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submit_dance.py
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# ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-research. 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
# ------------------------------------------------------------------------
from copy import deepcopy
import json
import threading
import os
import random
import numpy as np
import argparse
import torchvision.transforms.functional as F
import torch
import cv2
from collections import defaultdict
from tqdm import tqdm
from pathlib import Path
from models import build_model
from util.tool import load_model
from main import get_args_parser
import torch.backends.cudnn as cudnn
from torch.utils.data import Dataset, DataLoader
import sys
sys.path.append('thirdparty/segment-anything')
from segment_anything import build_sam, SamPredictor
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, lable_offset=0):
# Plots one bounding box on image img
# tl = line_thickness or round(
# 0.002 * max(img.shape[0:2])) + 1 # line thickness
tl = 2
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 + lable_offset), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
return img
'''
deep sort 中的画图方法，在原图上进行作画
'''
def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color=False, lable_offset=0):
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, lable_offset=lable_offset)
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 ListImgDataset(Dataset):
def __init__(self, mot_path, img_list, det_db) -> None:
super().__init__()
self.mot_path = mot_path
self.img_list = img_list
self.det_db = det_db
'''
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]
def load_img_from_file(self, f_path):
cur_img = cv2.imread(os.path.join(self.mot_path, f_path))
assert cur_img is not None, f_path
cur_img = cv2.cvtColor(cur_img, cv2.COLOR_BGR2RGB)
proposals = []
im_h, im_w = cur_img.shape[:2]
if len(self.det_db):
for line in self.det_db[f_path[:-4].replace('dancetrack/', 'DanceTrack/') + '.txt']:
l, t, w, h, s = list(map(float, line.split(',')))
proposals.append([(l + w / 2) / im_w,
(t + h / 2) / im_h,
w / im_w,
h / im_h,
s])
return cur_img, torch.as_tensor(proposals).reshape(-1, 5), f_path
def init_img(self, img, proposals):
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, proposals
def __len__(self):
return len(self.img_list)
def __getitem__(self, index): # 加载图像和proposal。并对图像颜色通道转换+resize+normalize+to_tensor。
img, proposals, f_path = self.load_img_from_file(self.img_list[index])
img, ori_img, proposals = self.init_img(img, proposals)
return img, ori_img, proposals, f_path
class Detector(object):
def __init__(self, args, model, vid):
self.args = args
self.detr = model
self.vid = vid
self.seq_num = os.path.basename(vid)
img_list = os.listdir(os.path.join(self.args.mot_path, vid, 'img1'))
img_list = [os.path.join(vid, 'img1', i) for i in img_list if 'jpg' in i]
self.img_list = sorted(img_list)
self.img_len = len(self.img_list)
self.predict_path = os.path.join(self.args.output_dir, args.exp_name)
os.makedirs(self.predict_path, exist_ok=True)
self.sam_predictor = SamPredictor(build_sam(checkpoint="thirdparty/segment-anything/sam_vit_h_4b8939.pth"))
_ = self.sam_predictor.model.to(device='cuda')
self.save_path = 'tmp'
@staticmethod
def filter_dt_by_score(dt_instances, prob_threshold):
keep = dt_instances.scores > prob_threshold
# if keep.sum() % 5 != 0:
# print(dt_instances.scores)
keep &= dt_instances.obj_idxes >= 0
return dt_instances[keep]
@staticmethod
def filter_dt_by_area(dt_instances, area_threshold):
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 visualize_img_with_bbox(img_path, img, dt_instances, ref_pts=None, gt_boxes=None, obj_instances=None):
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)
if obj_instances is not None:
img_show = draw_bboxes(img, np.concatenate([obj_instances.boxes, obj_instances.scores_obj.reshape(-1, 1)], axis=-1), lable_offset=-50)
cv2.imwrite(img_path, img_show)
def detect(self, prob_threshold=0.6, area_threshold=100, vis=False):
track_instances = None
det_db = []
if len(self.args.det_db):
with open(os.path.join(self.args.mot_path, self.args.det_db)) as f:
det_db = json.load(f)
loader = DataLoader(ListImgDataset(self.args.mot_path, self.img_list, det_db), 1, num_workers=2)
lines = defaultdict(list)
total_dts = defaultdict(int)
total_occlusion_dts = defaultdict(int)
print('g_size: %d'%self.args.g_size)
for i, data in enumerate(tqdm(loader)):
cur_img, ori_img, proposals, f_path = [d[0] for d in data]
cur_img, proposals = cur_img.cuda(), proposals.cuda()
if i == 0:
fps = 25
size = ori_img.to(torch.device('cpu')).numpy().shape[:2:-1]
self.videowriter = cv2.VideoWriter('visam.avi', cv2.VideoWriter_fourcc('M','J','P','G'), fps, size)
# track_instances = None
if track_instances is not None:
track_instances.remove('boxes')
# track_instances.remove('labels')
seq_h, seq_w, _ = ori_img.shape
# 内部包含backboe+encode+decode+跟踪匹配关系+跟踪目标过滤（从query中过滤）
res = self.detr.inference_single_image(cur_img, (seq_h, seq_w), track_instances, proposals)
track_instances = res['track_instances']
dt_instances_all = deepcopy(track_instances).get_bn(0)
# filter det instances by score.
dt_instances_all = self.filter_dt_by_score(dt_instances_all, prob_threshold) # 保留置信度比较高的目标（因为motr内部可能会保留相对置信度高一些的目标，但输出需要输出比较高一些）
dt_instances_all = self.filter_dt_by_area(dt_instances_all, area_threshold) # 过滤小目标
active_indx = []
full_indx = torch.arange(len(dt_instances_all), device=dt_instances_all.scores.device)
for id in torch.unique(dt_instances_all.obj_idxes):
indx = torch.where(dt_instances_all.obj_idxes == id)[0]
active_indx.append(full_indx[indx][dt_instances_all.scores[indx].argmax()])
if len(active_indx):
active_indx = torch.stack(active_indx)
dt_instances_all = dt_instances_all[active_indx]
for g_id in range(1):
# dt_instances = dt_instances_all[dt_instances_all.group_ids==g_id]
dt_instances = dt_instances_all
total_dts[g_id] += len(dt_instances)
bbox_xyxy = dt_instances.boxes.tolist()
identities = dt_instances.obj_idxes.tolist()
masks_all = []
self.sam_predictor.set_image(ori_img.to(torch.device('cpu')).numpy().copy())
for bbox, id in zip(np.array(bbox_xyxy), identities):
masks, iou_predictions, low_res_masks = self.sam_predictor.predict(box=bbox)
index_max = iou_predictions.argsort()[0]
masks = np.concatenate([masks[index_max:(index_max+1)], masks[index_max:(index_max+1)], masks[index_max:(index_max+1)]], axis=0)
masks = masks.astype(np.int32)*np.array(colors(id))[:, None, None]
masks_all.append(masks)
self.sam_predictor.reset_image()
if len(masks_all):
masks_sum = masks_all[0].copy()
for m in masks_all[1:]:
masks_sum += m
else:
masks_sum = np.zeros_like(img).transpose(2, 0, 1)
img = ori_img.to(torch.device('cpu')).numpy().copy()[..., ::-1]
img = (img * 0.5 + (masks_sum.transpose(1,2,0) * 30) %128).astype(np.uint8)
for bbox in bbox_xyxy:
cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0,0,255), thickness=3)
self.videowriter.write(img)
if vis:
# for visual
cur_vis_img_path = os.path.join(self.save_path, '%08d.jpg'%i)
gt_boxes = None
all_ref_pts = None # tensor_to_numpy(res['ref_pts'][0, :, :2])
self.visualize_img_with_bbox(cur_vis_img_path, ori_img.to(torch.device('cpu')).numpy().copy(), dt_instances.to(torch.device('cpu')), ref_pts=all_ref_pts, gt_boxes=gt_boxes)
if 'track_instances_ori' in res:
active_track_instances = res['track_instances_ori']
active_track_instances = active_track_instances[active_track_instances.scores_obj >= 0.3]
active_track_instances.scores = active_track_instances.scores_obj
active_track_instances = active_track_instances.to(torch.device('cpu'))
cur_vis_img_path = os.path.join(self.save_path, 'det_%08d.jpg'%i)
self.visualize_img_with_bbox(cur_vis_img_path, ori_img.to(torch.device('cpu')).numpy().copy(), active_track_instances, ref_pts=all_ref_pts, gt_boxes=gt_boxes)
save_format = '{frame},{id},{x1:.2f},{y1:.2f},{w:.2f},{h:.2f},1,-1,-1,-1\n'
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
lines[g_id].append(save_format.format(frame=i + 1, id=track_id, x1=x1, y1=y1, w=w, h=h))
self.videowriter.release()
for g_id in range(1):
os.makedirs(os.path.join(self.predict_path+'%d'%g_id), exist_ok=True)
with open(os.path.join(self.predict_path+'%d'%g_id, f'{self.seq_num}.txt'), 'w') as f:
f.writelines(lines[g_id])
print("{}: totally {} dts {} occlusion dts".format(self.seq_num, total_dts[g_id], total_occlusion_dts[g_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)
print(args)
seed = args.seed
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.cuda.manual_seed(seed)
cudnn.benchmark = True
cudnn.deterministic = True
# load model and weights
detr, _, _ = build_model(args)
detr.track_embed.score_thr = args.update_score_threshold
# detr.track_base = RuntimeTrackerBase(args.score_threshold, args.score_threshold, args.miss_tolerance)
checkpoint = torch.load(args.resume, map_location='cpu')
detr = load_model(detr, args.resume)
detr.eval()
detr = detr.cuda()
# '''for MOT17 submit'''
# sub_dir = 'dancetrack/test'
sub_dir = 'dancetrack/val'
# sub_dir = 'dancetrack/train'
seq_nums = os.listdir(os.path.join(args.mot_path, sub_dir))
if 'seqmap' in seq_nums:
seq_nums.remove('seqmap')
vids = [os.path.join(sub_dir, seq) for seq in seq_nums]
rank = int(os.environ.get('RLAUNCH_REPLICA', '0'))
ws = int(os.environ.get('RLAUNCH_REPLICA_TOTAL', '1'))
vids = vids[rank::ws]
for ith, vid in enumerate(vids):
det = Detector(args, model=detr, vid=vid)
det.detect(args.score_threshold, vis=False)
# break
# for g_id in range(args.g_size):
# os.system("python TrackEval/scripts/run_mot_challenge.py --SPLIT_TO_EVAL val --METRICS HOTA CLEAR Identity --GT_FOLDER ~/yanfeng/data/dancetrack/val --SEQMAP_FILE ~/yanfeng/data/dancetrack/val_seqmap.txt --SKIP_SPLIT_FOL True --TRACKERS_TO_EVAL '' --TRACKER_SUB_FOLDER '' --USE_PARALLEL True --NUM_PARALLEL_CORES 8 --PLOT_CURVES False --TRACKERS_FOLDER %s"%(det.predict_path+'%d'%g_id))
import sys
sys.path.append("/mnt/dolphinfs/hdd_pool/docker/user/hadoop-vacv/yanfeng/project/MOTRv2/MOTRv3/TrackEval/scripts")
import run_mot_challenge
for g_id in range(1):
res_eval = run_mot_challenge.main(SPLIT_TO_EVAL="val",
METRICS=['HOTA', 'CLEAR', 'Identity'],
GT_FOLDER="/mnt/dolphinfs/hdd_pool/docker/user/hadoop-vacv/yanfeng/data/dancetrack/val",
SEQMAP_FILE="/mnt/dolphinfs/hdd_pool/docker/user/hadoop-vacv/yanfeng/data/dancetrack/val_seqmap.txt",
SKIP_SPLIT_FOL=True,
TRACKERS_TO_EVAL=[''],
TRACKER_SUB_FOLDER='',
USE_PARALLEL=True,
NUM_PARALLEL_CORES=8,
PLOT_CURVES=False,
TRACKERS_FOLDER="%s"%(det.predict_path+'%d'%g_id)
)
print(float(res_eval[0]['MotChallenge2DBox']['']['COMBINED_SEQ']['pedestrian']['summaries'][0]['HOTA']))