# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import cv2
import time
import numpy as np
import collections
__all__ = [
'MOTTimer', 'Detection', 'write_mot_results', 'load_det_results',
'preprocess_reid', 'get_crops', 'clip_box', 'scale_coords', 'flow_statistic'
]
class MOTTimer(object):
"""
This class used to compute and print the current FPS while evaling.
"""
def __init__(self, window_size=20):
self.start_time = 0.
self.diff = 0.
self.duration = 0.
self.deque = collections.deque(maxlen=window_size)
def tic(self):
# using time.time instead of time.clock because time time.clock
# does not normalize for multithreading
self.start_time = time.time()
def toc(self, average=True):
self.diff = time.time() - self.start_time
self.deque.append(self.diff)
if average:
self.duration = np.mean(self.deque)
else:
self.duration = np.sum(self.deque)
return self.duration
def clear(self):
self.start_time = 0.
self.diff = 0.
self.duration = 0.
class Detection(object):
"""
This class represents a bounding box detection in a single image.
Args:
tlwh (Tensor): Bounding box in format `(top left x, top left y,
width, height)`.
score (Tensor): Bounding box confidence score.
feature (Tensor): A feature vector that describes the object
contained in this image.
cls_id (Tensor): Bounding box category id.
"""
def __init__(self, tlwh, score, feature, cls_id):
self.tlwh = np.asarray(tlwh, dtype=np.float32)
self.score = float(score)
self.feature = np.asarray(feature, dtype=np.float32)
self.cls_id = int(cls_id)
def to_tlbr(self):
"""
Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
"""
ret = self.tlwh.copy()
ret[2:] += ret[:2]
return ret
def to_xyah(self):
"""
Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
"""
ret = self.tlwh.copy()
ret[:2] += ret[2:] / 2
ret[2] /= ret[3]
return ret
def write_mot_results(filename, results, data_type='mot', num_classes=1):
# support single and multi classes
if data_type in ['mot', 'mcmot']:
save_format = '{frame},{id},{x1},{y1},{w},{h},{score},{cls_id},-1,-1\n'
elif data_type == 'kitti':
save_format = '{frame} {id} car 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
else:
raise ValueError(data_type)
f = open(filename, 'w')
for cls_id in range(num_classes):
for frame_id, tlwhs, tscores, track_ids in results[cls_id]:
if data_type == 'kitti':
frame_id -= 1
for tlwh, score, track_id in zip(tlwhs, tscores, track_ids):
if track_id < 0: continue
if data_type == 'mot':
cls_id = -1
x1, y1, w, h = tlwh
x2, y2 = x1 + w, y1 + h
line = save_format.format(
frame=frame_id,
id=track_id,
x1=x1,
y1=y1,
x2=x2,
y2=y2,
w=w,
h=h,
score=score,
cls_id=cls_id)
f.write(line)
print('MOT results save in {}'.format(filename))
def load_det_results(det_file, num_frames):
assert os.path.exists(det_file) and os.path.isfile(det_file), \
'{} is not exist or not a file.'.format(det_file)
labels = np.loadtxt(det_file, dtype='float32', delimiter=',')
assert labels.shape[1] == 7, \
"Each line of {} should have 7 items: '[frame_id],[x0],[y0],[w],[h],[score],[class_id]'.".format(det_file)
results_list = []
for frame_i in range(num_frames):
results = {'bbox': [], 'score': [], 'cls_id': []}
lables_with_frame = labels[labels[:, 0] == frame_i + 1]
# each line of lables_with_frame:
# [frame_id],[x0],[y0],[w],[h],[score],[class_id]
for l in lables_with_frame:
results['bbox'].append(l[1:5])
results['score'].append(l[5:6])
results['cls_id'].append(l[6:7])
results_list.append(results)
return results_list
def scale_coords(coords, input_shape, im_shape, scale_factor):
# Note: ratio has only one value, scale_factor[0] == scale_factor[1]
#
# This function only used for JDE YOLOv3 or other detectors with
# LetterBoxResize and JDEBBoxPostProcess, coords output from detector had
# not scaled back to the origin image.
ratio = scale_factor[0]
pad_w = (input_shape[1] - int(im_shape[1])) / 2
pad_h = (input_shape[0] - int(im_shape[0])) / 2
coords[:, 0::2] -= pad_w
coords[:, 1::2] -= pad_h
coords[:, 0:4] /= ratio
coords[:, :4] = np.clip(coords[:, :4], a_min=0, a_max=coords[:, :4].max())
return coords.round()
def clip_box(xyxy, ori_image_shape):
H, W = ori_image_shape
xyxy[:, 0::2] = np.clip(xyxy[:, 0::2], a_min=0, a_max=W)
xyxy[:, 1::2] = np.clip(xyxy[:, 1::2], a_min=0, a_max=H)
w = xyxy[:, 2:3] - xyxy[:, 0:1]
h = xyxy[:, 3:4] - xyxy[:, 1:2]
mask = np.logical_and(h > 0, w > 0)
keep_idx = np.nonzero(mask)
return xyxy[keep_idx[0]], keep_idx
def get_crops(xyxy, ori_img, w, h):
crops = []
xyxy = xyxy.astype(np.int64)
ori_img = ori_img.transpose(1, 0, 2) # [h,w,3]->[w,h,3]
for i, bbox in enumerate(xyxy):
crop = ori_img[bbox[0]:bbox[2], bbox[1]:bbox[3], :]
crops.append(crop)
crops = preprocess_reid(crops, w, h)
return crops
def preprocess_reid(imgs,
w=64,
h=192,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]):
im_batch = []
for img in imgs:
img = cv2.resize(img, (w, h))
img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
img_mean = np.array(mean).reshape((3, 1, 1))
img_std = np.array(std).reshape((3, 1, 1))
img -= img_mean
img /= img_std
img = np.expand_dims(img, axis=0)
im_batch.append(img)
im_batch = np.concatenate(im_batch, 0)
return im_batch
def flow_statistic(result,
secs_interval,
do_entrance_counting,
video_fps,
entrance,
id_set,
interval_id_set,
in_id_list,
out_id_list,
prev_center,
records,
data_type='mot',
num_classes=1):
# Count in and out number:
# Use horizontal center line as the entrance just for simplification.
# If a person located in the above the horizontal center line
# at the previous frame and is in the below the line at the current frame,
# the in number is increased by one.
# If a person was in the below the horizontal center line
# at the previous frame and locates in the below the line at the current frame,
# the out number is increased by one.
# TODO: if the entrance is not the horizontal center line,
# the counting method should be optimized.
if do_entrance_counting:
entrance_y = entrance[1] # xmin, ymin, xmax, ymax
frame_id, tlwhs, tscores, track_ids = result
for tlwh, score, track_id in zip(tlwhs, tscores, track_ids):
if track_id < 0: continue
if data_type == 'kitti':
frame_id -= 1
x1, y1, w, h = tlwh
center_x = x1 + w / 2.
center_y = y1 + h / 2.
if track_id in prev_center:
if prev_center[track_id][1] <= entrance_y and \
center_y > entrance_y:
in_id_list.append(track_id)
if prev_center[track_id][1] >= entrance_y and \
center_y < entrance_y:
out_id_list.append(track_id)
prev_center[track_id][0] = center_x
prev_center[track_id][1] = center_y
else:
prev_center[track_id] = [center_x, center_y]
# Count totol number, number at a manual-setting interval
frame_id, tlwhs, tscores, track_ids = result
for tlwh, score, track_id in zip(tlwhs, tscores, track_ids):
if track_id < 0: continue
id_set.add(track_id)
interval_id_set.add(track_id)
# Reset counting at the interval beginning
if frame_id % video_fps == 0 and frame_id / video_fps % secs_interval == 0:
curr_interval_count = len(interval_id_set)
interval_id_set.clear()
info = "Frame id: {}, Total count: {}".format(frame_id, len(id_set))
if do_entrance_counting:
info += ", In count: {}, Out count: {}".format(
len(in_id_list), len(out_id_list))
if frame_id % video_fps == 0 and frame_id / video_fps % secs_interval == 0:
info += ", Count during {} secs: {}".format(secs_interval,
curr_interval_count)
interval_id_set.clear()
print(info)
info += "\n"
records.append(info)
return {
"id_set": id_set,
"interval_id_set": interval_id_set,
"in_id_list": in_id_list,
"out_id_list": out_id_list,
"prev_center": prev_center,
"records": records
}