149 lines
5.2 KiB
Python
149 lines
5.2 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 argparse
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import time
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from functools import reduce
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import yaml
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import ast
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import numpy as np
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import cv2
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import paddle
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from paddleclas.deploy.utils import logger, config
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from paddleclas.deploy.utils.predictor import Predictor
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from paddleclas.deploy.utils.get_image_list import get_image_list
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from paddleclas.deploy.python.preprocess import create_operators
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from paddleclas.deploy.python.det_preprocess import det_preprocess
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class DetPredictor(Predictor):
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def __init__(self, config):
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super().__init__(config["Global"],
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config["Global"]["det_inference_model_dir"])
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self.preprocess_ops = create_operators(config["DetPreProcess"][
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"transform_ops"])
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self.config = config
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def preprocess(self, img):
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im_info = {
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'scale_factor': np.array(
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[1., 1.], dtype=np.float32),
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'im_shape': np.array(
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img.shape[:2], dtype=np.float32),
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'input_shape': self.config["Global"]["image_shape"],
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"scale_factor": np.array(
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[1., 1.], dtype=np.float32)
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}
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im, im_info = det_preprocess(img, im_info, self.preprocess_ops)
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inputs = self.create_inputs(im, im_info)
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return inputs
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def create_inputs(self, im, im_info):
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"""generate input for different model type
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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model_arch (str): model type
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Returns:
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inputs (dict): input of model
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"""
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inputs = {}
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inputs['image'] = np.array((im, )).astype('float32')
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inputs['im_shape'] = np.array(
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(im_info['im_shape'], )).astype('float32')
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inputs['scale_factor'] = np.array(
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(im_info['scale_factor'], )).astype('float32')
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return inputs
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def parse_det_results(self, pred, threshold, label_list):
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max_det_results = self.config["Global"]["max_det_results"]
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keep_indexes = pred[:, 1].argsort()[::-1][:max_det_results]
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results = []
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for idx in keep_indexes:
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single_res = pred[idx]
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class_id = int(single_res[0])
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score = single_res[1]
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bbox = single_res[2:]
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if score < threshold:
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continue
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label_name = label_list[class_id]
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results.append({
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"class_id": class_id,
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"score": score,
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"bbox": bbox,
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"label_name": label_name,
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})
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return results
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def predict(self, image, threshold=0.5, run_benchmark=False):
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'''
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Args:
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image (str/np.ndarray): path of image/ np.ndarray read by cv2
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threshold (float): threshold of predicted box' score
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Returns:
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results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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MaskRCNN's results include 'masks': np.ndarray:
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shape: [N, im_h, im_w]
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'''
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inputs = self.preprocess(image)
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np_boxes = None
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input_names = self.predictor.get_input_names()
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for i in range(len(input_names)):
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input_tensor = self.predictor.get_input_handle(input_names[i])
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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t1 = time.time()
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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t2 = time.time()
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print("Inference: {} ms per batch image".format((t2 - t1) * 1000.0))
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# do not perform postprocess in benchmark mode
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results = []
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if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
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print('[WARNNING] No object detected.')
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else:
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results = self.parse_det_results(
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np_boxes, self.config["Global"]["threshold"],
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self.config["Global"]["label_list"])
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return results
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def main(config):
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det_predictor = DetPredictor(config)
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image_list = get_image_list(config["Global"]["infer_imgs"])
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assert config["Global"]["batch_size"] == 1
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for idx, image_file in enumerate(image_list):
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img = cv2.imread(image_file)[:, :, ::-1]
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output = det_predictor.predict(img)
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print(output)
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return
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(args.config, overrides=args.override, show=True)
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main(config)
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