630 lines
27 KiB
Python
630 lines
27 KiB
Python
# 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 utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from 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 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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# =============================================================================
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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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# =============================================================================
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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=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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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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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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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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features_dict = {}
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for path, im, im0s, vid_cap, s in dataset:
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if save_img and 'imgshow' not in locals().keys():
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imgshow = im0s.copy()
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## ============================= tracking 功能只处理视频，writed by WQG
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if dataset.mode == 'image':
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continue
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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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# Second-stage classifier (optional)
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# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
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# Define the path for the CSV file
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# csv_path = save_dir / 'predictions.csv'
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# Create or append to the CSV file
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# def write_to_csv(image_name, prediction, confidence):
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# data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence}
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# with open(csv_path, mode='a', newline='') as f:
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# writer = csv.DictWriter(f, fieldnames=data.keys())
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# if not csv_path.is_file():
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# writer.writeheader()
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# writer.writerow(data)
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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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if webcam: # batch_size >= 1
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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s += f'{i}: '
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else:
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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))
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nd = len(det)
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if nd:
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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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det = np.concatenate([det[:, :4], np.arange(nd).reshape(-1, 1), det[:, 4:]], axis=-1)
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'''FeatFlag为相对于上一帧boxes，当前boxes是否为静止的标志。'''
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# def static_estimate(box1, box2, TH1=8, TH2=12):
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# dij_abs = max(np.abs(box1 - box2))
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# dij_euc = max([np.linalg.norm((box1[:2] - box2[:2])),
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# np.linalg.norm((box1[2:4] - box2[2:4]))
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# ])
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# if dij_abs < TH1 and dij_euc < TH2:
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# return True
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# else:
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# return False
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# FeatFlag = [-1] * nd
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# if len(boxes_and_imgs):
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# detj = boxes_and_imgs[-1][0]
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# frmj = boxes_and_imgs[-1][-1]
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# for ii in range(nd):
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# ## flag 中保存的是box索引
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# condt1 = frame-frmj==1
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# flag = [idx for jj, idx in enumerate(detj[:, 4]) if condt1 and static_estimate(det[ii, :4], detj[jj, :4])]
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# if len(flag) == 1:
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# FeatFlag[ii] = flag[0]
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boxes_and_imgs.append((det, im0, frame))
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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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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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# detbox = [tlwh2tlbr(x._tlwh).tolist() + [x.track_id, x.score, x.cls, x.frame_id, x.idx]
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# for x in tracker.tracked_stracks if x.is_activated]
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if len(tracks):
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'''
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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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'''
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tracks[:, 7] = dataset.frame
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'''================== 1. 提取手势位置 ==================='''
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# idx_0 = tracks[:, 6].astype(np.int_) == 0
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# hn = 0
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# for j, index in enumerate(idx_0):
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# if index:
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# track = tracks[j, :]
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# hand_local, imgshow = handpose.get_hand_local(track, im0)
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# handlocals_dict.update({int(track[7]): {int(track[8]): hand_local}})
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# # '''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'''
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# # if hand_local: tracks[j, :4] = hand_local
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# hn += 1
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# cv2.imwrite(f"D:\DeepLearning\yolov5\hands\images\{Path(source).stem}_{int(track[7])}_{hn}.png", imgshow)
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'''================== 2. 存储轨迹信息 ==================='''
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track_boxes = np.concatenate([track_boxes, tracks], axis=0)
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# det_boxes = np.concatenate([det_boxes, detbox], axis=0)
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'''================== 3. 存储轨迹 REID 特征 ============='''
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def crop_img(track, image):
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tlbr = track.tlwh_to_tlbr(track._tlwh).astype(np.int_)
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H, W = image.shape[:2]
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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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img = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]
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# cv2.imwrite(f"./runs/imgs/{int(track.idx)}.png", img)
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return img
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feat_dict_1 = {f'{int(x.idx)}_img': crop_img(x, im0) for x in tracker.tracked_stracks if x.is_activated}
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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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feat_dict.update(feat_dict_1)
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features_dict.update({int(dataset.frame): feat_dict})
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# det_anno = tracks.copy()
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# else:
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# idmark = -1 * np.ones([det.shape[0], 1])
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# det_anno = np.concatenate([det[:,:4], idmark, det[:, 4:]], axis=1)
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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 with tracking)
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im0 = annotator.result()
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save_path_img, ext = os.path.splitext(save_path)
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if save_img:
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if dataset.mode == 'image':
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imgpath = save_path_img + f"_{dataset}.png"
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else:
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imgpath = save_path_img + f"_{dataset.frame}.png"
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cv2.imwrite(Path(imgpath), im0)
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if vid_path[i] != save_path: # new video
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vid_path[i] = save_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 = 30, im0.shape[1], im0.shape[0]
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save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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vid_writer[i] = cv2.VideoWriter(save_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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## ======================================================================== 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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'''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'''
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filename = os.path.split(save_path_img)[-1]
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# file, ext = os.path.splitext(filename)
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# =============================================================================
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# fileElements = filename.split('_')
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# if len(fileElements) == 6 and len(fileElements[3])==1:
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# barcode = fileElements[1]
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# camera = fileElements[3]
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# elif len(fileElements) == 7 and len(fileElements[3])==1:
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# barcode = fileElements[2]
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# camera = fileElements[4]
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# else:
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# barcode = ''
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# camera = ''
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# =============================================================================
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'''======================== 1. save in './run/detect/' ===================='''
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if source.find("front") >= 0:
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carttemp = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
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else:
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carttemp = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
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imgshow = drawtracks(track_boxes, carttemp)
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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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'''======================== 2. save boxes and raw images =================='''
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# boxes_imgs_dir = Path('./tracking/data/boxes_imgs/')
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# if not boxes_imgs_dir.exists():
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# boxes_imgs_dir.mkdir(parents=True, exist_ok=True)
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# boxes_imgs_path = boxes_imgs_dir.joinpath(f'{filename}.pkl')
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# with open(boxes_imgs_path, 'wb') as file:
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# pickle.dump(boxes_and_imgs, file)
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'''======================== 3. save tracks data ==========================='''
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tracks_dir = Path('./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 + "_track.npy")
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np.save(tracks_path, track_boxes)
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detect_path = tracks_dir.joinpath(filename + "_detect.npy")
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np.save(detect_path, det_boxes)
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'''======================== 4. save reid features data ===================='''
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feats_dir = Path('./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:
|
||
pickle.dump(features_dict, file)
|
||
|
||
'''======================== 5. 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_yolov5m_250000.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt'
|
||
|
||
'''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 main_loop_folders(opt):
|
||
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||
|
||
# path1 = r"D:\datasets\ym\videos\标记视频"
|
||
|
||
path2 = r"D:\datasets\ym\永辉双摄视频\加购_前摄\videos_front"
|
||
# path3 = r"D:\datasets\ym\永辉双摄视频\加购_后摄\videos_back"
|
||
# path4 = r"D:\datasets\ym\永辉双摄视频\退购_前摄\videos_front"
|
||
# path5 = r"D:\datasets\ym\永辉双摄视频\退购_后摄\videos_back"
|
||
|
||
path6 = r"D:\datasets\ym\测试数据20240328\front"
|
||
path7 = r"D:\datasets\ym\测试数据20240328\back"
|
||
|
||
'''列表paths内的元素为视频文件夹，该文件夹下元素为视频文件'''
|
||
paths = [path2, path7] # [path1, path2, path3, path4, path5]
|
||
|
||
optdict = vars(opt)
|
||
k1, k2 = 0, 0
|
||
for p in paths:
|
||
files = []
|
||
if os.path.isdir(p):
|
||
files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))
|
||
for file in files:
|
||
file = r"D:\datasets\ym\测试数据20240328\front\112954521-7dd5ddad-922a-427b-b59e-a593e95e6ff4_front.mp4"
|
||
|
||
optdict["source"] = file
|
||
run(**optdict)
|
||
|
||
k2 += 1
|
||
if k2 == 1:
|
||
break
|
||
|
||
elif os.path.isfile(p):
|
||
run(**optdict)
|
||
|
||
k1 += 1
|
||
if k1 == 1:
|
||
break
|
||
|
||
def find_files_in_nested_dirs(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 extensions:
|
||
all_files.append(os.path.join(dirpath, filename))
|
||
return all_files
|
||
|
||
print('=======')
|
||
|
||
def main(opt):
|
||
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||
|
||
p = r"D:\datasets\ym\永辉测试数据_202404\20240402"
|
||
optdict = vars(opt)
|
||
files = []
|
||
k = 0
|
||
|
||
all_files = find_files_in_nested_dirs(p)
|
||
|
||
if os.path.isdir(p):
|
||
files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))
|
||
for file in files:
|
||
optdict["source"] = file
|
||
run(**optdict)
|
||
|
||
k += 1
|
||
if k == 2:
|
||
break
|
||
elif os.path.isfile(p):
|
||
run(**vars(opt))
|
||
|
||
def main_loop(opt):
|
||
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||
|
||
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"
|
||
|
||
k = 0
|
||
if os.path.isdir(p):
|
||
files = find_files_in_nested_dirs(p)
|
||
|
||
files = [r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_back_174037372.mp4",
|
||
r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_front_174037379.mp4"
|
||
]
|
||
|
||
files = [r"D:\datasets\ym\广告板遮挡测试\8\2500441577966_20240508-175946_front_addGood_70f75407b7ae_155_17788571404.mp4"]
|
||
for file in files:
|
||
optdict["source"] = file
|
||
run(**optdict)
|
||
|
||
k += 1
|
||
if k == 2:
|
||
break
|
||
elif os.path.isfile(p):
|
||
optdict["source"] = p
|
||
run(**vars(opt))
|
||
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
opt = parse_opt()
|
||
|
||
# main_loop_folders(opt)
|
||
# main(opt)
|
||
main_loop(opt)
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|