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# Copyright (c) 2022 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 time
import os
import ast
import glob
import yaml
import copy
import numpy as np
import subprocess as sp
from python.keypoint_preprocess import EvalAffine, TopDownEvalAffine, expand_crop
class Times(object):
def __init__(self):
self.time = 0.
# start time
self.st = 0.
# end time
self.et = 0.
def start(self):
self.st = time.time()
def end(self, repeats=1, accumulative=True):
self.et = time.time()
if accumulative:
self.time += (self.et - self.st) / repeats
else:
self.time = (self.et - self.st) / repeats
def reset(self):
self.time = 0.
self.st = 0.
self.et = 0.
def value(self):
return round(self.time, 4)
class PipeTimer(Times):
def __init__(self):
super(PipeTimer, self).__init__()
self.total_time = Times()
self.module_time = {
'det': Times(),
'mot': Times(),
'attr': Times(),
'kpt': Times(),
'video_action': Times(),
'skeleton_action': Times(),
'reid': Times(),
'det_action': Times(),
'cls_action': Times(),
'vehicle_attr': Times(),
'vehicleplate': Times(),
'lanes': Times(),
'vehicle_press': Times(),
'vehicle_retrograde': Times()
}
self.img_num = 0
self.track_num = 0
def get_total_time(self):
total_time = self.total_time.value()
total_time = round(total_time, 4)
average_latency = total_time / max(1, self.img_num)
qps = 0
if total_time > 0:
qps = 1 / average_latency
return total_time, average_latency, qps
def info(self):
total_time, average_latency, qps = self.get_total_time()
print("------------------ Inference Time Info ----------------------")
print("total_time(ms): {}, img_num: {}".format(total_time * 1000,
self.img_num))
for k, v in self.module_time.items():
v_time = round(v.value(), 4)
if v_time > 0 and k in ['det', 'mot', 'video_action']:
print("{} time(ms): {}; per frame average time(ms): {}".format(
k, v_time * 1000, v_time * 1000 / self.img_num))
elif v_time > 0:
print("{} time(ms): {}; per trackid average time(ms): {}".
format(k, v_time * 1000, v_time * 1000 / self.track_num))
print("average latency time(ms): {:.2f}, QPS: {:2f}".format(
average_latency * 1000, qps))
return qps
def report(self, average=False):
dic = {}
dic['total'] = round(self.total_time.value() / max(1, self.img_num),
4) if average else self.total_time.value()
dic['det'] = round(self.module_time['det'].value() /
max(1, self.img_num),
4) if average else self.module_time['det'].value()
dic['mot'] = round(self.module_time['mot'].value() /
max(1, self.img_num),
4) if average else self.module_time['mot'].value()
dic['attr'] = round(self.module_time['attr'].value() /
max(1, self.img_num),
4) if average else self.module_time['attr'].value()
dic['kpt'] = round(self.module_time['kpt'].value() /
max(1, self.img_num),
4) if average else self.module_time['kpt'].value()
dic['video_action'] = self.module_time['video_action'].value()
dic['skeleton_action'] = round(
self.module_time['skeleton_action'].value() / max(1, self.img_num),
4) if average else self.module_time['skeleton_action'].value()
dic['img_num'] = self.img_num
return dic
class PushStream(object):
def __init__(self, pushurl="rtsp://127.0.0.1:8554/"):
self.command = ""
# 自行设置
self.pushurl = pushurl
def initcmd(self, fps, width, height):
self.command = [
'ffmpeg', '-y', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-pix_fmt',
'bgr24', '-s', "{}x{}".format(width, height), '-r', str(fps), '-i',
'-', '-pix_fmt', 'yuv420p', '-f', 'rtsp', self.pushurl
]
self.pipe = sp.Popen(self.command, stdin=sp.PIPE)
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--infer_img or --infer_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
assert infer_dir is None or os.path.isdir(infer_dir), \
"{} is not a directory".format(infer_dir)
# infer_img has a higher priority
if infer_img and os.path.isfile(infer_img):
return [infer_img]
images = set()
infer_dir = os.path.abspath(infer_dir)
assert os.path.isdir(infer_dir), \
"infer_dir {} is not a directory".format(infer_dir)
exts = ['jpg', 'jpeg', 'png', 'bmp']
exts += [ext.upper() for ext in exts]
for ext in exts:
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
images = list(images)
assert len(images) > 0, "no image found in {}".format(infer_dir)
print("Found {} inference images in total.".format(len(images)))
return images
def crop_image_with_det(batch_input, det_res, thresh=0.3):
boxes = det_res['boxes']
score = det_res['boxes'][:, 1]
boxes_num = det_res['boxes_num']
start_idx = 0
crop_res = []
for b_id, input in enumerate(batch_input):
boxes_num_i = boxes_num[b_id]
if boxes_num_i == 0:
continue
boxes_i = boxes[start_idx:start_idx + boxes_num_i, :]
score_i = score[start_idx:start_idx + boxes_num_i]
res = []
for box, s in zip(boxes_i, score_i):
if s > thresh:
crop_image, new_box, ori_box = expand_crop(input, box)
if crop_image is not None:
res.append(crop_image)
crop_res.append(res)
return crop_res
def normal_crop(image, rect):
imgh, imgw, c = image.shape
label, conf, xmin, ymin, xmax, ymax = [int(x) for x in rect.tolist()]
org_rect = [xmin, ymin, xmax, ymax]
if label != 0:
return None, None, None
xmin = max(0, xmin)
ymin = max(0, ymin)
xmax = min(imgw, xmax)
ymax = min(imgh, ymax)
return image[ymin:ymax, xmin:xmax, :], [xmin, ymin, xmax, ymax], org_rect
def crop_image_with_mot(input, mot_res, expand=True):
res = mot_res['boxes']
crop_res = []
new_bboxes = []
ori_bboxes = []
for box in res:
if expand:
crop_image, new_bbox, ori_bbox = expand_crop(input, box[1:])
else:
crop_image, new_bbox, ori_bbox = normal_crop(input, box[1:])
if crop_image is not None:
crop_res.append(crop_image)
new_bboxes.append(new_bbox)
ori_bboxes.append(ori_bbox)
return crop_res, new_bboxes, ori_bboxes
def parse_mot_res(input):
mot_res = []
boxes, scores, ids = input[0]
for box, score, i in zip(boxes[0], scores[0], ids[0]):
xmin, ymin, w, h = box
res = [i, 0, score, xmin, ymin, xmin + w, ymin + h]
mot_res.append(res)
return {'boxes': np.array(mot_res)}
def refine_keypoint_coordinary(kpts, bbox, coord_size):
"""
This function is used to adjust coordinate values to a fixed scale.
"""
tl = bbox[:, 0:2]
wh = bbox[:, 2:] - tl
tl = np.expand_dims(np.transpose(tl, (1, 0)), (2, 3))
wh = np.expand_dims(np.transpose(wh, (1, 0)), (2, 3))
target_w, target_h = coord_size
res = (kpts - tl) / wh * np.expand_dims(
np.array([[target_w], [target_h]]), (2, 3))
return res
def parse_mot_keypoint(input, coord_size):
parsed_skeleton_with_mot = {}
ids = []
skeleton = []
for tracker_id, kpt_seq in input:
ids.append(tracker_id)
kpts = np.array(kpt_seq.kpts, dtype=np.float32)[:, :, :2]
kpts = np.expand_dims(np.transpose(kpts, [2, 0, 1]),
-1) #T, K, C -> C, T, K, 1
bbox = np.array(kpt_seq.bboxes, dtype=np.float32)
skeleton.append(refine_keypoint_coordinary(kpts, bbox, coord_size))
parsed_skeleton_with_mot["mot_id"] = ids
parsed_skeleton_with_mot["skeleton"] = skeleton
return parsed_skeleton_with_mot |