443 lines
18 KiB
Python
443 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Fri Oct 18 13:09:42 2024
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@author: ym
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"""
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import argparse
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import os
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import sys
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import torch
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from pathlib import Path
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import numpy as np
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# from matplotlib.pylab import mpl
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# mpl.use('Qt5Agg')
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import matplotlib.pyplot as plt
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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.augmentations import letterbox
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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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# 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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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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'''================== 对图像进行旋转 ================== '''
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class LoadImages:
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# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
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def __init__(self, files, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
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images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
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videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
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ni, nv = len(images), len(videos)
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self.img_size = img_size
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self.stride = stride
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self.files = images + videos
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self.nf = ni + nv # number of files
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self.video_flag = [False] * ni + [True] * nv
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self.mode = 'image'
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self.auto = auto
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self.transforms = transforms # optional
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self.vid_stride = vid_stride # video frame-rate stride
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if any(videos):
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self._new_video(videos[0]) # new video
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else:
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self.cap = None
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self):
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if self.count == self.nf:
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raise StopIteration
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path = self.files[self.count]
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if self.video_flag[self.count]:
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# Read video
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self.mode = 'video'
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for _ in range(self.vid_stride):
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self.cap.grab()
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ret_val, im0 = self.cap.retrieve()
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while not ret_val:
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self.count += 1
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self.cap.release()
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if self.count == self.nf: # last video
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raise StopIteration
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path = self.files[self.count]
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self._new_video(path)
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ret_val, im0 = self.cap.read()
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self.frame += 1
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# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False
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s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
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else:
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# Read image
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self.count += 1
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im0 = cv2.imread(path) # BGR
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# image rorate
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(h, w) = im0.shape[:2]
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center = (w // 2, h // 2)
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angle = 90
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scale = 1.0
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M = cv2.getRotationMatrix2D(center, angle, scale)
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im0 = cv2.warpAffine(im0, M, (h, w))
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assert im0 is not None, f'Image Not Found {path}'
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s = f'image {self.count}/{self.nf} {path}: '
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if self.transforms:
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im = self.transforms(im0) # transforms
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else:
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im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize
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im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
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im = np.ascontiguousarray(im) # contiguous
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return path, im, im0, self.cap, s
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def _new_video(self, path):
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# Create a new video capture object
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self.frame = 0
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self.cap = cv2.VideoCapture(path)
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self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
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self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees
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# self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493
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def _cv2_rotate(self, im):
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# Rotate a cv2 video manually
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if self.orientation == 0:
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return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
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elif self.orientation == 180:
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return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
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elif self.orientation == 90:
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return cv2.rotate(im, cv2.ROTATE_180)
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return im
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def __len__(self):
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return self.nf # number of files
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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):
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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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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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save_img = True,
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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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assert isinstance(source,list), "source must be a list"
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fulldir, imgname = os.path.split(source[0])
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imgbase, ext = os.path.splitext(imgname)
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# 事件名、相机类型
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EventName = fulldir.split('\\')[-2] + "_" + str(Path(fulldir).stem)
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CamerType = imgbase.split('_')[1]
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save_dir = Path(project) / Path(EventName)
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if save_dir.exists():
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print(Path(fulldir).stem)
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# save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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# 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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save_path_video = os.path.join(str(save_dir), f"{EventName}_{CamerType}")
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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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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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track_boxes = np.empty((0, 10), dtype = np.float32)
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k = 0
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for path, im, im0s, vid_cap, s in dataset:
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# k +=1
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# if k==60:
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# break
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timeStamp = Path(path).stem.split('_')[2]
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with dt[0]:
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im = torch.from_numpy(im).to(model.device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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# Inference
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with dt[1]:
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred = model(im, augment=augment, visualize=visualize)
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# NMS
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with dt[2]:
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
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# Process predictions
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for i, det in enumerate(pred): # per image
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seen += 1
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im0 = im0s.copy()
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s += '%gx%g ' % im.shape[2:] # print string
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annotator = Annotator(im0s, 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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'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index, timestamp]
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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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continue
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tracks[:, 7] = dataset.count
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stamp = np.ones((len(tracks), 1)) * int(timeStamp)
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tracks = np.concatenate((tracks, stamp), axis=1)
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'''================== 1. 存储 dets/subimgs/features Dict ============='''
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# imgs, features = inference_image(im0, tracks)
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track_boxes = np.concatenate([track_boxes, tracks], axis=0)
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for *xyxy, id, conf, cls, fid, bid, t 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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p = Path(path) # to Path
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if save_img:
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imgpath = str(save_dir/p.stem) + f"_{dataset.count}.png"
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cv2.imwrite(Path(imgpath), im0)
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if vid_path[i] != save_path_video: # new video
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vid_path[i] = save_path_video
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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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fps, w, h = 30, im0.shape[1], im0.shape[0]
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vpath = str(Path(save_path_video).with_suffix('.mp4'))
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vid_writer[i] = cv2.VideoWriter(vpath, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer[i].write(im0)
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LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
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for v in vid_writer:
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v.release()
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if track_boxes.size == 0:
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return CamerType, []
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save_path_np = os.path.join(str(fulldir), f"{EventName}_{CamerType}")
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np.save(save_path_np, track_boxes)
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# Print results
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t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
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return CamerType, track_boxes
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def parse_opt():
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modelpath = ROOT / 'ckpts/best_cls10_0906.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt, best_yolov5m_250000'
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str, default=modelpath, help='model path or triton URL') # 'yolov5s.pt', best_15000_0908.pt
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parser.add_argument('--source', type=str, default='', help='file/dir/URL/glob/screen/0(webcam)') # images, videos
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
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parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
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parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
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parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', action='store_true', help='show results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-csv', action='store_true', help='save results in CSV format')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
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parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--visualize', action='store_true', help='visualize features')
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parser.add_argument('--update', action='store_true', help='update all models')
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parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
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parser.add_argument('--name', default='exp', help='save results to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
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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 run_yolo(eventdir, savedir):
|
||
|
||
opt = parse_opt()
|
||
optdict = vars(opt)
|
||
optdict["project"] = savedir
|
||
optdict["source"] = eventdir
|
||
run(**vars(opt))
|
||
|
||
|
||
|
||
|
||
|