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eval.py
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eval.py
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# ------------------------------------------------------------------------
# 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 [email protected]
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 <http:https://www.gnu.org/licenses/>.
"""
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 models.structures import Instances
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
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), 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()
class MOTR(object):
def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
pass
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:
return np.concatenate(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, 'MOT15/images/train', self.seq_num, 'img1'))
img_list = [os.path.join(self.args.mot_path, 'MOT15/images/train', 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, 'MOT15/images/train')
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=0.7, area_threshold=100, vis=False):
total_dts = 0
track_instances = None
max_id = 0
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())
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_{}.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']
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=True)
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)