127 lines
4.3 KiB
Python
127 lines
4.3 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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sys.path.insert(0, ".")
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import time
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from paddlehub.utils.log import logger
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from paddlehub.module.module import moduleinfo, serving
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import cv2
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import numpy as np
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import paddle.nn as nn
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import tools.infer.predict as paddle_predict
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from tools.infer.utils import Base64ToCV2, create_paddle_predictor
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from deploy.hubserving.clas.params import read_params
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@moduleinfo(
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name="clas_system",
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version="1.0.0",
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summary="class system service",
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author="paddle-dev",
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author_email="paddle-dev@baidu.com",
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type="cv/class")
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class ClasSystem(nn.Layer):
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def __init__(self, use_gpu=None, enable_mkldnn=None):
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"""
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initialize with the necessary elements
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"""
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cfg = read_params()
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if use_gpu is not None:
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cfg.use_gpu = use_gpu
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if enable_mkldnn is not None:
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cfg.enable_mkldnn = enable_mkldnn
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cfg.hubserving = True
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cfg.enable_benchmark = False
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self.args = cfg
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if cfg.use_gpu:
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try:
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_places = os.environ["CUDA_VISIBLE_DEVICES"]
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int(_places[0])
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print("Use GPU, GPU Memery:{}".format(cfg.gpu_mem))
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print("CUDA_VISIBLE_DEVICES: ", _places)
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except:
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raise RuntimeError(
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"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
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)
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else:
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print("Use CPU")
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print("Enable MKL-DNN") if enable_mkldnn else None
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self.predictor = create_paddle_predictor(self.args)
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def read_images(self, paths=[]):
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images = []
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for img_path in paths:
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assert os.path.isfile(
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img_path), "The {} isn't a valid file.".format(img_path)
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img = cv2.imread(img_path)
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if img is None:
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logger.info("error in loading image:{}".format(img_path))
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continue
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img = img[:, :, ::-1]
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images.append(img)
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return images
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def predict(self, images=[], paths=[], top_k=1):
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"""
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Args:
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images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
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paths (list[str]): The paths of images. If paths not images
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Returns:
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res (list): The result of chinese texts and save path of images.
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"""
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if images != [] and isinstance(images, list) and paths == []:
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predicted_data = images
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elif images == [] and isinstance(paths, list) and paths != []:
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predicted_data = self.read_images(paths)
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else:
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raise TypeError(
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"The input data is inconsistent with expectations.")
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assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
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all_results = []
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for img in predicted_data:
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if img is None:
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logger.info("error in loading image")
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all_results.append([])
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continue
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self.args.image_file = img
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self.args.top_k = top_k
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starttime = time.time()
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classes, scores = paddle_predict.predict(self.args, self.predictor)
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elapse = time.time() - starttime
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logger.info("Predict time: {}".format(elapse))
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all_results.append([classes.tolist(), scores.tolist(), elapse])
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return all_results
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@serving
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def serving_method(self, images, **kwargs):
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"""
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Run as a service.
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"""
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to_cv2 = Base64ToCV2()
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images_decode = [to_cv2(image) for image in images]
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results = self.predict(images_decode, **kwargs)
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return results
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