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