resize_w, resize_h)
resize_w: width of the destination resolution
resize_h: height of the destination resolution
Returns:
graph: a graph configured with dvpp
Raises:
Exception("[create_graph_with_dvpp]: create graph failed, ret ", ret)
'''
nntensorlist_object =hiai.NNTensorList()
graph = hiai.Graph(hiai.GraphConfig(graph_id=65530))
with graph.as_default():
engine = hiai.Engine()
resize_config = hiai.ResizeConfig(resize_width=resize_cfg[0], resize_height = resize_cfg[1])
nntensorlist_object = engine.resize(input_tensor_list=nntensorlist_object, config=resize_config)
ai_model_desc = hiai.AIModelDescription(name=os.path.basename(self.model_path), path=self.model_path)
ai_config = hiai.AIConfig(hiai.AIConfigItem("Inference", "item_value_2"))
final_result = engine.inference(input_tensor_list=nntensorlist_object,
ai_model=ai_model_desc,
ai_config=ai_config)
ret = copy.deepcopy(graph.create_graph())
if ret != hiai.HiaiPythonStatust.HIAI_PYTHON_OK:
graph.destroy()
raise Exception("[create_graph_with_dvpp]: create graph failed, ret ", ret)
print("[create_graph_with_dvpp]: create graph successful")
return graph
def CreateGraph(self):
'''
Create graph
Returns:
graph
'''
path, filename = os.path.split(self.model_path)
nntensorlist_object = hiai.NNTensorList()
id = random.randint(1, 2**32 - 1)
graph = hiai.Graph(hiai.GraphConfig(graph_id=id))
with graph.as_default():
engine = hiai.Engine()
#resize_config = hiai.ResizeConfig(resize_width=300, resize_height = 300)
#nntensorlist_object = engine.resize(input_tensor_list=nntensorlist_object, config=resize_config)
ai_model_desc = hiai.AIModelDescription(name=filename,
path=self.model_path)
ai_config = hiai.AIConfig(
hiai.AIConfigItem("Inference", "item_value_2"))
final_result = engine.inference(
input_tensor_list=nntensorlist_object,
ai_model=ai_model_desc,
ai_config=ai_config)
ret = copy.deepcopy(graph.create_graph())
if ret != hiai.HiaiPythonStatust.HIAI_PYTHON_OK:
graph.destroy()
raise Exception("create graph failed, ret ", ret)
print("create graph successful")
return graph
def CreateGraph(self, model, graph_id, model_engine_id):
# 获取Graph实例
myGraph = hiai.Graph(hiai.GraphConfig(graph_id=graph_id))
if myGraph is None:
print 'get graph failed'
return None
with myGraph.as_default():
model_engine = hiai.Engine(
hiai.EngineConfig(
engine_name='ModelInferenceEngine',
side=hiai.HiaiPythonSide.Device,
internal_so_name='/lib64/libhiai_python_device2.7.so',
engine_id=model_engine_id))
if model_engine is None:
print 'get model_engine failed'
return None
else:
print 'get model_engine ok!'
with model_engine.as_default():
if (None == model_engine.inference(
input_tensor_list=hiai.NNTensorList(),
ai_model=model)):
print 'Init model_engine failed '
return None
else:
print 'Init model_engine ok!'
# 创建Graph
if (hiai.HiaiPythonStatust.HIAI_PYTHON_OK == myGraph.create_graph()):
print 'create graph ok '
return myGraph
else:
print 'create graph failed'
return None
def CreateGraph(self, model,graph_id, model_engine_id):
myGraph = hiai.Graph(hiai.GraphConfig(graph_id = graph_id))
if myGraph is None:
print('get graph failed')
return None
with myGraph.as_default():
model_engine = hiai.Engine(hiai.EngineConfig(engine_name='ModelInferenceEngine', side=hiai.HiaiPythonSide.Device,engine_id = model_engine_id))
if model_engine is None:
print('get model_engine failed')
return None
else:
print('get model_engine ok!')
with model_engine.as_default():
if (None == model_engine.inference(input_tensor_list=hiai.NNTensorList(), ai_model=model)):
print('Init model_engine failed ')
return None
else:
print('Init model_engine ok!')
# Create Graph
if (hiai.HiaiPythonStatust.HIAI_PYTHON_OK == myGraph.create_graph()):
print('create graph ok ')
return myGraph
else:
print('create graph failed')
return None
def CreateGraph(self):
'''
Create graph
Returns:
graph
'''
nntensorlist_object = hiai.NNTensorList()
graph = hiai.Graph(hiai.GraphConfig(graph_id=65530))
with graph.as_default():
engine = hiai.Engine()
print(self.model_path, os.path.basename(self.model_path))
ai_model_desc = hiai.AIModelDescription(
name=os.path.basename(
self.model_path), path=self.model_path)
ai_config = hiai.AIConfig(
hiai.AIConfigItem(
"Inference", "item_value_2"))
final_result = engine.inference(
input_tensor_list=nntensorlist_object,
ai_model=ai_model_desc,
ai_config=ai_config)
ret = copy.deepcopy(graph.create_graph())
if ret != hiai.HiaiPythonStatust.HIAI_PYTHON_OK:
graph.destroy()
raise Exception("create graph failed, ret ", ret)
print("create graph successful")
return graph
def ExcuteInference(self, images):
result = []
for i in range(0, len(images)):
nArray = Yuv2Array(images[i])
ssd = {"name": "face_detection", "path": self.modelPath}
nntensor = hiai.NNTensor(nArray)
tensorList = hiai.NNTensorList(nntensor)
if self.first == True:
self.graph = hiai.Graph(hiai.GraphConfig(graph_id=2001))
with self.graph.as_default():
self.engine_config = hiai.EngineConfig(
engine_name="HIAIDvppInferenceEngine",
side=hiai.HiaiPythonSide.Device,
internal_so_name='/lib/libhiai_python_device2.7.so',
engine_id=2001)
self.engine = hiai.Engine(self.engine_config)
self.ai_model_desc = hiai.AIModelDescription(
name=ssd['name'], path=ssd['path'])
self.ai_config = hiai.AIConfig(
hiai.AIConfigItem("Inference", "item_value_2"))
final_result = self.engine.inference(
input_tensor_list=tensorList,
ai_model=self.ai_model_desc,
ai_config=self.ai_config)
ret = copy.deepcopy(self.graph.create_graph())
if ret != hiai.HiaiPythonStatust.HIAI_PYTHON_OK:
print("create graph failed, ret", ret)
d_ret = graph.destroy()
SetExitFlag(True)
return HIAI_APP_ERROR, None
self.first = False
else:
with self.graph.as_default():
final_result = self.engine.inference(
input_tensor_list=tensorList,
ai_model=self.ai_model_desc,
ai_config=self.ai_config)
resTensorList = self.graph.proc(input_nntensorlist=tensorList)
print("Inference result: ", resTensorList[0].shape)
result.append(resTensorList)
return HIAI_APP_OK, result
def ExcuteInference(self, images):
result = []
for i in range(0, len(images)):
nArray = Yuv2Array(images[i])
ssd = {"name": "object_detection", "path": self.modelPath}
nntensor = hiai.NNTensor(nArray)
'''
gray = cv2.cvtColor(nArray, cv2.COLOR_YUV2GRAY_420)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts=cnts[0]
#print("1111111")
j=0
for c in cnts:
M = cv2.moments(c)
c = c.astype("float")
c = c.astype("int")
shape = detect(c)
# cv2.drawContours(cv_image, [c], -1, (0, 255, 0), 2)
# image=cv2.cvtColor(cv_image, cv2.COLOR_YUV2RGB_I420)
# picpath='~/sample-objectdetection-python/picpath'+str(i)+'.jpg'
# cv2.imwrite(picpath,image)
# print("111111")
#BGR=cv2.cvtColor(nArray,cv2.COLOR_YUV2RGB)
#return BGR
# print(cv123_image)
# img_np = YUVtoRGB(nArray)
# cv2.imwrite("text4.jpg",img_np)
'''
tensorList = hiai.NNTensorList(nntensor)
if self.first == True:
self.graph = hiai.Graph(hiai.GraphConfig(graph_id=2001))
with self.graph.as_default():
self.engine_config = hiai.EngineConfig(
engine_name="HIAIDvppInferenceEngine",
side=hiai.HiaiPythonSide.Device,
internal_so_name='/lib/libhiai_python_device2.7.so',
engine_id=2001)
self.engine = hiai.Engine(self.engine_config)
self.ai_model_desc = hiai.AIModelDescription(
name=ssd['name'], path=ssd['path'])
self.ai_config = hiai.AIConfig(
hiai.AIConfigItem("Inference", "item_value_2"))
final_result = self.engine.inference(
input_tensor_list=tensorList,
ai_model=self.ai_model_desc,
ai_config=self.ai_config)
ret = copy.deepcopy(self.graph.create_graph())
if ret != hiai.HiaiPythonStatust.HIAI_PYTHON_OK:
print("create graph failed, ret", ret)
d_ret = graph.destroy()
SetExitFlag(True)
return HIAI_APP_ERROR, None
self.first = False
else:
with self.graph.as_default():
final_result = self.engine.inference(
input_tensor_list=tensorList,
ai_model=self.ai_model_desc,
ai_config=self.ai_config)
resTensorList = self.graph.proc(input_nntensorlist=tensorList)
# print("Inference result: ", resTensorList[0].shape)
result.append(resTensorList)
return HIAI_APP_OK, result
