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import os
import sys
os.environ["TOKENIZERS_PARALLELISM"] = "false"
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, project_root)
import gc
import resource
import argparse
import cv2
import tqdm
import torch
from torch.multiprocessing import Pool, set_start_method
import mmcv
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import init_detector
from mmdet.registry import VISUALIZERS
from mmcv.ops.nms import batched_nms
import masa
from masa.apis import inference_masa, init_masa, inference_detector, build_test_pipeline
from masa.models.sam import SamPredictor, sam_model_registry
from utils import filter_and_update_tracks
import warnings
warnings.filterwarnings('ignore')
# Ensure the right start method for multiprocessing
try:
set_start_method('spawn')
except RuntimeError:
pass
def set_file_descriptor_limit(limit):
soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (limit, hard))
# Set the file descriptor limit to 65536
set_file_descriptor_limit(65536)
def visualize_frame(args, visualizer, frame, track_result, frame_idx, fps=None):
visualizer.add_datasample(
name='video_' + str(frame_idx),
image=frame[:, :, ::-1],
data_sample=track_result[0],
draw_gt=False,
show=False,
out_file=None,
pred_score_thr=args.score_thr,
fps=fps,)
frame = visualizer.get_image()
gc.collect()
return frame
def parse_args():
parser = argparse.ArgumentParser(description='MASA video demo')
parser.add_argument('video', help='Video file')
parser.add_argument('--det_config', help='Detector Config file')
parser.add_argument('--masa_config', help='Masa Config file')
parser.add_argument('--det_checkpoint', help='Detector Checkpoint file')
parser.add_argument('--masa_checkpoint', help='Masa Checkpoint file')
parser.add_argument( '--device', default='cuda:0', help='Device used for inference')
parser.add_argument('--score-thr', type=float, default=0.2, help='Bbox score threshold')
parser.add_argument('--out', type=str, help='Output video file')
parser.add_argument('--save_dir', type=str, help='Output for video frames')
parser.add_argument('--texts', help='text prompt')
parser.add_argument('--line_width', type=int, default=5, help='Line width')
parser.add_argument('--unified', action='store_true', help='Use unified model, which means the masa adapter is built upon the detector model.')
parser.add_argument('--detector_type', type=str, default='mmdet', help='Choose detector type')
parser.add_argument('--fp16', action='store_true', help='Activation fp16 mode')
parser.add_argument('--no-post', action='store_true', help='Do not post-process the results ')
parser.add_argument('--show_fps', action='store_true', help='Visualize the fps')
parser.add_argument('--sam_mask', action='store_true', help='Use SAM to generate mask for segmentation tracking')
parser.add_argument('--sam_path', type=str, default='saved_models/pretrain_weights/sam_vit_h_4b8939.pth', help='Default path for SAM models')
parser.add_argument('--sam_type', type=str, default='vit_h', help='Default type for SAM models')
parser.add_argument(
'--wait-time',
type=float,
default=1,
help='The interval of show (s), 0 is block')
args = parser.parse_args()
return args
def main():
args = parse_args()
assert args.out, \
('Please specify at least one operation (save the '
'video) with the argument "--out" ')
# build the model from a config file and a checkpoint file
if args.unified:
masa_model = init_masa(args.masa_config, args.masa_checkpoint, device=args.device)
else:
det_model = init_detector(args.det_config, args.det_checkpoint, palette='random', device=args.device)
masa_model = init_masa(args.masa_config, args.masa_checkpoint, device=args.device)
# build test pipeline
det_model.cfg.test_dataloader.dataset.pipeline[
0].type = 'mmdet.LoadImageFromNDArray'
test_pipeline = Compose(det_model.cfg.test_dataloader.dataset.pipeline)
if args.sam_mask:
print('Loading SAM model...')
device = args.device
sam_model = sam_model_registry[args.sam_type](args.sam_path)
sam_predictor = SamPredictor(sam_model.to(device))
video_reader = mmcv.VideoReader(args.video)
video_writer = None
#### parsing the text input
texts = args.texts
if texts is not None:
masa_test_pipeline = build_test_pipeline(masa_model.cfg, with_text=True)
else:
masa_test_pipeline = build_test_pipeline(masa_model.cfg)
if texts is not None:
masa_model.cfg.visualizer['texts'] = texts
else:
masa_model.cfg.visualizer['texts'] = det_model.dataset_meta['classes']
# init visualizer
masa_model.cfg.visualizer['save_dir'] = args.save_dir
masa_model.cfg.visualizer['line_width'] = args.line_width
if args.sam_mask:
masa_model.cfg.visualizer['alpha'] = 0.5
visualizer = VISUALIZERS.build(masa_model.cfg.visualizer)
if args.out:
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_writer = cv2.VideoWriter(
args.out, fourcc, video_reader.fps,
(video_reader.width, video_reader.height))
frame_idx = 0
instances_list = []
frames = []
fps_list = []
for frame in track_iter_progress((video_reader, len(video_reader))):
# unified models mean that masa build upon and reuse the foundation model's backbone features for tracking
if args.unified:
track_result = inference_masa(masa_model, frame,
frame_id=frame_idx,
video_len=len(video_reader),
test_pipeline=masa_test_pipeline,
text_prompt=texts,
fp16=args.fp16,
detector_type=args.detector_type,
show_fps=args.show_fps)
if args.show_fps:
track_result, fps = track_result
else:
if args.detector_type == 'mmdet':
result = inference_detector(det_model, frame,
text_prompt=texts,
test_pipeline=test_pipeline,
fp16=args.fp16)
# Perfom inter-class NMS to remove nosiy detections
det_bboxes, keep_idx = batched_nms(boxes=result.pred_instances.bboxes,
scores=result.pred_instances.scores,
idxs=result.pred_instances.labels,
class_agnostic=True,
nms_cfg=dict(type='nms',
iou_threshold=0.5,
class_agnostic=True,
split_thr=100000))
det_bboxes = torch.cat([det_bboxes,
result.pred_instances.scores[keep_idx].unsqueeze(1)],
dim=1)
det_labels = result.pred_instances.labels[keep_idx]
track_result = inference_masa(masa_model, frame, frame_id=frame_idx,
video_len=len(video_reader),
test_pipeline=masa_test_pipeline,
det_bboxes=det_bboxes,
det_labels=det_labels,
fp16=args.fp16,
show_fps=args.show_fps)
if args.show_fps:
track_result, fps = track_result
frame_idx += 1
if 'masks' in track_result[0].pred_track_instances:
if len(track_result[0].pred_track_instances.masks) >0:
track_result[0].pred_track_instances.masks = torch.stack(track_result[0].pred_track_instances.masks, dim=0)
track_result[0].pred_track_instances.masks = track_result[0].pred_track_instances.masks.cpu().numpy()
track_result[0].pred_track_instances.bboxes = track_result[0].pred_track_instances.bboxes.to(torch.float32)
instances_list.append(track_result.to('cpu'))
frames.append(frame)
if args.show_fps:
fps_list.append(fps)
if not args.no_post:
instances_list = filter_and_update_tracks(instances_list, (frame.shape[1], frame.shape[0]))
if args.sam_mask:
print('Start to generate mask using SAM!')
for idx, (frame, track_result) in tqdm.tqdm(enumerate(zip(frames, instances_list))):
track_result = track_result.to(device)
track_result[0].pred_track_instances.instances_id = track_result[0].pred_track_instances.instances_id.to(device)
track_result[0].pred_track_instances = track_result[0].pred_track_instances[(track_result[0].pred_track_instances.scores.float() > args.score_thr).to(device)]
input_boxes = track_result[0].pred_track_instances.bboxes
if len(input_boxes) == 0:
continue
sam_predictor.set_image(frame)
transformed_boxes = sam_predictor.transform.apply_boxes_torch(input_boxes, frame.shape[:2])
masks, _, _ = sam_predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes,
multimask_output=False,
)
track_result[0].pred_track_instances.masks = masks.squeeze(1).cpu().numpy()
instances_list[idx] = track_result
if args.out:
print('Start to visualize the results...')
num_cores = max(1, min(os.cpu_count() - 1, 16))
print('Using {} cores for visualization'.format(num_cores))
if args.show_fps:
with Pool(processes=num_cores) as pool:
frames = pool.starmap(
visualize_frame, [(args, visualizer, frame, track_result.to('cpu'), idx, fps) for idx, (frame, fps, track_result) in enumerate(zip(frames, fps_list, instances_list))]
)
else:
with Pool(processes=num_cores) as pool:
frames = pool.starmap(
visualize_frame, [(args, visualizer, frame, track_result.to('cpu'), idx) for idx, (frame, track_result) in
enumerate(zip(frames, instances_list))]
)
for frame in frames:
if args.out:
video_writer.write(frame[:, :, ::-1])
if video_writer:
video_writer.release()
print('Done')
if __name__ == '__main__':
main()
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