def __init__(self, ai_settings, follow_flag=False):
self.ai_settings = ai_settings
# 初始化角度预测器
valid_places = (
Place(TargetType.kFPGA, PrecisionType.kFP16, DataLayoutType.kNHWC),
Place(TargetType.kHost, PrecisionType.kFloat),
Place(TargetType.kARM, PrecisionType.kFloat),
)
config = CxxConfig()
model_dir = ai_settings.angle_model_path
config.set_model_file(model_dir + "/model")
config.set_param_file(model_dir + "/params")
config.set_valid_places(valid_places)
self.angle_predictor = CreatePaddlePredictor(config)
# 初始化标志预测器
if follow_flag:
label_model_path = ai_settings.follow_model_path
elif not follow_flag:
label_model_path = ai_settings.label_model_path
model_dir = label_model_path
pm_config = pm.PaddleMobileConfig()
pm_config.precision = pm.PaddleMobileConfig.Precision.FP32
pm_config.device = pm.PaddleMobileConfig.Device.kFPGA
pm_config.model_dir = model_dir
pm_config.thread_num = 4
self.label_predictor = pm.CreatePaddlePredictor(pm_config)
# 标志物相关变量
self.label_img = None
self.angle_img = None
self.ImgInd = 0
def load_label_model(self):
model_dir = self.label_model_path
pm_config = pm.PaddleMobileConfig()
pm_config.precision = pm.PaddleMobileConfig.Precision.FP32
pm_config.device = pm.PaddleMobileConfig.Device.kFPGA
pm_config.model_dir = model_dir
pm_config.thread_num = 4
label_predictor = pm.CreatePaddlePredictor(pm_config)
return label_predictor
def load_model(): # 加载已经训练好的手写数字识别模型(静态图)
path = "hand_inference_model"
model_dir = path
pm_config = pm.PaddleMobileConfig()
pm_config.precision = pm.PaddleMobileConfig.Precision.FP32
pm_config.device = pm.PaddleMobileConfig.Device.kFPGA
pm_config.model_dir = model_dir
pm_config.thread_num = 4
predictor = pm.CreatePaddlePredictor(pm_config)
return predictor
def load_model(self, model_flie, param_file, thread_num, model_dir):
'''
加载PaddleMobile模型
参数：模型文件、模型参数文件、线程数、模型目录
返回：模型预测器
'''
pm_config = pm.PaddleMobileConfig()
pm_config.precision = pm.PaddleMobileConfig.Precision.FP32 ######ok
pm_config.device = pm.PaddleMobileConfig.Device.kFPGA ######ok
if model_dir:
pm_config.model_dir = model_dir
else:
pm_config.prog_file = model_flie
pm_config.param_file = param_file
pm_config.thread_num = thread_num
predictor = pm.CreatePaddlePredictor(pm_config)
return predictor
def load_model(self, model_dir, param_dir, thread_num):
"""
加载PaddleMobile模型
:param model_dir: 模型文件路径
:param param_dir: 模型参数文件路径
:param thread_num: 线程数
:return: 模型预测器
"""
pm_config = pm.PaddleMobileConfig()
pm_config.precision = pm.PaddleMobileConfig.Precision.FP32
pm_config.device = pm.PaddleMobileConfig.Device.kFPGA
pm_config.prog_file = model_dir
pm_config.param_file = param_dir
pm_config.thread_num = thread_num
predictor = pm.CreatePaddlePredictor(pm_config)
return predictor
def init(configs):
global predictor
model_dir = configs['model']
pm_config = pm.PaddleMobileConfig()
pm_config.precision = pm.PaddleMobileConfig.Precision.FP32
pm_config.device = pm.PaddleMobileConfig.Device.kFPGA
pm_config.prog_file = os.path.join(model_dir, "model")
pm_config.param_file = os.path.join(model_dir, "params")
pm_config.prog_file = model_dir + "/model"
pm_config.param_file = model_dir + '/params'
pm_config.thread_num = 4
print('')
print('configuration for predictor is :')
print('\tPrecision: ' + str(pm_config.precision))
print('\t Device: ' + str(pm_config.device))
print('\t Model: ' + str(pm_config.prog_file))
print('\t Params: ' + str(pm_config.param_file))
print('\tThreadNum: ' + str(pm_config.thread_num))
print('')
predictor = pm.CreatePaddlePredictor(pm_config)
def __init__(self, configs, label_map, name):
self.name = name
self.predictor = None
self.labels = []
self.classes = []
self.configs = configs
self.model_dir = configs['model']
self.label_map = label_map
self.pm_config = pm.PaddleMobileConfig()
self.pm_config.precision = pm.PaddleMobileConfig.Precision.FP32
self.pm_config.device = pm.PaddleMobileConfig.Device.kFPGA
self.pm_config.prog_file = os.path.join(self.model_dir, 'model')
self.pm_config.param_file = os.path.join(self.model_dir, 'params')
self.pm_config.thread_num = 4
print('configuration for predictor is :')
print('\tPrecision: ' + str(self.pm_config.precision))
print('\t Device: ' + str(self.pm_config.device))
print('\t Model: ' + str(self.pm_config.prog_file))
print('\t Params: ' + str(self.pm_config.param_file))
print('\tThreadNum: ' + str(self.pm_config.thread_num))
self.predictor = pm.CreatePaddlePredictor(self.pm_config)
self.tensor = self.init_tensor(
(1, 3, configs['input_width'], configs['input_height']))
# init video_thread
if self.name == 'video' or self.name == 'camera':
self.video_thread = VideoThread(self.video_path,
self.configs['input_width'],
self.configs['input_height'], 1,
'video_thread')
if self.name == 'video':
self.video_path = configs[self.name]
elif self.name == 'camera':
self.video_path = configs[self.name]
