更新 detacttracking
This commit is contained in:
lee
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detecttracking/track_reid.py
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detecttracking/track_reid.py
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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"""
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Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
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Usage - sources:
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$ python detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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Usage - formats:
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$ python detect.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s_openvino_model # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s_paddle_model # PaddlePaddle
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"""
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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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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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from models.common import DetectMultiBackend
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from detecttracking.utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from detecttracking.utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
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increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
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from detecttracking.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, boxes_add_fid
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from tracking.trackers import BOTSORT, BYTETracker
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from tracking.utils.showtrack import drawtracks
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from hands.hand_inference import hand_pose
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# from contrast.feat_extract.config import config as conf
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from contrast.feat_extract.inference import FeatsInterface
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# ReIDEncoder = FeatsInterface(conf)
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IMG_FORMATS = '.bmp', '.dng', '.jpeg', '.jpg', '.mpo', '.png', '.tif', '.tiff', '.webp', '.pfm' # include image suffixes
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VID_FORMATS = '.asf', '.avi', '.gif', '.m4v', '.mkv', '.mov', '.mp4', '.mpeg', '.mpg', '.ts', '.wmv' # include video suffixes
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# from tracking.trackers.reid.reid_interface import ReIDInterface
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# from tracking.trackers.reid.config import config as ReIDConfig
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# ReIDEncoder = ReIDInterface(ReIDConfig)
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# tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml"
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# def inference_image(image, detections):
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# H, W, _ = np.shape(image)
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# imgs = []
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# batch_patches = []
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# patches = []
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# for d in range(np.size(detections, 0)):
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# tlbr = detections[d, :4].astype(np.int_)
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# tlbr[0] = max(0, tlbr[0])
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# tlbr[1] = max(0, tlbr[1])
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# tlbr[2] = min(W - 1, tlbr[2])
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# tlbr[3] = min(H - 1, tlbr[3])
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# img1 = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]
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# img = img1[:, :, ::-1].copy() # the model expects RGB inputs
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# patch = ReIDEncoder.transform(img)
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# imgs.append(img1)
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# # patch = patch.to(device=self.device).half()
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# if str(ReIDEncoder.device) != "cpu":
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# patch = patch.to(device=ReIDEncoder.device).half()
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# else:
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# patch = patch.to(device=ReIDEncoder.device)
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# patches.append(patch)
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# if (d + 1) % ReIDEncoder.batch_size == 0:
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# patches = torch.stack(patches, dim=0)
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# batch_patches.append(patches)
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# patches = []
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# if len(patches):
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# patches = torch.stack(patches, dim=0)
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# batch_patches.append(patches)
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# features = np.zeros((0, ReIDEncoder.embedding_size))
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# for patches in batch_patches:
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# pred = ReIDEncoder.model(patches)
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# pred[torch.isinf(pred)] = 1.0
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# feat = pred.cpu().data.numpy()
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# features = np.vstack((features, feat))
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# return imgs, features
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def init_trackers(tracker_yaml=None, bs=1, resnetModel=None):
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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, resnetModel=resnetModel)
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trackers.append(tracker)
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return trackers
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'''=============== used in pipeline.py =================='''
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@smart_inference_mode()
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def yolo_resnet_tracker(
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weights=ROOT / 'yolov5s.pt', # model path or triton URL
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source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
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save_dir='',
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is_save_img=True,
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is_save_video=True,
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is_annotate=True,
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tracker_yaml="./detecttracking/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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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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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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data=ROOT / 'data/coco128.yaml', # dataset.yaml path
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video_frames=None,
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resnetModel=None,
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yoloModel=None
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):
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# source = str(source)
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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, data=data, fp16=half)
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model = yoloModel
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ReIDEncoder = FeatsInterface(resnetModel)
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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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# Dataloader
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bs = 1 # batch_size
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
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vid_path, vid_writer = [None] * bs, [None] * bs
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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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tracker = init_trackers(tracker_yaml, bs, resnetModel)[0]
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dt = (Profile(), Profile(), Profile())
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# trackerBoxes = np.empty((0, 9), dtype = np.float32)
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yoloResnetTracker = []
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for path, im, im0s, vid_cap, s in dataset:
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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(project / Path(path).stem, mkdir=True) if visualize else False
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pred = model(im, augment=augment, visualize=False)
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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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if dataset.mode == "video":
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frameId = dataset.frame
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else:
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frameId = dataset.count
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# Process predictions
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for i, det in enumerate(pred): # per image
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im0 = im0s.copy()
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annotator = Annotator(im0.copy(), line_width=line_thickness, example=str(names))
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s += '%gx%g ' % im.shape[2:] # print string
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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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det = det.cpu().numpy()
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## ================================================================ writed by WQG
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'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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0 1 2 3 4 5 6 7 8
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这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变
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'''
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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) > 0:
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tracks[:, 7] = frameId
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# trackerBoxes = np.concatenate([trackerBoxes, tracks], axis=0)
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'''================== 1. 存储 dets/subimgs/features Dict ============='''
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imgs, features = ReIDEncoder.inference(im0, tracks)
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imgdict, featdict = {}, {}
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for ii, bid in enumerate(tracks[:, 8]):
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featdict.update(
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{f"{int(frameId)}_{int(bid)}": features[ii, :]}) # [f"feat_{int(bid)}"] = features[i, :]
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imgdict.update({f"{int(frameId)}_{int(bid)}": imgs[ii]})
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frameDict = {"path": path,
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"fid": int(frameId),
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"bboxes": det,
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"tboxes": tracks,
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"imgs": imgdict,
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"feats": featdict}
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yoloResnetTracker.append(frameDict)
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# imgs, features = inference_image(im0, tracks)
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# TrackerFeats = np.concatenate([TrackerFeats, features], axis=0)
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'''================== 2. 提取手势位置 ==================='''
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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 results (image and video) ======'''
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# save_path = str(save_dir / Path(path).name) # 带有后缀名
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if is_annotate:
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im0 = annotator.result()
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if is_save_img:
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save_path_img = str(save_dir / Path(path).stem)
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if dataset.mode == 'image':
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imgpath = save_path_img + ".png"
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else:
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imgpath = save_path_img + f"_{frameId}.png"
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cv2.imwrite(Path(imgpath), im0)
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if video_frames is not None:
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video_frames.update({frameId: im0})
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# if dataset.mode == 'video' and is_save_video:
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if is_save_video:
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if dataset.mode == 'video':
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vdieo_path = str(save_dir / Path(path).stem) + '.mp4' # 带有后缀名
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else:
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videoname = str(Path(path).stem).split('_')[0] + '.mp4'
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vdieo_path = str(save_dir / videoname)
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if vid_path[i] != vdieo_path: # new video
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vid_path[i] = vdieo_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 25, im0.shape[1], im0.shape[0]
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vdieo_path = str(Path(vdieo_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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vid_writer[i] = cv2.VideoWriter(vdieo_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer[i].write(im0)
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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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return yoloResnetTracker
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@smart_inference_mode()
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def run(
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weights=ROOT / 'yolov5s.pt', # model path or triton URL
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source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
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project=ROOT / 'runs/detect', # save results to project/name
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name='exp', # save results to project/name
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tracker_yaml="./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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view_img=False, # show results
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save_txt=False, # save results to *.txt
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save_csv=False, # save results in CSV format
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save_conf=False, # save confidences in --save-txt labels
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save_crop=False, # save cropped prediction boxes
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nosave=False, # 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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data=ROOT / 'data/coco128.yaml', # dataset.yaml path
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):
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'''
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source: 视频文件或图像列表
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'''
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source = str(source)
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# filename = os.path.split(source)[-1]
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save_img = not nosave and not source.endswith('.txt') # save inference images
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
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screenshot = source.lower().startswith('screen')
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if is_url and is_file:
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source = check_file(source) # download
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# spth = source.split('\\')[-2] + "_" + Path(source).stem
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save_dir = Path(project) / Path(source.split('\\')[-2] + "_" + str(Path(source).stem))
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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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# Load model
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, 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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# Dataloader
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bs = 1 # batch_size
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
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vid_path, vid_writer = [None] * bs, [None] * bs
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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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seen, dt = 0, (Profile(), Profile(), Profile())
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tracker = init_trackers(tracker_yaml, bs)[0]
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handpose = hand_pose()
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handlocals_dict = {}
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boxes_and_imgs = []
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BoxesFeats = []
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track_boxes = np.empty((0, 9), dtype=np.float32)
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det_boxes = np.empty((0, 9), dtype=np.float32)
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DetBoxes = np.empty((0, 6), dtype=np.float32)
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TrackerBoxes = np.empty((0, 9), dtype=np.float32)
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TrackerFeats = np.empty((0, 256), dtype=np.float32)
|
||||
|
||||
features_dict = {}
|
||||
TracksDict = {}
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
if save_img and 'imgshow' not in locals().keys():
|
||||
imgshow = im0s.copy()
|
||||
|
||||
## ============================= tracking 功能只处理视频，writed by WQG
|
||||
# if dataset.mode == 'image':
|
||||
# continue
|
||||
|
||||
with dt[0]:
|
||||
im = torch.from_numpy(im).to(model.device)
|
||||
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=visualize)
|
||||
|
||||
# NMS
|
||||
with dt[2]:
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
||||
|
||||
# Process predictions
|
||||
for i, det in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if webcam: # batch_size >= 1
|
||||
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||
s += f'{i}: '
|
||||
else:
|
||||
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
|
||||
|
||||
s += '%gx%g ' % im.shape[2:] # print string
|
||||
|
||||
# im0_ant = im0.copy()
|
||||
annotator = Annotator(im0.copy(), line_width=line_thickness, example=str(names))
|
||||
|
||||
nd = len(det)
|
||||
if nd:
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
|
||||
|
||||
# det = det.cpu().numpy()
|
||||
## ============================================================ 前后帧相同 boxes 的特征赋值
|
||||
# def static_estimate(box1, box2, TH1=8, TH2=12):
|
||||
# dij_abs = max(np.abs(box1 - box2))
|
||||
# dij_euc = max([np.linalg.norm((box1[:2] - box2[:2])),
|
||||
# np.linalg.norm((box1[2:4] - box2[2:4]))
|
||||
# ])
|
||||
# if dij_abs < TH1 and dij_euc < TH2:
|
||||
# return True
|
||||
# else:
|
||||
# return False
|
||||
|
||||
# nw = 3 # 向前递推检查的窗口大小
|
||||
# nf = len(BoxesFeats) # 已经检测+特征提取的帧数
|
||||
# feat_curr = [None] * nd # nd: 当前帧检测出的boxes数
|
||||
# for ii in range(nd):
|
||||
# box = det[ii, :4]
|
||||
|
||||
# kk=1
|
||||
# feat = None
|
||||
# while kk <= nw and nf>=kk:
|
||||
# ki = -1 * kk
|
||||
# boxes_ = BoxesFeats[ki][0]
|
||||
# feats_ = BoxesFeats[ki][1]
|
||||
|
||||
# flag = [jj for jj in range(len(boxes_)) if static_estimate(box, boxes_[jj, :4])]
|
||||
# if len(flag) == 1:
|
||||
# feat = feats_[flag[0]]
|
||||
# break
|
||||
# kk += 1
|
||||
# if feat is not None:
|
||||
# feat_curr[ii] = feat
|
||||
|
||||
## ================================================================ writed by WQG
|
||||
'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||||
0 1 2 3 4 5 6 7 8
|
||||
这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变
|
||||
'''
|
||||
|
||||
det_tracking = Boxes(det, im0.shape).cpu().numpy()
|
||||
tracks = tracker.update(det_tracking, im0)
|
||||
if len(tracks) == 0:
|
||||
continue
|
||||
|
||||
if dataset.mode == "video":
|
||||
frameId = dataset.frame
|
||||
else:
|
||||
frameId = dataset.count
|
||||
tracks[:, 7] = frameId
|
||||
|
||||
tracks[:, 7] = frameId
|
||||
|
||||
'''================== 1. 存储 dets/subimgs/features Dict ============='''
|
||||
# imgs, features = inference_image(im0, tracks)
|
||||
imgs, features = ReIDEncoder.inference(im0, tracks)
|
||||
|
||||
TrackerFeats = np.concatenate([TrackerFeats, features], axis=0)
|
||||
|
||||
imgdict = {}
|
||||
boxdict = {}
|
||||
featdict = {}
|
||||
for ii, bid in enumerate(tracks[:, 8]):
|
||||
imgdict.update({int(bid): imgs[ii]}) # [f"img_{int(bid)}"] = imgs[i]
|
||||
boxdict.update({int(bid): tracks[ii, :]}) # [f"box_{int(bid)}"] = tracks[i, :]
|
||||
featdict.update({int(bid): features[ii, :]}) # [f"feat_{int(bid)}"] = features[i, :]
|
||||
TracksDict[f"frame_{int(frameId)}"] = {"imgs": imgdict, "boxes": boxdict, "feats": featdict}
|
||||
|
||||
track_boxes = np.concatenate([track_boxes, tracks], axis=0)
|
||||
|
||||
'''================== 2. 提取手势位置 ==================='''
|
||||
# idx_0 = tracks[:, 6].astype(np.int_) == 0
|
||||
# hn = 0
|
||||
# for j, index in enumerate(idx_0):
|
||||
# if index:
|
||||
# track = tracks[j, :]
|
||||
# hand_local, imgshow = handpose.get_hand_local(track, im0)
|
||||
# handlocals_dict.update({int(track[7]): {int(track[8]): hand_local}})
|
||||
|
||||
# # '''yoloV5和手势检测的召回率并不一直，用hand_local代替tracks中手部的(x1, y1, x2, y2)，会使得两种坐标方式混淆'''
|
||||
# # if hand_local: tracks[j, :4] = hand_local
|
||||
|
||||
# hn += 1
|
||||
# cv2.imwrite(f"D:\DeepLearning\yolov5\hands\images\{Path(source).stem}_{int(track[7])}_{hn}.png", imgshow)
|
||||
|
||||
for *xyxy, id, conf, cls, fid, bid in reversed(tracks):
|
||||
name = ('' if id == -1 else f'id:{int(id)} ') + names[int(cls)]
|
||||
label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}')
|
||||
|
||||
if id >= 0 and cls == 0:
|
||||
color = colors(int(cls), True)
|
||||
elif id >= 0 and cls != 0:
|
||||
color = colors(int(id), True)
|
||||
else:
|
||||
color = colors(19, True) # 19为调色板的最后一个元素
|
||||
|
||||
annotator.box_label(xyxy, label, color=color)
|
||||
|
||||
# Save results (image and video with tracking)
|
||||
im0 = annotator.result()
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
if save_img:
|
||||
save_path_img, ext = os.path.splitext(save_path)
|
||||
if dataset.mode == 'image':
|
||||
imgpath = save_path_img + ".png"
|
||||
else:
|
||||
imgpath = save_path_img + f"_{frameId}.png"
|
||||
cv2.imwrite(Path(imgpath), im0)
|
||||
|
||||
if dataset.mode == 'video':
|
||||
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
|
||||
|
||||
if track_boxes.size == 0:
|
||||
return
|
||||
|
||||
## ======================================================================== written by WQG
|
||||
## track_boxes: Array, [x1, y1, x2, y2, track_id, score, cls, frame_index, box_id]
|
||||
TracksDict.update({"TrackBoxes": track_boxes})
|
||||
|
||||
'''上面保存了检测结果是视频和图像，以下还保存五种类型的数据'''
|
||||
filename = os.path.split(save_path_img)[-1]
|
||||
|
||||
'''======================== 1. save in './run/detect/' ===================='''
|
||||
if source.find("front") >= 0 or Path(source).stem.split('_')[0] == '1':
|
||||
carttemp = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
|
||||
else:
|
||||
carttemp = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
|
||||
imgshow = drawtracks(track_boxes, carttemp)
|
||||
showpath_1 = save_path_img + "_show.png"
|
||||
cv2.imwrite(Path(showpath_1), imgshow)
|
||||
|
||||
'''======================== 2. save dets/subimgs/features Dict =================='''
|
||||
trackdicts_dir = Path('./tracking/data/trackdicts/')
|
||||
if not trackdicts_dir.exists():
|
||||
trackdicts_dir.mkdir(parents=True, exist_ok=True)
|
||||
trackdicts_dir = trackdicts_dir.joinpath(f'{filename}.pkl')
|
||||
with open(trackdicts_dir, 'wb') as file:
|
||||
pickle.dump(TracksDict, file)
|
||||
|
||||
# np.save(f'{filename}.npy', DetBoxes)
|
||||
|
||||
'''======================== 3. save hand_local data =================='''
|
||||
# handlocal_dir = Path('./tracking/data/handlocal/')
|
||||
# if not handlocal_dir.exists():
|
||||
# handlocal_dir.mkdir(parents=True, exist_ok=True)
|
||||
# handlocal_path = handlocal_dir.joinpath(f'{filename}.pkl')
|
||||
# with open(handlocal_path, 'wb') as file:
|
||||
# pickle.dump(handlocals_dict, file)
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
|
||||
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
if update:
|
||||
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
|
||||
|
||||
|
||||
def parse_opt():
|
||||
modelpath = ROOT / 'ckpts/best_cls10_0906.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt, best_yolov5m_250000'
|
||||
|
||||
'''datapath为视频文件目录或视频文件'''
|
||||
datapath = r"D:/datasets/ym/videos/标记视频/" # ROOT/'data/videos', ROOT/'data/images' images
|
||||
# datapath = r"D:\datasets\ym\highvalue\videos"
|
||||
# datapath = r"D:/dcheng/videos/"
|
||||
# modelpath = ROOT / 'ckpts/yolov5s.pt'
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', nargs='+', type=str, default=modelpath,
|
||||
help='model path or triton URL') # 'yolov5s.pt', best_15000_0908.pt
|
||||
parser.add_argument('--source', type=str, default=datapath,
|
||||
help='file/dir/URL/glob/screen/0(webcam)') # images, videos
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
|
||||
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--view-img', action='store_true', help='show results')
|
||||
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||||
parser.add_argument('--save-csv', action='store_true', help='save results in CSV format')
|
||||
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
|
||||
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
|
||||
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
|
||||
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
|
||||
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||||
parser.add_argument('--visualize', action='store_true', help='visualize features')
|
||||
parser.add_argument('--update', action='store_true', help='update all models')
|
||||
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save results to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
|
||||
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
|
||||
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
|
||||
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||||
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
||||
parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def find_video_imgs(root_dir):
|
||||
all_files = []
|
||||
extensions = ['.mp4']
|
||||
for dirpath, dirnames, filenames in os.walk(root_dir):
|
||||
for filename in filenames:
|
||||
file, ext = os.path.splitext(filename)
|
||||
if ext in IMG_FORMATS + VID_FORMATS:
|
||||
all_files.append(os.path.join(dirpath, filename))
|
||||
return all_files
|
||||
|
||||
|
||||
def main():
|
||||
'''
|
||||
run(): 单张图像或单个视频文件的推理，不支持图像序列，
|
||||
'''
|
||||
|
||||
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||||
opt = parse_opt()
|
||||
optdict = vars(opt)
|
||||
|
||||
# p = r"D:\datasets\ym\永辉测试数据_比对"
|
||||
# p = r"D:\datasets\ym\广告板遮挡测试\8"
|
||||
# p = r"D:\datasets\ym\videos\标记视频"
|
||||
# p = r"D:\datasets\ym\实验室测试"
|
||||
# p = r"D:\datasets\ym\永辉双摄视频\新建文件夹"
|
||||
# p = r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-112522_"
|
||||
# p = r"D:\datasets\ym\联华中环"
|
||||
# p = r"D:\exhibition\images\153112511_0_seek_105.mp4"
|
||||
# p = r"D:\exhibition\images\image"
|
||||
|
||||
p = r"\\192.168.1.28\share\数据\原始数据\小物品数据\视频\82654976401_20241213-143457_front_addGood_5478c9a53bbe_40_17700000001.mp4"
|
||||
optdict["project"] = r"D:\小物品入侵检测\result"
|
||||
|
||||
# optdict["project"] = r"D:\exhibition\result"
|
||||
if os.path.isdir(p):
|
||||
files = find_video_imgs(p)
|
||||
k = 0
|
||||
for file in files:
|
||||
optdict["source"] = file
|
||||
run(**optdict)
|
||||
|
||||
k += 1
|
||||
if k == 1:
|
||||
break
|
||||
elif os.path.isfile(p):
|
||||
optdict["source"] = p
|
||||
run(**optdict)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user