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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
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paddle_detection/deploy/python/mot_sde_infer.py Normal file
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# 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 time
import yaml
import cv2
import numpy as np
from collections import defaultdict
import paddle
from benchmark_utils import PaddleInferBenchmark
from preprocess import decode_image
from utils import argsparser, Timer, get_current_memory_mb
from infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig, load_predictor
# add python path
import sys
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
sys.path.insert(0, parent_path)
from pptracking.python.mot import JDETracker, DeepSORTTracker
from pptracking.python.mot.utils import MOTTimer, write_mot_results, get_crops, clip_box
from pptracking.python.mot.visualize import plot_tracking, plot_tracking_dict
class SDE_Detector(Detector):
"""
Args:
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
tracker_config (str): tracker config path
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
batch_size (int): size of pre batch in inference
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
output_dir (string): The path of output, default as 'output'
threshold (float): Score threshold of the detected bbox, default as 0.5
save_images (bool): Whether to save visualization image results, default as False
save_mot_txts (bool): Whether to save tracking results (txt), default as False
reid_model_dir (str): reid model dir, default None for ByteTrack, but set for DeepSORT
"""
def __init__(self,
model_dir,
tracker_config,
device='CPU',
run_mode='paddle',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_shape=640,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
output_dir='output',
threshold=0.5,
save_images=False,
save_mot_txts=False,
reid_model_dir=None):
super(SDE_Detector, self).__init__(
model_dir=model_dir,
device=device,
run_mode=run_mode,
batch_size=batch_size,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn,
output_dir=output_dir,
threshold=threshold, )
self.save_images = save_images
self.save_mot_txts = save_mot_txts
assert batch_size == 1, "MOT model only supports batch_size=1."
self.det_times = Timer(with_tracker=True)
self.num_classes = len(self.pred_config.labels)
# reid config
self.use_reid = False if reid_model_dir is None else True
if self.use_reid:
self.reid_pred_config = self.set_config(reid_model_dir)
self.reid_predictor, self.config = load_predictor(
reid_model_dir,
run_mode=run_mode,
batch_size=50, # reid_batch_size
min_subgraph_size=self.reid_pred_config.min_subgraph_size,
device=device,
use_dynamic_shape=self.reid_pred_config.use_dynamic_shape,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
else:
self.reid_pred_config = None
self.reid_predictor = None
assert tracker_config is not None, 'Note that tracker_config should be set.'
self.tracker_config = tracker_config
tracker_cfg = yaml.safe_load(open(self.tracker_config))
cfg = tracker_cfg[tracker_cfg['type']]
# tracker config
self.use_deepsort_tracker = True if tracker_cfg[
'type'] == 'DeepSORTTracker' else False
if self.use_deepsort_tracker:
# use DeepSORTTracker
if self.reid_pred_config is not None and hasattr(
self.reid_pred_config, 'tracker'):
cfg = self.reid_pred_config.tracker
budget = cfg.get('budget', 100)
max_age = cfg.get('max_age', 30)
max_iou_distance = cfg.get('max_iou_distance', 0.7)
matching_threshold = cfg.get('matching_threshold', 0.2)
min_box_area = cfg.get('min_box_area', 0)
vertical_ratio = cfg.get('vertical_ratio', 0)
self.tracker = DeepSORTTracker(
budget=budget,
max_age=max_age,
max_iou_distance=max_iou_distance,
matching_threshold=matching_threshold,
min_box_area=min_box_area,
vertical_ratio=vertical_ratio, )
else:
# use ByteTracker
use_byte = cfg.get('use_byte', False)
det_thresh = cfg.get('det_thresh', 0.3)
min_box_area = cfg.get('min_box_area', 0)
vertical_ratio = cfg.get('vertical_ratio', 0)
match_thres = cfg.get('match_thres', 0.9)
conf_thres = cfg.get('conf_thres', 0.6)
low_conf_thres = cfg.get('low_conf_thres', 0.1)
self.tracker = JDETracker(
use_byte=use_byte,
det_thresh=det_thresh,
num_classes=self.num_classes,
min_box_area=min_box_area,
vertical_ratio=vertical_ratio,
match_thres=match_thres,
conf_thres=conf_thres,
low_conf_thres=low_conf_thres, )
def postprocess(self, inputs, result):
# postprocess output of predictor
np_boxes_num = result['boxes_num']
if np_boxes_num[0] <= 0:
print('[WARNNING] No object detected.')
result = {'boxes': np.zeros([0, 6]), 'boxes_num': [0]}
result = {k: v for k, v in result.items() if v is not None}
return result
def reidprocess(self, det_results, repeats=1):
pred_dets = det_results['boxes']
pred_xyxys = pred_dets[:, 2:6]
ori_image = det_results['ori_image']
ori_image_shape = ori_image.shape[:2]
pred_xyxys, keep_idx = clip_box(pred_xyxys, ori_image_shape)
if len(keep_idx[0]) == 0:
det_results['boxes'] = np.zeros((1, 6), dtype=np.float32)
det_results['embeddings'] = None
return det_results
pred_dets = pred_dets[keep_idx[0]]
pred_xyxys = pred_dets[:, 2:6]
w, h = self.tracker.input_size
crops = get_crops(pred_xyxys, ori_image, w, h)
# to keep fast speed, only use topk crops
crops = crops[:50] # reid_batch_size
det_results['crops'] = np.array(crops).astype('float32')
det_results['boxes'] = pred_dets[:50]
input_names = self.reid_predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.reid_predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(det_results[input_names[i]])
# model prediction
for i in range(repeats):
self.reid_predictor.run()
output_names = self.reid_predictor.get_output_names()
feature_tensor = self.reid_predictor.get_output_handle(output_names[
0])
pred_embs = feature_tensor.copy_to_cpu()
det_results['embeddings'] = pred_embs
return det_results
def tracking(self, det_results):
pred_dets = det_results['boxes'] # 'cls_id, score, x0, y0, x1, y1'
pred_embs = det_results.get('embeddings', None)
if self.use_deepsort_tracker:
# use DeepSORTTracker, only support singe class
self.tracker.predict()
online_targets = self.tracker.update(pred_dets, pred_embs)
online_tlwhs, online_scores, online_ids = [], [], []
for t in online_targets:
if not t.is_confirmed() or t.time_since_update > 1:
continue
tlwh = t.to_tlwh()
tscore = t.score
tid = t.track_id
if self.tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
3] > self.tracker.vertical_ratio:
continue
online_tlwhs.append(tlwh)
online_scores.append(tscore)
online_ids.append(tid)
tracking_outs = {
'online_tlwhs': online_tlwhs,
'online_scores': online_scores,
'online_ids': online_ids,
}
return tracking_outs
else:
# use ByteTracker, support multiple class
online_tlwhs = defaultdict(list)
online_scores = defaultdict(list)
online_ids = defaultdict(list)
online_targets_dict = self.tracker.update(pred_dets, pred_embs)
for cls_id in range(self.num_classes):
online_targets = online_targets_dict[cls_id]
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
tscore = t.score
if tlwh[2] * tlwh[3] <= self.tracker.min_box_area:
continue
if self.tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
3] > self.tracker.vertical_ratio:
continue
online_tlwhs[cls_id].append(tlwh)
online_ids[cls_id].append(tid)
online_scores[cls_id].append(tscore)
tracking_outs = {
'online_tlwhs': online_tlwhs,
'online_scores': online_scores,
'online_ids': online_ids,
}
return tracking_outs
def predict_image(self,
image_list,
run_benchmark=False,
repeats=1,
visual=True,
seq_name=None):
num_classes = self.num_classes
image_list.sort()
ids2names = self.pred_config.labels
mot_results = []
for frame_id, img_file in enumerate(image_list):
batch_image_list = [img_file] # bs=1 in MOT model
frame, _ = decode_image(img_file, {})
if run_benchmark:
# preprocess
inputs = self.preprocess(batch_image_list) # warmup
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(batch_image_list)
self.det_times.preprocess_time_s.end()
# model prediction
result_warmup = self.predict(repeats=repeats) # warmup
self.det_times.inference_time_s.start()
result = self.predict(repeats=repeats)
self.det_times.inference_time_s.end(repeats=repeats)
# postprocess
result_warmup = self.postprocess(inputs, result) # warmup
self.det_times.postprocess_time_s.start()
det_result = self.postprocess(inputs, result)
self.det_times.postprocess_time_s.end()
# tracking
if self.use_reid:
det_result['frame_id'] = frame_id
det_result['seq_name'] = seq_name
det_result['ori_image'] = frame
det_result = self.reidprocess(det_result)
result_warmup = self.tracking(det_result)
self.det_times.tracking_time_s.start()
if self.use_reid:
det_result = self.reidprocess(det_result)
tracking_outs = self.tracking(det_result)
self.det_times.tracking_time_s.end()
self.det_times.img_num += 1
cm, gm, gu = get_current_memory_mb()
self.cpu_mem += cm
self.gpu_mem += gm
self.gpu_util += gu
else:
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(batch_image_list)
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
result = self.predict()
self.det_times.inference_time_s.end()
self.det_times.postprocess_time_s.start()
det_result = self.postprocess(inputs, result)
self.det_times.postprocess_time_s.end()
# tracking process
self.det_times.tracking_time_s.start()
if self.use_reid:
det_result['frame_id'] = frame_id
det_result['seq_name'] = seq_name
det_result['ori_image'] = frame
det_result = self.reidprocess(det_result)
tracking_outs = self.tracking(det_result)
self.det_times.tracking_time_s.end()
self.det_times.img_num += 1
online_tlwhs = tracking_outs['online_tlwhs']
online_scores = tracking_outs['online_scores']
online_ids = tracking_outs['online_ids']
mot_results.append([online_tlwhs, online_scores, online_ids])
if visual:
if len(image_list) > 1 and frame_id % 10 == 0:
print('Tracking frame {}'.format(frame_id))
frame, _ = decode_image(img_file, {})
if isinstance(online_tlwhs, defaultdict):
im = plot_tracking_dict(
frame,
num_classes,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
ids2names=ids2names)
else:
im = plot_tracking(
frame,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
ids2names=ids2names)
save_dir = os.path.join(self.output_dir, seq_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(
os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
return mot_results
def predict_video(self, video_file, camera_id):
video_out_name = 'output.mp4'
if camera_id != -1:
capture = cv2.VideoCapture(camera_id)
else:
capture = cv2.VideoCapture(video_file)
video_out_name = os.path.split(video_file)[-1]
# Get Video info : resolution, fps, frame count
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(capture.get(cv2.CAP_PROP_FPS))
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print("fps: %d, frame_count: %d" % (fps, frame_count))
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
out_path = os.path.join(self.output_dir, video_out_name)
video_format = 'mp4v'
fourcc = cv2.VideoWriter_fourcc(*video_format)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
frame_id = 1
timer = MOTTimer()
results = defaultdict(list)
num_classes = self.num_classes
data_type = 'mcmot' if num_classes > 1 else 'mot'
ids2names = self.pred_config.labels
while (1):
ret, frame = capture.read()
if not ret:
break
if frame_id % 10 == 0:
print('Tracking frame: %d' % (frame_id))
frame_id += 1
timer.tic()
seq_name = video_out_name.split('.')[0]
mot_results = self.predict_image(
[frame[:, :, ::-1]], visual=False, seq_name=seq_name)
timer.toc()
# bs=1 in MOT model
online_tlwhs, online_scores, online_ids = mot_results[0]
fps = 1. / timer.duration
if self.use_deepsort_tracker:
# use DeepSORTTracker, only support singe class
results[0].append(
(frame_id + 1, online_tlwhs, online_scores, online_ids))
im = plot_tracking(
frame,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
fps=fps,
ids2names=ids2names)
else:
# use ByteTracker, support multiple class
for cls_id in range(num_classes):
results[cls_id].append(
(frame_id + 1, online_tlwhs[cls_id],
online_scores[cls_id], online_ids[cls_id]))
im = plot_tracking_dict(
frame,
num_classes,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
fps=fps,
ids2names=ids2names)
writer.write(im)
if camera_id != -1:
cv2.imshow('Mask Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if self.save_mot_txts:
result_filename = os.path.join(
self.output_dir, video_out_name.split('.')[-2] + '.txt')
write_mot_results(result_filename, results)
writer.release()
def main():
deploy_file = os.path.join(FLAGS.model_dir, 'infer_cfg.yml')
with open(deploy_file) as f:
yml_conf = yaml.safe_load(f)
arch = yml_conf['arch']
detector = SDE_Detector(
FLAGS.model_dir,
tracker_config=FLAGS.tracker_config,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=1,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn,
output_dir=FLAGS.output_dir,
threshold=FLAGS.threshold,
save_images=FLAGS.save_images,
save_mot_txts=FLAGS.save_mot_txts, )
# predict from video file or camera video stream
if FLAGS.video_file is not None or FLAGS.camera_id != -1:
detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
else:
# predict from image
if FLAGS.image_dir is None and FLAGS.image_file is not None:
assert FLAGS.batch_size == 1, "--batch_size should be 1 in MOT models."
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
seq_name = FLAGS.image_dir.split('/')[-1]
detector.predict_image(
img_list, FLAGS.run_benchmark, repeats=10, seq_name=seq_name)
if not FLAGS.run_benchmark:
detector.det_times.info(average=True)
else:
mode = FLAGS.run_mode
model_dir = FLAGS.model_dir
model_info = {
'model_name': model_dir.strip('/').split('/')[-1],
'precision': mode.split('_')[-1]
}
bench_log(detector, img_list, model_info, name='MOT')
if __name__ == '__main__':
paddle.enable_static()
parser = argsparser()
FLAGS = parser.parse_args()
print_arguments(FLAGS)
FLAGS.device = FLAGS.device.upper()
assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
], "device should be CPU, GPU, NPU or XPU"
main()