502 lines
19 KiB
Python
502 lines
19 KiB
Python
# Copyright (c) 2022 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 copy
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import math
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import time
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import yaml
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import cv2
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import numpy as np
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from collections import defaultdict
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import paddle
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from benchmark_utils import PaddleInferBenchmark
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from utils import gaussian_radius, gaussian2D, draw_umich_gaussian
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from preprocess import preprocess, decode_image, WarpAffine, NormalizeImage, Permute
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from utils import argsparser, Timer, get_current_memory_mb
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from infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig
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from keypoint_preprocess import get_affine_transform
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# add python path
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import sys
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
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sys.path.insert(0, parent_path)
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from pptracking.python.mot import CenterTracker
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from pptracking.python.mot.utils import MOTTimer, write_mot_results
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from pptracking.python.mot.visualize import plot_tracking
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def transform_preds_with_trans(coords, trans):
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target_coords = np.ones((coords.shape[0], 3), np.float32)
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target_coords[:, :2] = coords
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target_coords = np.dot(trans, target_coords.transpose()).transpose()
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return target_coords[:, :2]
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def affine_transform(pt, t):
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new_pt = np.array([pt[0], pt[1], 1.]).T
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new_pt = np.dot(t, new_pt)
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return new_pt[:2]
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def affine_transform_bbox(bbox, trans, width, height):
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bbox = np.array(copy.deepcopy(bbox), dtype=np.float32)
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bbox[:2] = affine_transform(bbox[:2], trans)
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bbox[2:] = affine_transform(bbox[2:], trans)
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bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, width - 1)
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bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, height - 1)
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return bbox
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class CenterTrack(Detector):
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"""
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Args:
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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device (str): Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU
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run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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cpu_threads (int): cpu threads
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enable_mkldnn (bool): whether to open MKLDNN
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output_dir (string): The path of output, default as 'output'
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threshold (float): Score threshold of the detected bbox, default as 0.5
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save_images (bool): Whether to save visualization image results, default as False
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save_mot_txts (bool): Whether to save tracking results (txt), default as False
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"""
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def __init__(
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self,
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model_dir,
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tracker_config=None,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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trt_min_shape=1,
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trt_max_shape=960,
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trt_opt_shape=544,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False,
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output_dir='output',
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threshold=0.5,
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save_images=False,
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save_mot_txts=False, ):
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super(CenterTrack, self).__init__(
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model_dir=model_dir,
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device=device,
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run_mode=run_mode,
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batch_size=batch_size,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn,
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output_dir=output_dir,
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threshold=threshold, )
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self.save_images = save_images
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self.save_mot_txts = save_mot_txts
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assert batch_size == 1, "MOT model only supports batch_size=1."
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self.det_times = Timer(with_tracker=True)
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self.num_classes = len(self.pred_config.labels)
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# tracker config
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cfg = self.pred_config.tracker
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min_box_area = cfg.get('min_box_area', -1)
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vertical_ratio = cfg.get('vertical_ratio', -1)
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track_thresh = cfg.get('track_thresh', 0.4)
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pre_thresh = cfg.get('pre_thresh', 0.5)
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self.tracker = CenterTracker(
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num_classes=self.num_classes,
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min_box_area=min_box_area,
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vertical_ratio=vertical_ratio,
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track_thresh=track_thresh,
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pre_thresh=pre_thresh)
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self.pre_image = None
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def get_additional_inputs(self, dets, meta, with_hm=True):
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# Render input heatmap from previous trackings.
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trans_input = meta['trans_input']
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inp_width, inp_height = int(meta['inp_width']), int(meta['inp_height'])
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input_hm = np.zeros((1, inp_height, inp_width), dtype=np.float32)
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for det in dets:
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if det['score'] < self.tracker.pre_thresh:
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continue
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bbox = affine_transform_bbox(det['bbox'], trans_input, inp_width,
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inp_height)
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h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
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if (h > 0 and w > 0):
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radius = gaussian_radius(
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(math.ceil(h), math.ceil(w)), min_overlap=0.7)
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radius = max(0, int(radius))
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ct = np.array(
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[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
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dtype=np.float32)
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ct_int = ct.astype(np.int32)
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if with_hm:
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input_hm[0] = draw_umich_gaussian(input_hm[0], ct_int,
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radius)
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if with_hm:
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input_hm = input_hm[np.newaxis]
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return input_hm
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def preprocess(self, image_list):
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preprocess_ops = []
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for op_info in self.pred_config.preprocess_infos:
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new_op_info = op_info.copy()
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op_type = new_op_info.pop('type')
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preprocess_ops.append(eval(op_type)(**new_op_info))
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assert len(image_list) == 1, 'MOT only support bs=1'
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im_path = image_list[0]
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im, im_info = preprocess(im_path, preprocess_ops)
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#inputs = create_inputs(im, im_info)
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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((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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inputs['trans_input'] = im_info['trans_input']
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inputs['inp_width'] = im_info['inp_width']
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inputs['inp_height'] = im_info['inp_height']
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inputs['center'] = im_info['center']
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inputs['scale'] = im_info['scale']
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inputs['out_height'] = im_info['out_height']
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inputs['out_width'] = im_info['out_width']
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if self.pre_image is None:
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self.pre_image = inputs['image']
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# initializing tracker for the first frame
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self.tracker.init_track([])
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inputs['pre_image'] = self.pre_image
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self.pre_image = inputs['image'] # Note: update for next image
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# render input heatmap from tracker status
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pre_hm = self.get_additional_inputs(
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self.tracker.tracks, inputs, with_hm=True)
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inputs['pre_hm'] = pre_hm #.to_tensor(pre_hm)
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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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if input_names[i] == 'x':
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input_tensor.copy_from_cpu(inputs['image'])
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else:
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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return inputs
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def postprocess(self, inputs, result):
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# postprocess output of predictor
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np_bboxes = result['bboxes']
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if np_bboxes.shape[0] <= 0:
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print('[WARNNING] No object detected and tracked.')
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result = {'bboxes': np.zeros([0, 6]), 'cts': None, 'tracking': None}
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return result
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result = {k: v for k, v in result.items() if v is not None}
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return result
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def centertrack_post_process(self, dets, meta, out_thresh):
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if not ('bboxes' in dets):
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return [{}]
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preds = []
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c, s = meta['center'], meta['scale']
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h, w = meta['out_height'], meta['out_width']
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trans = get_affine_transform(
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center=c,
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input_size=s,
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rot=0,
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output_size=[w, h],
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shift=(0., 0.),
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inv=True).astype(np.float32)
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for i, dets_bbox in enumerate(dets['bboxes']):
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if dets_bbox[1] < out_thresh:
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break
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item = {}
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item['score'] = dets_bbox[1]
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item['class'] = int(dets_bbox[0]) + 1
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item['ct'] = transform_preds_with_trans(
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dets['cts'][i].reshape([1, 2]), trans).reshape(2)
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if 'tracking' in dets:
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tracking = transform_preds_with_trans(
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(dets['tracking'][i] + dets['cts'][i]).reshape([1, 2]),
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trans).reshape(2)
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item['tracking'] = tracking - item['ct']
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if 'bboxes' in dets:
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bbox = transform_preds_with_trans(
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dets_bbox[2:6].reshape([2, 2]), trans).reshape(4)
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item['bbox'] = bbox
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preds.append(item)
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return preds
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def tracking(self, inputs, det_results):
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result = self.centertrack_post_process(det_results, inputs,
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self.tracker.out_thresh)
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online_targets = self.tracker.update(result)
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online_tlwhs, online_scores, online_ids = [], [], []
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for t in online_targets:
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bbox = t['bbox']
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tlwh = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]
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tscore = float(t['score'])
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tid = int(t['tracking_id'])
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if tlwh[2] * tlwh[3] > 0:
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online_tlwhs.append(tlwh)
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online_ids.append(tid)
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online_scores.append(tscore)
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return online_tlwhs, online_scores, online_ids
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def predict(self, repeats=1):
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'''
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Args:
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repeats (int): repeats number for prediction
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Returns:
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result (dict): include 'bboxes', 'cts' and 'tracking':
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np.ndarray: shape:[N,6],[N,2] and [N,2], N: number of box
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'''
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# model prediction
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np_bboxes, np_cts, np_tracking = None, None, None
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for i in range(repeats):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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bboxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_bboxes = bboxes_tensor.copy_to_cpu()
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cts_tensor = self.predictor.get_output_handle(output_names[1])
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np_cts = cts_tensor.copy_to_cpu()
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tracking_tensor = self.predictor.get_output_handle(output_names[2])
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np_tracking = tracking_tensor.copy_to_cpu()
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result = dict(bboxes=np_bboxes, cts=np_cts, tracking=np_tracking)
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return result
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def predict_image(self,
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image_list,
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run_benchmark=False,
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repeats=1,
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visual=True,
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seq_name=None):
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mot_results = []
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num_classes = self.num_classes
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image_list.sort()
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ids2names = self.pred_config.labels
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data_type = 'mcmot' if num_classes > 1 else 'mot'
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for frame_id, img_file in enumerate(image_list):
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batch_image_list = [img_file] # bs=1 in MOT model
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if run_benchmark:
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# preprocess
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inputs = self.preprocess(batch_image_list) # warmup
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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# model prediction
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result_warmup = self.predict(repeats=repeats) # warmup
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self.det_times.inference_time_s.start()
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result = self.predict(repeats=repeats)
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self.det_times.inference_time_s.end(repeats=repeats)
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# postprocess
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result_warmup = self.postprocess(inputs, result) # warmup
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self.det_times.postprocess_time_s.start()
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det_result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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# tracking
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result_warmup = self.tracking(inputs, det_result)
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self.det_times.tracking_time_s.start()
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online_tlwhs, online_scores, online_ids = self.tracking(
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inputs, det_result)
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self.det_times.tracking_time_s.end()
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self.det_times.img_num += 1
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cm, gm, gu = get_current_memory_mb()
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self.cpu_mem += cm
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self.gpu_mem += gm
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self.gpu_util += gu
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else:
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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self.det_times.inference_time_s.start()
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result = self.predict()
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self.det_times.inference_time_s.end()
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self.det_times.postprocess_time_s.start()
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det_result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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# tracking process
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self.det_times.tracking_time_s.start()
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online_tlwhs, online_scores, online_ids = self.tracking(
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inputs, det_result)
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self.det_times.tracking_time_s.end()
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self.det_times.img_num += 1
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if visual:
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if len(image_list) > 1 and frame_id % 10 == 0:
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print('Tracking frame {}'.format(frame_id))
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frame, _ = decode_image(img_file, {})
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im = plot_tracking(
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frame,
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online_tlwhs,
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online_ids,
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online_scores,
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frame_id=frame_id,
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ids2names=ids2names)
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if seq_name is None:
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seq_name = image_list[0].split('/')[-2]
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save_dir = os.path.join(self.output_dir, seq_name)
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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cv2.imwrite(
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os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
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mot_results.append([online_tlwhs, online_scores, online_ids])
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return mot_results
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def predict_video(self, video_file, camera_id):
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video_out_name = 'mot_output.mp4'
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if camera_id != -1:
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capture = cv2.VideoCapture(camera_id)
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else:
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capture = cv2.VideoCapture(video_file)
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video_out_name = os.path.split(video_file)[-1]
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# Get Video info : resolution, fps, frame count
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width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = int(capture.get(cv2.CAP_PROP_FPS))
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frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
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print("fps: %d, frame_count: %d" % (fps, frame_count))
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if not os.path.exists(self.output_dir):
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os.makedirs(self.output_dir)
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out_path = os.path.join(self.output_dir, video_out_name)
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video_format = 'mp4v'
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fourcc = cv2.VideoWriter_fourcc(*video_format)
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writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
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frame_id = 1
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timer = MOTTimer()
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results = defaultdict(list) # centertrack onpy support single class
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num_classes = self.num_classes
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data_type = 'mcmot' if num_classes > 1 else 'mot'
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ids2names = self.pred_config.labels
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while (1):
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ret, frame = capture.read()
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if not ret:
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break
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if frame_id % 10 == 0:
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print('Tracking frame: %d' % (frame_id))
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frame_id += 1
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timer.tic()
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seq_name = video_out_name.split('.')[0]
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mot_results = self.predict_image(
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[frame[:, :, ::-1]], visual=False, seq_name=seq_name)
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timer.toc()
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fps = 1. / timer.duration
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online_tlwhs, online_scores, online_ids = mot_results[0]
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results[0].append(
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(frame_id + 1, online_tlwhs, online_scores, online_ids))
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im = plot_tracking(
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frame,
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online_tlwhs,
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online_ids,
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online_scores,
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frame_id=frame_id,
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fps=fps,
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ids2names=ids2names)
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writer.write(im)
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if camera_id != -1:
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cv2.imshow('Mask Detection', im)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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if self.save_mot_txts:
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result_filename = os.path.join(
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self.output_dir, video_out_name.split('.')[-2] + '.txt')
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write_mot_results(result_filename, results, data_type, num_classes)
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writer.release()
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def main():
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detector = CenterTrack(
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|
FLAGS.model_dir,
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|
tracker_config=None,
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|
device=FLAGS.device,
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|
run_mode=FLAGS.run_mode,
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|
batch_size=1,
|
|
trt_min_shape=FLAGS.trt_min_shape,
|
|
trt_max_shape=FLAGS.trt_max_shape,
|
|
trt_opt_shape=FLAGS.trt_opt_shape,
|
|
trt_calib_mode=FLAGS.trt_calib_mode,
|
|
cpu_threads=FLAGS.cpu_threads,
|
|
enable_mkldnn=FLAGS.enable_mkldnn,
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|
output_dir=FLAGS.output_dir,
|
|
threshold=FLAGS.threshold,
|
|
save_images=FLAGS.save_images,
|
|
save_mot_txts=FLAGS.save_mot_txts)
|
|
|
|
# predict from video file or camera video stream
|
|
if FLAGS.video_file is not None or FLAGS.camera_id != -1:
|
|
detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
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|
else:
|
|
# predict from image
|
|
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
|
|
detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10)
|
|
|
|
if not FLAGS.run_benchmark:
|
|
detector.det_times.info(average=True)
|
|
else:
|
|
mode = FLAGS.run_mode
|
|
model_dir = FLAGS.model_dir
|
|
model_info = {
|
|
'model_name': model_dir.strip('/').split('/')[-1],
|
|
'precision': mode.split('_')[-1]
|
|
}
|
|
bench_log(detector, img_list, model_info, name='MOT')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
parser = argsparser()
|
|
FLAGS = parser.parse_args()
|
|
print_arguments(FLAGS)
|
|
FLAGS.device = FLAGS.device.upper()
|
|
assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
|
|
], "device should be CPU, GPU, NPU or XPU"
|
|
|
|
main()
|