471 lines
19 KiB
Python
471 lines
19 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2021 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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# ------------------------------------------------------------------------
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"""
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SORT: A Simple, Online and Realtime Tracker
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Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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"""
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from __future__ import print_function
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import os
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import numpy as np
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import random
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import argparse
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import torchvision.transforms.functional as F
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import torch
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import cv2
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from tqdm import tqdm
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from pathlib import Path
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from PIL import Image, ImageDraw
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from models import build_model
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from util.tool import load_model
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from main import get_args_parser
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from torch.nn.functional import interpolate
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from typing import List
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from util.evaluation import Evaluator
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import motmetrics as mm
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import shutil
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from detectron2.structures import Instances
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from tracker import BYTETracker
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np.random.seed(2020)
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COLORS_10 = [(144, 238, 144), (178, 34, 34), (221, 160, 221), (0, 255, 0), (0, 128, 0), (210, 105, 30), (220, 20, 60),
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(192, 192, 192), (255, 228, 196), (50, 205, 50), (139, 0, 139), (100, 149, 237), (138, 43, 226),
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(238, 130, 238),
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(255, 0, 255), (0, 100, 0), (127, 255, 0), (255, 0, 255), (0, 0, 205), (255, 140, 0), (255, 239, 213),
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(199, 21, 133), (124, 252, 0), (147, 112, 219), (106, 90, 205), (176, 196, 222), (65, 105, 225),
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(173, 255, 47),
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(255, 20, 147), (219, 112, 147), (186, 85, 211), (199, 21, 133), (148, 0, 211), (255, 99, 71),
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(144, 238, 144),
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(255, 255, 0), (230, 230, 250), (0, 0, 255), (128, 128, 0), (189, 183, 107), (255, 255, 224),
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(128, 128, 128),
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(105, 105, 105), (64, 224, 208), (205, 133, 63), (0, 128, 128), (72, 209, 204), (139, 69, 19),
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(255, 245, 238),
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(250, 240, 230), (152, 251, 152), (0, 255, 255), (135, 206, 235), (0, 191, 255), (176, 224, 230),
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(0, 250, 154),
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(245, 255, 250), (240, 230, 140), (245, 222, 179), (0, 139, 139), (143, 188, 143), (255, 0, 0),
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(240, 128, 128),
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(102, 205, 170), (60, 179, 113), (46, 139, 87), (165, 42, 42), (178, 34, 34), (175, 238, 238),
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(255, 248, 220),
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(218, 165, 32), (255, 250, 240), (253, 245, 230), (244, 164, 96), (210, 105, 30)]
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def plot_one_box(x, img, color=None, label=None, score=None, line_thickness=None):
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# Plots one bounding box on image img
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tl = line_thickness or round(
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0.002 * max(img.shape[0:2])) + 1 # line thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl)
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# if label:
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# tf = max(tl - 1, 1) # font thickness
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# t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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# c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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# cv2.rectangle(img, c1, c2, color, -1) # filled
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# cv2.putText(img,
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# label, (c1[0], c1[1] - 2),
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# 0,
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# tl / 3, [225, 255, 255],
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# thickness=tf,
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# lineType=cv2.LINE_AA)
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# if score is not None:
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# cv2.putText(img, score, (c1[0], c1[1] + 30), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
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return img
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def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color=False):
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if cvt_color:
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ori_img = cv2.cvtColor(np.asarray(ori_img), cv2.COLOR_RGB2BGR)
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img = ori_img
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for i, box in enumerate(bbox):
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x1, y1, x2, y2 = [int(i) for i in box[:4]]
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x1 += offset[0]
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x2 += offset[0]
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y1 += offset[1]
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y2 += offset[1]
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if len(box) > 4:
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score = '{:.2f}'.format(box[4])
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else:
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score = None
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# box text and bar
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id = int(identities[i]) if identities is not None else 0
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color = COLORS_10[id % len(COLORS_10)]
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label = '{:d}'.format(id)
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# t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
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img = plot_one_box([x1, y1, x2, y2], img, color, label, score=score)
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return img
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def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 255)) -> np.ndarray:
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assert len(points.shape) == 2 and points.shape[1] == 2, 'invalid points shape: {}'.format(points.shape)
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for i, (x, y) in enumerate(points):
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if i >= 300:
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color = (0, 255, 0)
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cv2.circle(img, (int(x), int(y)), 2, color=color, thickness=2)
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return img
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def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
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return tensor.detach().cpu().numpy()
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class Track(object):
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track_cnt = 0
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def __init__(self, box):
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self.box = box
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self.time_since_update = 0
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self.id = Track.track_cnt
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Track.track_cnt += 1
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self.miss = 0
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def miss_one_frame(self):
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self.miss += 1
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def clear_miss(self):
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self.miss = 0
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def update(self, box):
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self.box = box
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self.clear_miss()
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def write_results(filename, results):
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save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\n'
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with open(filename, 'w') as f:
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for frame_id, tlwhs, track_ids, scores in results:
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for tlwh, track_id, score in zip(tlwhs, track_ids, scores):
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if track_id < 0:
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continue
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x1, y1, w, h = tlwh
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line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1), h=round(h, 1), s=round(score, 2))
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f.write(line)
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logger.info('save results to {}'.format(filename))
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class MOTR(object):
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def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
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self.tracker = BYTETracker()
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def update(self, dt_instances: Instances):
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ret = []
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for i in range(len(dt_instances)):
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label = dt_instances.labels[i]
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if label == 0:
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id = dt_instances.obj_idxes[i]
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box_with_score = np.concatenate([dt_instances.boxes[i], dt_instances.scores[i:i+1]], axis=-1)
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ret.append(np.concatenate((box_with_score, [id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive
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if len(ret) > 0:
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online_targets = self.tracker.update(np.concatenate(ret))
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online_ret = []
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for t in online_targets:
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online_ret.append(np.array([t.tlbr[0], t.tlbr[1], t.tlbr[2], t.tlbr[3], t.score, t.track_id]).reshape(1, -1))
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if len(online_ret) > 0:
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return np.concatenate(online_ret)
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return np.empty((0, 6))
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def load_label(label_path: str, img_size: tuple) -> dict:
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labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)
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h, w = img_size
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# Normalized cewh to pixel xyxy format
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labels = labels0.copy()
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labels[:, 2] = w * (labels0[:, 2] - labels0[:, 4] / 2)
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labels[:, 3] = h * (labels0[:, 3] - labels0[:, 5] / 2)
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labels[:, 4] = w * (labels0[:, 2] + labels0[:, 4] / 2)
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labels[:, 5] = h * (labels0[:, 3] + labels0[:, 5] / 2)
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targets = {'boxes': [], 'labels': [], 'area': []}
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num_boxes = len(labels)
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visited_ids = set()
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for label in labels[:num_boxes]:
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obj_id = label[1]
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if obj_id in visited_ids:
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continue
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visited_ids.add(obj_id)
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targets['boxes'].append(label[2:6].tolist())
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targets['area'].append(label[4] * label[5])
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targets['labels'].append(0)
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targets['boxes'] = np.asarray(targets['boxes'])
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targets['area'] = np.asarray(targets['area'])
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targets['labels'] = np.asarray(targets['labels'])
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return targets
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def filter_pub_det(res_file, pub_det_file, filter_iou=False):
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frame_boxes = {}
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with open(pub_det_file, 'r') as f:
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lines = f.readlines()
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for line in lines:
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if len(line) == 0:
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continue
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elements = line.strip().split(',')
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frame_id = int(elements[0])
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x1, y1, w, h = elements[2:6]
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x1, y1, w, h = float(x1), float(y1), float(w), float(h)
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x2 = x1 + w - 1
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y2 = y1 + h - 1
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if frame_id not in frame_boxes:
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frame_boxes[frame_id] = []
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frame_boxes[frame_id].append([x1, y1, x2, y2])
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for frame, boxes in frame_boxes.items():
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frame_boxes[frame] = np.array(boxes)
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ids = {}
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num_filter_box = 0
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with open(res_file, 'r') as f:
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lines = list(f.readlines())
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with open(res_file, 'w') as f:
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for line in lines:
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if len(line) == 0:
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continue
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elements = line.strip().split(',')
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frame_id, obj_id = elements[:2]
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frame_id = int(frame_id)
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obj_id = int(obj_id)
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x1, y1, w, h = elements[2:6]
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x1, y1, w, h = float(x1), float(y1), float(w), float(h)
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x2 = x1 + w - 1
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y2 = y1 + h - 1
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if obj_id not in ids:
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# track initialization.
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if frame_id not in frame_boxes:
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num_filter_box += 1
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print("filter init box {} {}".format(frame_id, obj_id))
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continue
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pub_dt_boxes = frame_boxes[frame_id]
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dt_box = np.array([[x1, y1, x2, y2]])
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if filter_iou:
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max_iou = bbox_iou(dt_box, pub_dt_boxes).max()
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if max_iou < 0.5:
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num_filter_box += 1
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print("filter init box {} {}".format(frame_id, obj_id))
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continue
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else:
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pub_dt_centers = (pub_dt_boxes[:, :2] + pub_dt_boxes[:, 2:4]) * 0.5
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x_inside = (dt_box[0, 0] <= pub_dt_centers[:, 0]) & (dt_box[0, 2] >= pub_dt_centers[:, 0])
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y_inside = (dt_box[0, 1] <= pub_dt_centers[:, 1]) & (dt_box[0, 3] >= pub_dt_centers[:, 1])
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center_inside: np.ndarray = x_inside & y_inside
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if not center_inside.any():
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num_filter_box += 1
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print("filter init box {} {}".format(frame_id, obj_id))
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continue
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print("save init track {} {}".format(frame_id, obj_id))
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ids[obj_id] = True
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f.write(line)
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print("totally {} boxes are filtered.".format(num_filter_box))
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class Detector(object):
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def __init__(self, args, model=None, seq_num=2):
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self.args = args
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self.detr = model
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self.seq_num = seq_num
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img_list = os.listdir(os.path.join(self.args.mot_path, self.seq_num, 'img1'))
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img_list = [os.path.join(self.args.mot_path, self.seq_num, 'img1', _) for _ in img_list if
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('jpg' in _) or ('png' in _)]
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self.img_list = sorted(img_list)
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self.img_len = len(self.img_list)
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self.tr_tracker = MOTR()
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'''
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common settings
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'''
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self.img_height = 800
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self.img_width = 1536
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self.mean = [0.485, 0.456, 0.406]
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self.std = [0.229, 0.224, 0.225]
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self.save_path = os.path.join(self.args.output_dir, 'results/{}'.format(seq_num))
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os.makedirs(self.save_path, exist_ok=True)
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self.predict_path = os.path.join(self.args.output_dir, 'preds', self.seq_num)
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os.makedirs(self.predict_path, exist_ok=True)
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if os.path.exists(os.path.join(self.predict_path, 'gt.txt')):
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os.remove(os.path.join(self.predict_path, 'gt.txt'))
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def load_img_from_file(self,f_path):
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label_path = f_path.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
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cur_img = cv2.imread(f_path)
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cur_img = cv2.cvtColor(cur_img, cv2.COLOR_BGR2RGB)
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targets = load_label(label_path, cur_img.shape[:2]) if os.path.exists(label_path) else None
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return cur_img, targets
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def init_img(self, img):
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ori_img = img.copy()
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self.seq_h, self.seq_w = img.shape[:2]
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scale = self.img_height / min(self.seq_h, self.seq_w)
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if max(self.seq_h, self.seq_w) * scale > self.img_width:
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scale = self.img_width / max(self.seq_h, self.seq_w)
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target_h = int(self.seq_h * scale)
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target_w = int(self.seq_w * scale)
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img = cv2.resize(img, (target_w, target_h))
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img = F.normalize(F.to_tensor(img), self.mean, self.std)
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img = img.unsqueeze(0)
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return img, ori_img
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@staticmethod
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def filter_dt_by_score(dt_instances: Instances, prob_threshold: float) -> Instances:
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keep = dt_instances.scores > prob_threshold
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return dt_instances[keep]
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@staticmethod
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def filter_dt_by_area(dt_instances: Instances, area_threshold: float) -> Instances:
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wh = dt_instances.boxes[:, 2:4] - dt_instances.boxes[:, 0:2]
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areas = wh[:, 0] * wh[:, 1]
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keep = areas > area_threshold
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return dt_instances[keep]
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@staticmethod
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def write_results(txt_path, frame_id, bbox_xyxy, identities):
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save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
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with open(txt_path, 'a') as f:
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for xyxy, track_id in zip(bbox_xyxy, identities):
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if track_id < 0 or track_id is None:
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continue
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x1, y1, x2, y2 = xyxy
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w, h = x2 - x1, y2 - y1
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line = save_format.format(frame=int(frame_id), id=int(track_id), x1=x1, y1=y1, w=w, h=h)
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f.write(line)
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def eval_seq(self):
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data_root = os.path.join(self.args.mot_path)
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result_filename = os.path.join(self.predict_path, 'gt.txt')
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evaluator = Evaluator(data_root, self.seq_num)
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accs = evaluator.eval_file(result_filename)
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return accs
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@staticmethod
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def visualize_img_with_bbox(img_path, img, dt_instances: Instances, ref_pts=None, gt_boxes=None):
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img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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if dt_instances.has('scores'):
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img_show = draw_bboxes(img, np.concatenate([dt_instances.boxes, dt_instances.scores.reshape(-1, 1)], axis=-1), dt_instances.obj_idxes)
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else:
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img_show = draw_bboxes(img, dt_instances.boxes, dt_instances.obj_idxes)
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# if ref_pts is not None:
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# img_show = draw_points(img_show, ref_pts)
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# if gt_boxes is not None:
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# img_show = draw_bboxes(img_show, gt_boxes, identities=np.ones((len(gt_boxes), )) * -1)
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cv2.imwrite(img_path, img_show)
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def detect(self, prob_threshold=0.2, area_threshold=100, vis=False):
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total_dts = 0
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track_instances = None
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max_id = 0
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# we only consider val split (second half images)
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for i in tqdm(range((int(self.img_len / 2)), self.img_len)):
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# for i in tqdm(range(0, self.img_len)):
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img, targets = self.load_img_from_file(self.img_list[i])
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cur_img, ori_img = self.init_img(img)
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# track_instances = None
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if track_instances is not None:
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track_instances.remove('boxes')
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track_instances.remove('labels')
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res = self.detr.inference_single_image(cur_img.cuda().float(), (self.seq_h, self.seq_w), track_instances)
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track_instances = res['track_instances']
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max_id = max(max_id, track_instances.obj_idxes.max().item())
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print("ref points.shape={}".format(res['ref_pts'].shape))
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all_ref_pts = tensor_to_numpy(res['ref_pts'][0, :, :2])
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dt_instances = track_instances.to(torch.device('cpu'))
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# filter det instances by score.
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dt_instances = self.filter_dt_by_score(dt_instances, prob_threshold)
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dt_instances = self.filter_dt_by_area(dt_instances, area_threshold)
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total_dts += len(dt_instances)
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if vis:
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# for visual
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cur_vis_img_path = os.path.join(self.save_path, 'frame_{:0>8d}.jpg'.format(i))
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gt_boxes = None
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self.visualize_img_with_bbox(cur_vis_img_path, ori_img, dt_instances, ref_pts=all_ref_pts, gt_boxes=gt_boxes)
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|
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tracker_outputs = self.tr_tracker.update(dt_instances)
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|
|
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self.write_results(txt_path=os.path.join(self.predict_path, 'gt.txt'),
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frame_id=(i + 1),
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bbox_xyxy=tracker_outputs[:, :4],
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identities=tracker_outputs[:, 5])
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print("totally {} dts max_id={}".format(total_dts, max_id))
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|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
|
|
args = parser.parse_args()
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|
if args.output_dir:
|
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Path(args.output_dir).mkdir(parents=True, exist_ok=True)
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|
|
|
# load model and weights
|
|
detr, _, _ = build_model(args)
|
|
checkpoint = torch.load(args.resume, map_location='cpu')
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|
detr = load_model(detr, args.resume)
|
|
detr = detr.cuda()
|
|
detr.eval()
|
|
|
|
# seq_nums = ['ADL-Rundle-6', 'ETH-Bahnhof', 'KITTI-13', 'PETS09-S2L1', 'TUD-Stadtmitte', 'ADL-Rundle-8', 'KITTI-17',
|
|
# 'ETH-Pedcross2', 'ETH-Sunnyday', 'TUD-Campus', 'Venice-2']
|
|
seq_nums = ['MOT17-02-SDP',
|
|
'MOT17-04-SDP',
|
|
'MOT17-05-SDP',
|
|
'MOT17-09-SDP',
|
|
'MOT17-10-SDP',
|
|
'MOT17-11-SDP',
|
|
'MOT17-13-SDP']
|
|
accs = []
|
|
seqs = []
|
|
|
|
for seq_num in seq_nums:
|
|
print("solve {}".format(seq_num))
|
|
det = Detector(args, model=detr, seq_num=seq_num)
|
|
det.detect(vis=False)
|
|
accs.append(det.eval_seq())
|
|
seqs.append(seq_num)
|
|
|
|
metrics = mm.metrics.motchallenge_metrics
|
|
mh = mm.metrics.create()
|
|
summary = Evaluator.get_summary(accs, seqs, metrics)
|
|
strsummary = mm.io.render_summary(
|
|
summary,
|
|
formatters=mh.formatters,
|
|
namemap=mm.io.motchallenge_metric_names
|
|
)
|
|
print(strsummary)
|
|
with open("eval_log.txt", 'a') as f:
|
|
print(strsummary, file=f)
|