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 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_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