def pdx_predict_det(self,img):
'''
目标检测接口
将帧图片传给服务端预测返回筛选之后的boxes
将poxes的结果可视化
'''
or_img = img
img = image_to_base64(img)
respone = self.stub.paddlex_predict_det(PaddleXserver_pb2.image(
_image = img
))
result = []
for value in respone.boxes:
dict_temp = {}
dict_temp['category_id'] = value.category_id
dict_temp['bbox'] = []
dict_temp['bbox'].append(value.bbox.xmin)
dict_temp['bbox'].append(value.bbox.ymin)
dict_temp['bbox'].append(value.bbox.width)
dict_temp['bbox'].append(value.bbox.height)
dict_temp['score'] = value.score
dict_temp['category'] = value.category
result.append(dict_temp)
visualize_img = pdx.det.visualize(or_img,result,threshold=0,save_dir=None)
return visualize_img
def pdx_predict_det_seg(self,img):
'''
实例分割接口
将帧图发送服务端，返回结果
可视化结果
'''
or_img = img
img = image_to_base64(img)
respone = self.stub.paddlex_predict_det_seg(PaddleXserver_pb2.image(
_image = img
))
result = []
for value in respone.boxes_seg:
dict_temp = {}
dict_temp['category_id'] = value.category_id
dict_temp['bbox'] = []
dict_temp['bbox'].append(value.bbox.xmin)
dict_temp['bbox'].append(value.bbox.ymin)
dict_temp['bbox'].append(value.bbox.width)
dict_temp['bbox'].append(value.bbox.height)
dict_temp['score'] = value.score
dict_temp['mask'] = base64_to_image(value._mask)
dict_temp['category'] = value.category
result.append(dict_temp)
visualize_img = pdx.det.visualize(or_img,result,threshold=0,save_dir=None)
return visualize_img
def paddlex_predict_det(self, request, context):
'''
目标检测接口
获得上传的帧图片，将预测完毕的图片筛选后返回
'''
img = base64_to_image(request._image)
#写入图片用
result = self.server_predict.predict(img)
det_inputs_result = PaddleXserver_pb2.det_inputs_result()
for value in result:
#循环读取result字典
# 返回符合score的box
if value['score'] < self.server_predict.score.default_threshold:
continue
#把真值赋给数据对象
temp = det_inputs_result.boxes.add()
temp.category_id = value['category_id']
temp.bbox.xmin = value['bbox'][0]
temp.bbox.ymin = value['bbox'][1]
temp.bbox.width = value['bbox'][2]
temp.bbox.height = value['bbox'][3]
temp.score = value['score']
temp.category = value['category']
return det_inputs_result
def paddlex_predict_det_seg(self, request, context):
'''
实例分割接口
接受帧图预测
结果筛选后返回
'''
img = base64_to_image(request._image)
#写入图片用
result = self.server_predict.predict(img)
det_seg_inputs_result = PaddleXserver_pb2.det_seg_inputs_result(
) #返回符合score的box
for value in result:
if value['score'] < self.server_predict.score.default_threshold:
continue
#把真值赋给数据对象
temp = det_seg_inputs_result.boxes_seg.add()
temp.category_id = value['category_id']
temp.bbox.xmin = value['bbox'][0]
temp.bbox.ymin = value['bbox'][1]
temp.bbox.width = value['bbox'][2]
temp.bbox.height = value['bbox'][3]
temp.score = value['score']
temp._mask = image_to_base64(value['mask'])
temp.category = value['category']
return det_seg_inputs_result
def paddlex_init(self,request,context):
'''
用获得的参数初始化加载模型
返回加载模型的结果
'''
self.server_predict = Server_Predict(request.model_dir,request.use_gpu,request.gpu_id)
respone = self.server_predict.load_model()
return PaddleXserver_pb2.paddlex_init_result(init_result = respone)
def load_model(self):
'''
将参数传给服务端，加载模型返回加载结果
'''
# print('use model_dir'+self.model_dir)
respone = self.stub.paddlex_init(PaddleXserver_pb2.paddlex_init_cmd(
model_dir = self.model_dir,
use_gpu = self.use_gpu,
gpu_id = self.gpu_id
))
return respone.init_result
def paddlex_predict_cls(self,request,context):
'''
图像分类接口
返回的结果只有一个不是一组
'''
img = base64_to_image(request._image)
result = self.server_predict.predict(img)
cls_inputs_result = PaddleXserver_pb2.cls_inputs_result()
#把真值赋给数据对象,只返回第一个值
cls_inputs_result.category_id = result[0]['category_id']
cls_inputs_result.score = result[0]['score']
cls_inputs_result.category = result[0]['category']
return cls_inputs_result
def paddlex_predict_seg(self,request,context):
'''
语义分割接口
解释帧图返回预测结果
'''
img = base64_to_image(request._image)
result = self.server_predict.predict(img)
seg_inputs_result = PaddleXserver_pb2.seg_inputs_result()
#把真值赋给数据对象
score_maps = (result['score_map']*255).astype(np.uint8)
score_maps = np.array_split(score_maps,score_maps.shape[2],axis=2)
for mask in score_maps:
score_map = seg_inputs_result._score_map.add()
score_map.value = image_to_base64(mask)
seg_inputs_result.label_map = image_to_base64(result['label_map'])
seg_inputs_result.set_threshold = self.server_predict.score.default_threshold
return seg_inputs_result
def pdx_predict_cls(self,img):
'''
图像分类接口
将帧图发送服务端，返回结果一个
'''
img = image_to_base64(img)
respone = self.stub.paddlex_predict_cls(PaddleXserver_pb2.image(
_image = img
))
result = []
dict_temp = {}
dict_temp['category_id'] = respone.category_id
dict_temp['score'] = respone.score
dict_temp['category'] = respone.category
result.append(dict_temp)
return result
def pdx_predict_seg(self,img):
'''
语义分割接口
将帧图发送服务端，返回一个分割结果
'''
or_img = img
img = image_to_base64(img)
respone = self.stub.paddlex_predict_seg(PaddleXserver_pb2.image(
_image = img
))
dict_temp = {}
dict_temp['score_map'] = []
for value in respone._score_map:
temp = base64_to_image(value.value)
temp = temp[:,:,np.newaxis]/255
dict_temp['score_map'].append(temp.astype('float32'))
dict_temp['score_map'] = np.concatenate(dict_temp['score_map'],axis=2)
dict_temp['label_map'] = base64_to_image(respone.label_map)
visualize_img = pdx.seg.visualize(or_img, dict_temp, weight=respone.set_threshold, save_dir=None, color=None)
return visualize_img
