退购1.1定位算法
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jiajie555
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ultralytics/yolo/engine/predictor.py
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662
ultralytics/yolo/engine/predictor.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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"""
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Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
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Usage - sources:
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$ yolo mode=predict model=yolov8n.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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$ yolo mode=predict model=yolov8n.pt # PyTorch
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yolov8n.torchscript # TorchScript
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yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True
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yolov8n_openvino_model # OpenVINO
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yolov8n.engine # TensorRT
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yolov8n.mlmodel # CoreML (macOS-only)
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yolov8n_saved_model # TensorFlow SavedModel
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yolov8n.pb # TensorFlow GraphDef
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yolov8n.tflite # TensorFlow Lite
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yolov8n_edgetpu.tflite # TensorFlow Edge TPU
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yolov8n_paddle_model # PaddlePaddle
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"""
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import math
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import os
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import platform
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import time
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from math import sqrt
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from pathlib import Path
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from random import random
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import cv2
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import numpy as np
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import torch
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from PIL import Image
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data import load_inference_source
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from ultralytics.yolo.data.augment import LetterBox, classify_transforms
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops
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from ultralytics.yolo.utils.checks import check_imgsz, check_imshow
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from ultralytics.yolo.utils.files import increment_path
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from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
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from ultralytics.yolo.engine.ids import check_tings
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STREAM_WARNING = """
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WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,
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causing potential out-of-memory errors for large sources or long-running streams/videos.
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Usage:
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results = model(source=..., stream=True) # generator of Results objects
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for r in results:
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boxes = r.boxes # Boxes object for bbox outputs
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masks = r.masks # Masks object for segment masks outputs
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probs = r.probs # Class probabilities for classification outputs
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"""
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class BasePredictor:
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"""
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BasePredictor
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A base class for creating predictors.
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Attributes:
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args (SimpleNamespace): Configuration for the predictor.
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save_dir (Path): Directory to save results.
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done_setup (bool): Whether the predictor has finished setup.
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model (nn.Module): Model used for prediction.
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data (dict): Data configuration.
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device (torch.device): Device used for prediction.
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dataset (Dataset): Dataset used for prediction.
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vid_path (str): Path to video file.
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vid_writer (cv2.VideoWriter): Video writer for saving video output.
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annotator (Annotator): Annotator used for prediction.
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data_path (str): Path to data.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""
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Initializes the BasePredictor class.
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Args:
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cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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# self.real_MovBox = None
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self.args = get_cfg(cfg, overrides)
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project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
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name = self.args.name or f'{self.args.mode}'
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self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
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if self.args.conf is None:
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self.args.conf = 0.25 # default conf=0.25
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self.done_warmup = False
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if self.args.show:
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self.args.show = check_imshow(warn=True)
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# Usable if setup is done
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self.model = None
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self.data = self.args.data # data_dict
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self.imgsz = None
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self.device = None
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self.dataset = None
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self.vid_path, self.vid_writer = None, None
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self.plotted_img = None
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self.data_path = None
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self.source_type = None
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self.batch = None
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self.callbacks = _callbacks or callbacks.get_default_callbacks()
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callbacks.add_integration_callbacks(self)
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def preprocess(self, im):
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"""Prepares input image before inference.
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Args:
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im (torch.Tensor | List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
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"""
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if not isinstance(im, torch.Tensor):
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im = np.stack(self.pre_transform(im)) #size(640,XX)
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im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
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im = np.ascontiguousarray(im) # contiguous
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im = torch.from_numpy(im)
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# NOTE: assuming im with (b, 3, h, w) if it's a tensor
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img = im.to(self.device)
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img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
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img /= 255 # 0 - 255 to 0.0 - 1.0
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return img
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def pre_transform(self, im):
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"""Pre-tranform input image before inference.
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Args:
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im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
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Return: A list of transformed imgs.
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"""
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same_shapes = all(x.shape == im[0].shape for x in im)
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auto = same_shapes and self.model.pt
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return [LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im]
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def write_results(self, idx, results, batch):
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"""Write inference results to a file or directory."""
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p, im, _ = batch
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log_string = ''
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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self.seen += 1
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if self.source_type.webcam or self.source_type.from_img: # batch_size >= 1
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log_string += f'{idx}: '
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frame = self.dataset.count
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else:
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frame = getattr(self.dataset, 'frame', 0)
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self.data_path = p
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self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
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log_string += '%gx%g ' % im.shape[2:] # print string
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result = results[idx]
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# print('result', result)
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log_string += result.verbose()
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if self.args.save or self.args.show: # Add bbox to image
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plot_args = dict(line_width=self.args.line_width,
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boxes=self.args.boxes,
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conf=self.args.show_conf,
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labels=self.args.show_labels)
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if not self.args.retina_masks:
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plot_args['im_gpu'] = im[idx]
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self.plotted_img = result.plot(**plot_args)
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# Write
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if self.args.save_txt:
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result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf)
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if self.args.save_crop:
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result.save_crop(save_dir=self.save_dir / 'crops', file_name=self.data_path.stem)
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return log_string
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def postprocess(self, preds, img, orig_img):
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"""Post-processes predictions for an image and returns them."""
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return preds
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def boxesMov_output(self, path, img_MovBoxes):
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return img_MovBoxes
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def __call__(self, source=None, model=None, stream=False):
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"""Performs inference on an image or stream."""
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self.stream = stream
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if stream:
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return self.stream_inference(source, model)
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else:
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# print('*****************',list(self.stream_inference(source, model)))
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return list(self.stream_inference(source, model)) # merge list of Result into one
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def predict_cli(self, source=None, model=None):
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"""Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode."""
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gen = self.stream_inference(source, model)
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for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
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pass
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def setup_source(self, source):
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"""Sets up source and inference mode."""
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self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size
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self.transforms = getattr(self.model.model, 'transforms', classify_transforms(
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self.imgsz[0])) if self.args.task == 'classify' else None
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self.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride)
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self.dataset0 = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride)
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self.source_type = self.dataset.source_type
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if not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or # streams
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len(self.dataset) > 1000 or # images
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any(getattr(self.dataset, 'video_flag', [False]))): # videos
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LOGGER.warning(STREAM_WARNING)
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self.vid_path, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs
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@smart_inference_mode()
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def stream_inference(self, source=None, model=None):
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"""Streams real-time inference on camera feed and saves results to file."""
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if self.args.verbose:
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LOGGER.info('')
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# Setup model
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if not self.model:
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self.setup_model(model)
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# Setup source every time predict is called
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self.setup_source(source if source is not None else self.args.source)
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# Check if save_dir/ label file exists
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if self.args.save or self.args.save_txt:
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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# Warmup model
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if not self.done_warmup:
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self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))
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self.done_warmup = True
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self.seen, self.windows, self.batch, self.batch0, profilers = 0, [], None, None, (ops.Profile(), ops.Profile(), ops.Profile())
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self.run_callbacks('on_predict_start')
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allMovBoxes = []
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boxesAll = []
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id = 0
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img_MovBoxes = []
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idx = []
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id0 = 0
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fgbg = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=16, detectShadows=True)
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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for batch0 in self.dataset0:
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path, im0, vid_cap, s = batch0
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id0 += 1
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# video_len = int(str(s).split(')')[0].split('/')[-1])
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# print('video_len', id0, video_len)
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img0 = np.array(im0).squeeze()
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frame = cv2.resize(img0, (512, 640), interpolation=cv2.INTER_CUBIC)
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frame = cv2.medianBlur(frame, ksize=3)
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# 计算前景掩码
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fgmask = fgbg.apply(frame)
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draw1 = cv2.threshold(fgmask, 230, 255, cv2.THRESH_BINARY)[1] # 二值化
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draw1 = cv2.erode(draw1, kernel, iterations=1)
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draw1 = cv2.dilate(draw1, kernel, iterations=1)
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dics = {}
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contours_m, hierarchy_m = cv2.findContours(draw1.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for contour in contours_m:
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dics[len(contour)] = contour
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if len(dics.keys()) > 0:
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cc = sorted(dics.keys())
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iouArea = cv2.contourArea(dics[cc[-1]])
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print("***********id0", id0, iouArea)
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if iouArea>3000:
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idx.append(id0)
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# if len(idx) != 0 and abs(id0 - idx[-1]) > 30:
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# break
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# print('*******id0', id0, idx)
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if len(idx) != 0:
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idx.sort()
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print('idx', idx)
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for batch in self.dataset:
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t0 = time.time()
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id += 1
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if idx[0] <= id <= idx[-1]:
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self.run_callbacks('on_predict_batch_start')
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self.batch = batch
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path, im0s, vid_cap, s = batch
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visualize = increment_path(self.save_dir / Path(path[0]).stem,
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mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False
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# Preprocess
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with profilers[0]:
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im = self.preprocess(im0s)
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# Inference
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with profilers[1]:
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preds = self.model(im, augment=self.args.augment, visualize=visualize)
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# Postprocess
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with profilers[2]:
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self.results = self.postprocess(preds, im, im0s)
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self.run_callbacks('on_predict_postprocess_end')
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# Visualize, save, write results
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n = len(im0s)
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for i in range(n):
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tt0 = time.time()
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im1 = im0s[i].copy()
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### create class MovingBox
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boxesMov = MovingBox(id, self.results[i].boxes.xywh, self.results[i].boxes.xyxy, self.results[i].boxes.conf, path)
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if id == idx[0]:
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boxesAll= boxesMov._initiate_box()
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else:
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### allMovBoxes->是运动框的坐标
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boxesAll, allMovBoxes = boxesMov.update(boxesAll, im1, self.save_dir)
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if len(allMovBoxes) != 0:
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mov_id = 0
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for box in allMovBoxes:
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box_xyxy = xywh2xyxy(box[0])
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cropMov = im0s[i][int(box_xyxy[1]):int(box_xyxy[3]), int(box_xyxy[0]):int(box_xyxy[2]), ::-1]
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# crop_save = os.path.abspath(im0s[i])
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# Image.fromarray(cropMov).show()
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### img_MovBoxes->运动商品图片
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img_MovBoxes.append([id, cropMov])
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tt1 = time.time() - tt0
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# print('运动框判断所需时间{}s'.format(tt1))
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crop_save = self.save_dir / 'movboxes/'
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ori_img = self.save_dir / 'oriImage/'
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if not os.path.exists(crop_save):
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# crop_save.parent.mkdir(parents=True, exist_ok=True)
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crop_save.mkdir(parents=True, exist_ok=True)
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if not os.path.exists(ori_img):
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ori_img.mkdir()
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f = str(crop_save) + '\\frame' + str(id) + '_' + str(mov_id) + '_' + '.jpg'
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g = str(ori_img) + '\\frame' + str(id) + '_' + '.jpg'
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# Image.fromarray(cropMov[::-1]).save(f, quality=95, subsampling=0)
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Image.fromarray(cropMov).save(f, quality=95, subsampling=0)
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# Image.fromarray(im1[:,:,::-1]).save(g, quality=95, subsampling=0)
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mov_id += 1
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self.results[i].speed = {
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'preprocess': profilers[0].dt * 1E3 / n,
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'inference': profilers[1].dt * 1E3 / n,
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'postprocess': profilers[2].dt * 1E3 / n}
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if self.source_type.tensor: # skip write, show and plot operations if input is raw tensor
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continue
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p, im0 = path[i], im0s[i].copy()
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p = Path(p)
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if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
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s += self.write_results(i, self.results, (p, im, im0))
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if self.args.show and self.plotted_img is not None:
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self.show(p)
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if self.args.save and self.plotted_img is not None:
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self.save_preds(vid_cap, i, str(self.save_dir / p.name))
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elif id < idx[0]:
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# self.results = []
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continue
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else:
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break
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else:
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for batch in self.dataset:
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t0 = time.time()
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id += 1
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self.run_callbacks('on_predict_batch_start')
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self.batch = batch
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path, im0s, vid_cap, s = batch
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visualize = increment_path(self.save_dir / Path(path[0]).stem,
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mkdir=True) if self.args.visualize and (
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not self.source_type.tensor) else False
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# Preprocess
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with profilers[0]:
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im = self.preprocess(im0s)
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# Inference
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with profilers[1]:
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preds = self.model(im, augment=self.args.augment, visualize=visualize)
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# Postprocess
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with profilers[2]:
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self.results = self.postprocess(preds, im, im0s)
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self.run_callbacks('on_predict_postprocess_end')
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# Visualize, save, write results
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n = len(im0s)
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for i in range(n):
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tt0 = time.time()
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im1 = im0s[i].copy()
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### create class MovingBox
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boxesMov = MovingBox(id, self.results[i].boxes.xywh, self.results[i].boxes.xyxy,
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self.results[i].boxes.conf, path)
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if id == 1:
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boxesAll = boxesMov._initiate_box()
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else:
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### allMovBoxes->是运动框的坐标
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boxesAll, allMovBoxes = boxesMov.update(boxesAll, im1, self.save_dir)
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if len(allMovBoxes) != 0:
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mov_id = 0
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for box in allMovBoxes:
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box_xyxy = xywh2xyxy(box[0])
|
||||
cropMov = im0s[i][int(box_xyxy[1]):int(box_xyxy[3]), int(box_xyxy[0]):int(box_xyxy[2]),
|
||||
::-1]
|
||||
# crop_save = os.path.abspath(im0s[i])
|
||||
# Image.fromarray(cropMov).show()
|
||||
### img_MovBoxes->运动商品图片
|
||||
img_MovBoxes.append([id, cropMov])
|
||||
tt1 = time.time() - tt0
|
||||
# print('运动框判断所需时间{}s'.format(tt1))
|
||||
crop_save = self.save_dir / 'movboxes/'
|
||||
ori_img = self.save_dir / 'oriImage/'
|
||||
if not os.path.exists(crop_save):
|
||||
# crop_save.parent.mkdir(parents=True, exist_ok=True)
|
||||
crop_save.mkdir(parents=True, exist_ok=True)
|
||||
if not os.path.exists(ori_img):
|
||||
ori_img.mkdir()
|
||||
f = str(crop_save) + '\\frame' + str(id) + '_' + str(mov_id) + '_' + '.jpg'
|
||||
g = str(ori_img) + '\\frame' + str(id) + '_' + '.jpg'
|
||||
# Image.fromarray(cropMov[::-1]).save(f, quality=95, subsampling=0)
|
||||
Image.fromarray(cropMov).save(f, quality=95, subsampling=0)
|
||||
# Image.fromarray(im1[:,:,::-1]).save(g, quality=95, subsampling=0)
|
||||
mov_id += 1
|
||||
|
||||
self.results[i].speed = {
|
||||
'preprocess': profilers[0].dt * 1E3 / n,
|
||||
'inference': profilers[1].dt * 1E3 / n,
|
||||
'postprocess': profilers[2].dt * 1E3 / n}
|
||||
if self.source_type.tensor: # skip write, show and plot operations if input is raw tensor
|
||||
continue
|
||||
p, im0 = path[i], im0s[i].copy()
|
||||
p = Path(p)
|
||||
if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
|
||||
s += self.write_results(i, self.results, (p, im, im0))
|
||||
if self.args.show and self.plotted_img is not None:
|
||||
self.show(p)
|
||||
|
||||
if self.args.save and self.plotted_img is not None:
|
||||
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
|
||||
|
||||
self.run_callbacks('on_predict_batch_end')
|
||||
yield from self.results
|
||||
# Print time (inference-only)
|
||||
if self.args.verbose:
|
||||
LOGGER.info(f'{s}{profilers[1].dt * 1E3:.1f}ms')
|
||||
t1 = time.time() - t0
|
||||
# print('推理所需时间：{}s'.format(t1))
|
||||
|
||||
t2 = time.time()
|
||||
self.real_MovBox = self.boxesMov_output(path, img_MovBoxes)
|
||||
for i in range(len(self.results)):
|
||||
self.results[i].movBoxes = self.real_MovBox
|
||||
# print("self.results11111111111", self.results[i])
|
||||
t3 = time.time() - t2
|
||||
print('保留最后十帧所有运动框所需时间：{}s'.format(t3))
|
||||
|
||||
# Release assets
|
||||
if isinstance(self.vid_writer[-1], cv2.VideoWriter):
|
||||
self.vid_writer[-1].release() # release final video writer
|
||||
|
||||
# Print results
|
||||
if self.args.verbose and self.seen:
|
||||
t = tuple(x.t / self.seen * 1E3 for x in profilers) # speeds per image
|
||||
LOGGER.info(f'Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape '
|
||||
f'{(1, 3, *self.imgsz)}' % t)
|
||||
if self.args.save or self.args.save_txt or self.args.save_crop:
|
||||
nl = len(list(self.save_dir.glob('labels/*.txt'))) # number of labels
|
||||
s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
|
||||
|
||||
self.run_callbacks('on_predict_end')
|
||||
|
||||
|
||||
|
||||
def setup_model(self, model, verbose=True):
|
||||
"""Initialize YOLO model with given parameters and set it to evaluation mode."""
|
||||
device = select_device(self.args.device, verbose=verbose)
|
||||
model = model or self.args.model
|
||||
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
|
||||
self.model = AutoBackend(model,
|
||||
device=device,
|
||||
dnn=self.args.dnn,
|
||||
data=self.args.data,
|
||||
fp16=self.args.half,
|
||||
fuse=True,
|
||||
verbose=verbose)
|
||||
self.device = device
|
||||
self.model.eval()
|
||||
|
||||
def show(self, p):
|
||||
"""Display an image in a window using OpenCV imshow()."""
|
||||
im0 = self.plotted_img
|
||||
if platform.system() == 'Linux' and p not in self.windows:
|
||||
self.windows.append(p)
|
||||
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||||
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||||
cv2.imshow(str(p), im0)
|
||||
cv2.waitKey(500 if self.batch[3].startswith('image') else 1) # 1 millisecond
|
||||
|
||||
def save_preds(self, vid_cap, idx, save_path):
|
||||
"""Save video predictions as mp4 at specified path."""
|
||||
im0 = self.plotted_img
|
||||
# Save imgs
|
||||
if self.dataset.mode == 'image':
|
||||
cv2.imwrite(save_path, im0)
|
||||
else: # 'video' or 'stream'
|
||||
if self.vid_path[idx] != save_path: # new video
|
||||
self.vid_path[idx] = save_path
|
||||
if isinstance(self.vid_writer[idx], cv2.VideoWriter):
|
||||
self.vid_writer[idx].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = int(vid_cap.get(cv2.CAP_PROP_FPS)) # integer required, floats produce error in MP4 codec
|
||||
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
|
||||
self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
self.vid_writer[idx].write(im0)
|
||||
|
||||
def run_callbacks(self, event: str):
|
||||
"""Runs all registered callbacks for a specific event."""
|
||||
for callback in self.callbacks.get(event, []):
|
||||
callback(self)
|
||||
|
||||
def add_callback(self, event: str, func):
|
||||
"""
|
||||
Add callback
|
||||
"""
|
||||
self.callbacks[event].append(func)
|
||||
|
||||
class BoxState:
|
||||
Static = 0
|
||||
Moving = 1
|
||||
class allBoxes:
|
||||
def __init__(self, fame_id, tr_id, xywh):
|
||||
self.fame_id = fame_id
|
||||
self.state = BoxState.Static
|
||||
self.track_id = tr_id
|
||||
self.xywh = xywh
|
||||
def is_static(self):
|
||||
return self.state == BoxState.Static
|
||||
def is_moving(self):
|
||||
return self.state == BoxState.Moving
|
||||
def xywh2xyxy(x):
|
||||
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[0] = x[0] - x[2] / 2 # top left x
|
||||
y[1] = x[1] - x[3] / 2 # top left y
|
||||
y[2] = x[0] + x[2] / 2 # bottom right x
|
||||
y[3] = x[1] + x[3] / 2 # bottom right y
|
||||
return y
|
||||
def plot_one_box(x, img, trid, line_thickness=3,color=None):
|
||||
# Plots one bounding box on image img
|
||||
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
|
||||
# print('x',x)
|
||||
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
|
||||
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
|
||||
cv2.putText(img, str(trid), (c1[0], c1[1] - 2), 0, tl / 3, [0, 0, 255], thickness=6, lineType=cv2.LINE_AA)
|
||||
def compute_iou(box1, box2):
|
||||
xx1 = max(box1[0], box2[0])
|
||||
yy1 = max(box1[1], box2[1])
|
||||
xx2 = min(box1[2], box2[2])
|
||||
yy2 = min(box1[3], box2[3])
|
||||
w = max(0., xx2 - xx1)
|
||||
h = max(0., yy2 - yy1)
|
||||
wh = w * h
|
||||
o = wh / ((box1[2] - box1[0]) * (box1[3] - box1[1])
|
||||
+ (box2[2] - box2[0]) * (box2[3] - box2[1]) - wh)
|
||||
return o
|
||||
|
||||
# def sort_(elem):
|
||||
# return elem[0]
|
||||
class MovingBox(object):
|
||||
def __init__(self, frame_id, xywh, xyxy, conf, path):
|
||||
# self.track_id = 0
|
||||
self.frame_id = frame_id
|
||||
# self.state = 1 # 0 static, 1 moving
|
||||
self.xywh = xywh
|
||||
self.conf = conf
|
||||
self.xyxy = xyxy
|
||||
self.min_thr = 0.8
|
||||
self.max_thr = 1.2
|
||||
self.iou_thr = 0.9
|
||||
self.path = path
|
||||
|
||||
def _initiate_box(self):
|
||||
boxes = []
|
||||
state = 0
|
||||
for i in range(len(self.xywh)):
|
||||
boxes.append([self.xywh[i], state, self.xyxy[i]])
|
||||
# print('moving_box', boxes)
|
||||
return boxes
|
||||
|
||||
def update(self, boxes, img, save_dir):
|
||||
index = []
|
||||
allBoxes = []
|
||||
state = 1
|
||||
print(">>>>>>>>>>>>id", self.frame_id)
|
||||
# if len(self.xywh) != 0: ##detect box 不为空
|
||||
if len(boxes) == 0:
|
||||
for i in range(len(self.xywh)):
|
||||
allBoxes.append([self.xywh[i], state, self.xyxy[i]])
|
||||
|
||||
else:
|
||||
for i in range(len(self.xywh)): ## self.xywh为当前帧的检测框
|
||||
flag0 = 0 # 0 长宽比未匹配上, 1 匹配上
|
||||
for j in range(len(boxes)): ## boxes为前一帧的检测框
|
||||
rw = self.xywh[i][2] / boxes[j][0][2]
|
||||
rh = self.xywh[i][3] / boxes[j][0][3]
|
||||
area0 = boxes[j][0][2] * boxes[j][0][3]
|
||||
area1 = self.xywh[i][2] * self.xywh[i][3]
|
||||
area_r = area1 / area0
|
||||
# print(f'detect_{i} track_{j}')
|
||||
# print("rw,rh,area_r", rw, rh, area_r)
|
||||
##可能短边比长边，可能长边比短边
|
||||
if (rw < self.max_thr and rw > self.min_thr) and (rh < self.max_thr and rh > self.min_thr) \
|
||||
and (area_r < self.max_thr and area_r > self.min_thr):
|
||||
flag0 = 1
|
||||
iou = compute_iou(self.xyxy[i], boxes[j][2])
|
||||
print('iou',iou)
|
||||
if iou > self.iou_thr:
|
||||
index.append(i)
|
||||
break
|
||||
print("=====last_box", boxes)
|
||||
print('index', index)
|
||||
|
||||
for i in range(len(self.xywh)):
|
||||
if i in index:
|
||||
state = 0
|
||||
allBoxes.append([self.xywh[i], state, self.xyxy[i]])
|
||||
else:
|
||||
state = 1
|
||||
allBoxes.append([self.xywh[i], state, self.xyxy[i]])
|
||||
# track_id += 1
|
||||
moving_boxes = [box for box in allBoxes if box[1]==1]
|
||||
if len(self.xywh) != 0:
|
||||
for i, x in enumerate(allBoxes):
|
||||
xyxy = xywh2xyxy(x[0])
|
||||
if x[1] == 1:
|
||||
# flag = str(i) + '_moving_'
|
||||
flag = 'moving_'
|
||||
box_color = (0,255,0)
|
||||
else:
|
||||
# flag = str(i) + '_static_'
|
||||
flag = 'static_'
|
||||
box_color = (0,0,255)
|
||||
plot_one_box(xyxy, img, f'{flag}{self.conf[i].cpu().numpy():.2f}', color=box_color)
|
||||
path2 = save_dir / 'detect_result/'
|
||||
if not os.path.exists(path2):
|
||||
path2.mkdir(parents=True, exist_ok=True)
|
||||
cv2.imwrite(os.sep.join([str(path2), str(self.path).split('.mp4')[0].split('\\')[-1]+ "_" + str(self.frame_id) + '.jpg']), img)
|
||||
else:
|
||||
path2 = save_dir / 'detect_result/'
|
||||
if not os.path.exists(path2):
|
||||
path2.mkdir(parents=True, exist_ok=True)
|
||||
cv2.imwrite(os.sep.join([str(path2), str(self.path).split('.mp4')[0].split('\\')[-1] + "_" + str(self.frame_id) + '.jpg']), img)
|
||||
return allBoxes, moving_boxes
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user