227 lines
9.5 KiB
Python
227 lines
9.5 KiB
Python
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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import argparse
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import csv
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import os
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import platform
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import sys
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from pathlib import Path
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import glob
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import numpy as np
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import pickle
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import torch
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# =============================================================================
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# FILE = Path(__file__).resolve()
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# ROOT = FILE.parents[0] # YOLOv5 root directory
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# if str(ROOT) not in sys.path:
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# sys.path.append(str(ROOT)) # add ROOT to PATH
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# ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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# =============================================================================
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sys.path.append('./ytracking')
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from models.common import DetectMultiBackend
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from utils.dataloaders import LoadImages
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from utils.general import (LOGGER, Profile, check_img_size, check_requirements, colorstr, cv2,
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increment_path, non_max_suppression, scale_boxes, strip_optimizer)
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from utils.torch_utils import select_device, smart_inference_mode
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'''集成跟踪模块，输出跟踪结果文件 .npy'''
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# from ultralytics.engine.results import Boxes # Results
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# from ultralytics.utils import IterableSimpleNamespace, yaml_load
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from tracking.utils.plotting import Annotator, colors
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from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load
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from tracking.trackers import BOTSORT, BYTETracker
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from tracking.utils.showtrack import drawtracks
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# tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml"
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def init_trackers(tracker_yaml=None, bs=1):
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"""
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Initialize trackers for object tracking during prediction.
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"""
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# tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml"
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TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT}
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cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml))
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trackers = []
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for _ in range(bs):
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tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
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trackers.append(tracker)
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return trackers
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@smart_inference_mode()
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def run(
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weights=r"D:/Project/ieemoo-ai/tools/ckpts/best_158734_cls11_noaug10.pt", # model path or triton URL
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source=r"D:/Project/ieemoo-ai/testdata/88.mp4", # file/dir/URL/glob/screen/0(webcam)
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project=r'./runs/detect', # save results to project/name
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name='exp', # save results to project/name
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tracker_yaml="D:/Project/ieemoo-ai/ytracking/tracking/trackers/cfg/botsort.yaml",
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imgsz=(640, 640), # inference size (height, width)
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conf_thres=0.25, # confidence threshold
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iou_thres=0.45, # NMS IOU threshold
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max_det=1000, # maximum detections per image
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device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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bs=1, # batch_size
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save_txt=False, # save results to *.txt
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save_img=True, # do not save images/videos
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classes=None, # filter by class: --class 0, or --class 0 2 3
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agnostic_nms=False, # class-agnostic NMS
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augment=False, # augmented inference
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visualize=False, # visualize features
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update=False, # update all models
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exist_ok=False, # existing project/name ok, do not increment
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line_thickness=3, # bounding box thickness (pixels)
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hide_labels=False, # hide labels
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hide_conf=False, # hide confidencesL
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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vid_stride=1, # video frame-rate stride
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):
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# Load model
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Run inference
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
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##=============================生成文件夹 save_dir，存储检测跟踪图像
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source = str(source)
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save_dir = Path(project) / Path(source).stem
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if save_dir.exists():
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print(Path(source).stem)
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# return
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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else:
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save_dir.mkdir(parents=True, exist_ok=True)
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# Dataloader
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seen, dt = 0, (Profile(), Profile(), Profile())
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
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## ================================================= 生成跟踪器对象
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tracker = init_trackers(tracker_yaml, bs)[0]
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track_boxes = np.empty((0, 9), dtype=np.float32)
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features_dict = {}
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for path, im, im0s, vid_cap, s in dataset:
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# img preprocess
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with dt[0]:
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im = torch.from_numpy(im).to(model.device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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# Inference
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with dt[1]:
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred = model(im, augment=augment, visualize=visualize)
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# NMS
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with dt[2]:
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
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# Process predictions
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for i, det in enumerate(pred): # per image
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seen += 1
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p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
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im0_ant = im0.copy()
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # im.jpg
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s += '%gx%g ' % im.shape[2:] # print string
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annotator = Annotator(im0_ant, line_width=line_thickness, example=str(names)) if save_img else None
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
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# boxes_and_imgs.append((det.cpu().numpy(), im0, frame))
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## ================================================================ writed by WQG
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det_tracking = Boxes(det, im0.shape).cpu().numpy()
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tracks = tracker.update(det_tracking, im0)
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if len(tracks):
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track_boxes = np.concatenate([track_boxes, tracks], axis=0)
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feat_dict = {int(x.idx): x.curr_feat for x in tracker.tracked_stracks if x.is_activated}
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frame_id = track_boxes[0, 7]
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features_dict.update({int(frame_id): feat_dict})
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if annotator is not None:
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for *xyxy, id, conf, cls, fid, bid in reversed(tracks):
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name = ('' if id == -1 else f'id:{int(id)} ') + names[int(cls)]
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label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}')
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if id >= 0 and cls == 0:
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color = colors(int(cls), True)
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elif id >= 0 and cls != 0:
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color = colors(int(id), True)
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else:
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color = colors(19, True) # 19为调色板的最后一个元素
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annotator.box_label(xyxy, label, color=color)
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# Save tracking image
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if annotator is not None:
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save_path_img, ext = os.path.splitext(save_path)
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imgpath = save_path_img + f"_{dataset.frame}.png"
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cv2.imwrite(Path(imgpath), annotator.result())
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# Print time (inference-only)
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LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
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## ======================================================================== written by WQG
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''' track_boxes: Array, [x1, y1, x2, y2, track_id, score, cls, frame_index, box_id] '''
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if save_img:
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filename = os.path.split(save_path_img)[-1]
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'''====== save in './run/detect/' ======'''
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imgshow = drawtracks(track_boxes)
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showpath_1 = save_path_img + "_show.png"
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cv2.imwrite(Path(showpath_1), imgshow)
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'''====== save tracks data ======'''
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tracks_dir = Path('D:/Project/ieemoo-ai/ytracking/tracking/tracking/data/tracks/')
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if not tracks_dir.exists():
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tracks_dir.mkdir(parents=True, exist_ok=True)
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tracks_path = tracks_dir.joinpath(filename + ".npy")
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np.save(tracks_path, track_boxes)
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'''====== save reid features data ======'''
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feats_dir = Path('D:/Project/ieemoo-ai/ytracking/tracking/data/trackfeats/')
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if not feats_dir.exists():
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feats_dir.mkdir(parents=True, exist_ok=True)
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feats_path = feats_dir.joinpath(f'{filename}.pkl')
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with open(feats_path, 'wb') as file:
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pickle.dump(features_dict, file)
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# Print results
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t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
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def main():
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ROOT = Path(Path.cwd())
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check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
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optdict = {'weights': r"D:/Project/ieemoo-ai/tools/ckpts/best_158734_cls11_noaug10.pt",
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'source': r"D:/Project/ieemoo-ai/testdata/88.mp4",
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}
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run(**optdict)
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if __name__ == '__main__':
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main()
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