feat: 新增PaddlePaddle检测支持，重构项目架构
1. 新增concurrently依赖用于并行启动服务 2. 新增服务器启动脚本统一管理环境变量和虚拟环境 3. 新增PaddlePaddle推理引擎和配套工具代码 4. 新增抽烟检测Paddle模型支持，完善模型管理 5. 重构开发启动脚本，优化开发体验 6. 更新.gitignore排除不必要的外部目录和缓存 7. 完善文档说明，新增PaddlePaddle部署指南
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wwh
301
third-party/paddle-inference/mot_keypoint_unite_infer.py
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third-party/paddle-inference/mot_keypoint_unite_infer.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import json
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import cv2
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import math
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import numpy as np
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import paddle
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import yaml
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import copy
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from collections import defaultdict
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from mot_keypoint_unite_utils import argsparser
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from preprocess import decode_image
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from infer import print_arguments, get_test_images, bench_log
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from mot_sde_infer import SDE_Detector
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from mot_jde_infer import JDE_Detector, MOT_JDE_SUPPORT_MODELS
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from keypoint_infer import KeyPointDetector, KEYPOINT_SUPPORT_MODELS
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from det_keypoint_unite_infer import predict_with_given_det
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from visualize import visualize_pose
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from benchmark_utils import PaddleInferBenchmark
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from utils import get_current_memory_mb
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from keypoint_postprocess import translate_to_ori_images
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# add python path
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import sys
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
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sys.path.insert(0, parent_path)
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from pptracking.python.mot.visualize import plot_tracking, plot_tracking_dict
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from pptracking.python.mot.utils import MOTTimer as FPSTimer
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def convert_mot_to_det(tlwhs, scores):
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results = {}
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num_mot = len(tlwhs)
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xyxys = copy.deepcopy(tlwhs)
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for xyxy in xyxys.copy():
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xyxy[2:] = xyxy[2:] + xyxy[:2]
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# support single class now
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results['boxes'] = np.vstack(
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[np.hstack([0, scores[i], xyxys[i]]) for i in range(num_mot)])
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results['boxes_num'] = np.array([num_mot])
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return results
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def mot_topdown_unite_predict(mot_detector,
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topdown_keypoint_detector,
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image_list,
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keypoint_batch_size=1,
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save_res=False):
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det_timer = mot_detector.get_timer()
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store_res = []
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image_list.sort()
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num_classes = mot_detector.num_classes
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for i, img_file in enumerate(image_list):
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# Decode image in advance in mot + pose prediction
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det_timer.preprocess_time_s.start()
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image, _ = decode_image(img_file, {})
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det_timer.preprocess_time_s.end()
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if FLAGS.run_benchmark:
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mot_results = mot_detector.predict_image(
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[image], run_benchmark=True, repeats=10)
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cm, gm, gu = get_current_memory_mb()
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mot_detector.cpu_mem += cm
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mot_detector.gpu_mem += gm
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mot_detector.gpu_util += gu
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else:
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mot_results = mot_detector.predict_image([image], visual=False)
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online_tlwhs, online_scores, online_ids = mot_results[
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0] # only support bs=1 in MOT model
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results = convert_mot_to_det(
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online_tlwhs[0],
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online_scores[0]) # only support single class for mot + pose
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if results['boxes_num'] == 0:
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continue
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keypoint_res = predict_with_given_det(
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image, results, topdown_keypoint_detector, keypoint_batch_size,
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FLAGS.run_benchmark)
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if save_res:
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save_name = img_file if isinstance(img_file, str) else i
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store_res.append([
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save_name, keypoint_res['bbox'],
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[keypoint_res['keypoint'][0], keypoint_res['keypoint'][1]]
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])
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if FLAGS.run_benchmark:
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cm, gm, gu = get_current_memory_mb()
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topdown_keypoint_detector.cpu_mem += cm
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topdown_keypoint_detector.gpu_mem += gm
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topdown_keypoint_detector.gpu_util += gu
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else:
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if not os.path.exists(FLAGS.output_dir):
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os.makedirs(FLAGS.output_dir)
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visualize_pose(
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img_file,
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keypoint_res,
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visual_thresh=FLAGS.keypoint_threshold,
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save_dir=FLAGS.output_dir)
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if save_res:
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"""
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1) store_res: a list of image_data
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2) image_data: [imageid, rects, [keypoints, scores]]
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3) rects: list of rect [xmin, ymin, xmax, ymax]
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4) keypoints: 17(joint numbers)*[x, y, conf], total 51 data in list
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5) scores: mean of all joint conf
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"""
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with open("det_keypoint_unite_image_results.json", 'w') as wf:
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json.dump(store_res, wf, indent=4)
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def mot_topdown_unite_predict_video(mot_detector,
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topdown_keypoint_detector,
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camera_id,
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keypoint_batch_size=1,
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save_res=False):
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video_name = 'output.mp4'
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if camera_id != -1:
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capture = cv2.VideoCapture(camera_id)
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else:
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capture = cv2.VideoCapture(FLAGS.video_file)
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video_name = os.path.split(FLAGS.video_file)[-1]
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# Get Video info : resolution, fps, frame count
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width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = int(capture.get(cv2.CAP_PROP_FPS))
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frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
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print("fps: %d, frame_count: %d" % (fps, frame_count))
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if not os.path.exists(FLAGS.output_dir):
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os.makedirs(FLAGS.output_dir)
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out_path = os.path.join(FLAGS.output_dir, video_name)
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fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
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writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
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frame_id = 0
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timer_mot, timer_kp, timer_mot_kp = FPSTimer(), FPSTimer(), FPSTimer()
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num_classes = mot_detector.num_classes
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assert num_classes == 1, 'Only one category mot model supported for uniting keypoint deploy.'
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data_type = 'mot'
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while (1):
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ret, frame = capture.read()
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if not ret:
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break
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if frame_id % 10 == 0:
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print('Tracking frame: %d' % (frame_id))
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frame_id += 1
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timer_mot_kp.tic()
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# mot model
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timer_mot.tic()
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frame2 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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mot_results = mot_detector.predict_image([frame2], visual=False)
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timer_mot.toc()
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online_tlwhs, online_scores, online_ids = mot_results[0]
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results = convert_mot_to_det(
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online_tlwhs[0],
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online_scores[0]) # only support single class for mot + pose
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if results['boxes_num'] == 0:
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continue
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# keypoint model
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timer_kp.tic()
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keypoint_res = predict_with_given_det(
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frame2, results, topdown_keypoint_detector, keypoint_batch_size,
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FLAGS.run_benchmark)
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timer_kp.toc()
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timer_mot_kp.toc()
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kp_fps = 1. / timer_kp.duration
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mot_kp_fps = 1. / timer_mot_kp.duration
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im = visualize_pose(
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frame,
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keypoint_res,
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visual_thresh=FLAGS.keypoint_threshold,
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returnimg=True,
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ids=online_ids[0])
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im = plot_tracking_dict(
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im,
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num_classes,
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online_tlwhs,
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online_ids,
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online_scores,
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frame_id=frame_id,
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fps=mot_kp_fps)
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writer.write(im)
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if camera_id != -1:
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cv2.imshow('Tracking and keypoint results', im)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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writer.release()
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print('output_video saved to: {}'.format(out_path))
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def main():
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deploy_file = os.path.join(FLAGS.mot_model_dir, 'infer_cfg.yml')
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with open(deploy_file) as f:
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yml_conf = yaml.safe_load(f)
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arch = yml_conf['arch']
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mot_detector_func = 'SDE_Detector'
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if arch in MOT_JDE_SUPPORT_MODELS:
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mot_detector_func = 'JDE_Detector'
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mot_detector = eval(mot_detector_func)(FLAGS.mot_model_dir,
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FLAGS.tracker_config,
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device=FLAGS.device,
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run_mode=FLAGS.run_mode,
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batch_size=1,
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trt_min_shape=FLAGS.trt_min_shape,
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trt_max_shape=FLAGS.trt_max_shape,
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trt_opt_shape=FLAGS.trt_opt_shape,
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trt_calib_mode=FLAGS.trt_calib_mode,
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cpu_threads=FLAGS.cpu_threads,
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enable_mkldnn=FLAGS.enable_mkldnn,
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threshold=FLAGS.mot_threshold,
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output_dir=FLAGS.output_dir)
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topdown_keypoint_detector = KeyPointDetector(
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FLAGS.keypoint_model_dir,
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device=FLAGS.device,
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run_mode=FLAGS.run_mode,
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batch_size=FLAGS.keypoint_batch_size,
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trt_min_shape=FLAGS.trt_min_shape,
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trt_max_shape=FLAGS.trt_max_shape,
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trt_opt_shape=FLAGS.trt_opt_shape,
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trt_calib_mode=FLAGS.trt_calib_mode,
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cpu_threads=FLAGS.cpu_threads,
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enable_mkldnn=FLAGS.enable_mkldnn,
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threshold=FLAGS.keypoint_threshold,
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output_dir=FLAGS.output_dir,
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use_dark=FLAGS.use_dark)
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keypoint_arch = topdown_keypoint_detector.pred_config.arch
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assert KEYPOINT_SUPPORT_MODELS[
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keypoint_arch] == 'keypoint_topdown', 'MOT-Keypoint unite inference only supports topdown models.'
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# predict from video file or camera video stream
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if FLAGS.video_file is not None or FLAGS.camera_id != -1:
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mot_topdown_unite_predict_video(
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mot_detector, topdown_keypoint_detector, FLAGS.camera_id,
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FLAGS.keypoint_batch_size, FLAGS.save_res)
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else:
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# predict from image
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img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
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mot_topdown_unite_predict(mot_detector, topdown_keypoint_detector,
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img_list, FLAGS.keypoint_batch_size,
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FLAGS.save_res)
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if not FLAGS.run_benchmark:
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mot_detector.det_times.info(average=True)
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topdown_keypoint_detector.det_times.info(average=True)
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else:
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mode = FLAGS.run_mode
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mot_model_dir = FLAGS.mot_model_dir
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mot_model_info = {
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'model_name': mot_model_dir.strip('/').split('/')[-1],
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'precision': mode.split('_')[-1]
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}
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bench_log(mot_detector, img_list, mot_model_info, name='MOT')
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keypoint_model_dir = FLAGS.keypoint_model_dir
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keypoint_model_info = {
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'model_name': keypoint_model_dir.strip('/').split('/')[-1],
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'precision': mode.split('_')[-1]
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}
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bench_log(topdown_keypoint_detector, img_list, keypoint_model_info,
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FLAGS.keypoint_batch_size, 'KeyPoint')
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if __name__ == '__main__':
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paddle.enable_static()
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parser = argsparser()
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FLAGS = parser.parse_args()
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print_arguments(FLAGS)
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FLAGS.device = FLAGS.device.upper()
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assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
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], "device should be CPU, GPU, NPU or XPU"
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main()
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