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王庆刚
2024-11-04 18:06:52 +08:00
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# -*- coding: utf-8 -*-
"""
Created on Fri Oct 18 13:09:42 2024
@author: ym
"""
import argparse
import os
import sys
import torch
from pathlib import Path
import numpy as np
# from matplotlib.pylab import mpl
# mpl.use('Qt5Agg')
import matplotlib.pyplot as plt
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend
from utils.augmentations import letterbox
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.torch_utils import select_device, smart_inference_mode
'''集成跟踪模块，输出跟踪结果文件 .npy'''
# from ultralytics.engine.results import Boxes # Results
# from ultralytics.utils import IterableSimpleNamespace, yaml_load
from tracking.utils.plotting import Annotator, colors
from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load, boxes_add_fid
from tracking.trackers import BOTSORT, BYTETracker
from tracking.utils.showtrack import drawtracks
from hands.hand_inference import hand_pose
from tracking.trackers.reid.reid_interface import ReIDInterface
from tracking.trackers.reid.config import config as ReIDConfig
ReIDEncoder = ReIDInterface(ReIDConfig)
IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes
VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
class LoadImages:
# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
def __init__(self, files, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
ni, nv = len(images), len(videos)
self.img_size = img_size
self.stride = stride
self.files = images + videos
self.nf = ni + nv # number of files
self.video_flag = [False] * ni + [True] * nv
self.mode = 'image'
self.auto = auto
self.transforms = transforms # optional
self.vid_stride = vid_stride # video frame-rate stride
if any(videos):
self._new_video(videos[0]) # new video
else:
self.cap = None
def __iter__(self):
self.count = 0
return self
def __next__(self):
if self.count == self.nf:
raise StopIteration
path = self.files[self.count]
if self.video_flag[self.count]:
# Read video
self.mode = 'video'
for _ in range(self.vid_stride):
self.cap.grab()
ret_val, im0 = self.cap.retrieve()
while not ret_val:
self.count += 1
self.cap.release()
if self.count == self.nf: # last video
raise StopIteration
path = self.files[self.count]
self._new_video(path)
ret_val, im0 = self.cap.read()
self.frame += 1
# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False
s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
else:
# Read image
self.count += 1
im0 = cv2.imread(path) # BGR
# image rorate
(h, w) = im0.shape[:2]
center = (w // 2, h // 2)
angle = 90
scale = 1.0
M = cv2.getRotationMatrix2D(center, angle, scale)
im0 = cv2.warpAffine(im0, M, (h, w))
assert im0 is not None, f'Image Not Found {path}'
s = f'image {self.count}/{self.nf} {path}: '
if self.transforms:
im = self.transforms(im0) # transforms
else:
im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
return path, im, im0, self.cap, s
def _new_video(self, path):
# Create a new video capture object
self.frame = 0
self.cap = cv2.VideoCapture(path)
self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees
# self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493
def _cv2_rotate(self, im):
# Rotate a cv2 video manually
if self.orientation == 0:
return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
elif self.orientation == 180:
return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
elif self.orientation == 90:
return cv2.rotate(im, cv2.ROTATE_180)
return im
def __len__(self):
return self.nf # number of files
def inference_image(image, detections):
H, W, _ = np.shape(image)
imgs = []
batch_patches = []
patches = []
for d in range(np.size(detections, 0)):
tlbr = detections[d, :4].astype(np.int_)
tlbr[0] = max(0, tlbr[0])
tlbr[1] = max(0, tlbr[1])
tlbr[2] = min(W - 1, tlbr[2])
tlbr[3] = min(H - 1, tlbr[3])
img1 = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]
img = img1[:, :, ::-1].copy() # the model expects RGB inputs
patch = ReIDEncoder.transform(img)
imgs.append(img1)
# patch = patch.to(device=self.device).half()
if str(ReIDEncoder.device) != "cpu":
patch = patch.to(device=ReIDEncoder.device).half()
else:
patch = patch.to(device=ReIDEncoder.device)
patches.append(patch)
if (d + 1) % ReIDEncoder.batch_size == 0:
patches = torch.stack(patches, dim=0)
batch_patches.append(patches)
patches = []
if len(patches):
patches = torch.stack(patches, dim=0)
batch_patches.append(patches)
features = np.zeros((0, ReIDEncoder.embedding_size))
for patches in batch_patches:
pred = ReIDEncoder.model(patches)
pred[torch.isinf(pred)] = 1.0
feat = pred.cpu().data.numpy()
features = np.vstack((features, feat))
return imgs, features
def init_trackers(tracker_yaml = None, bs=1):
"""
Initialize trackers for object tracking during prediction.
"""
# tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml"
TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT}
cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml))
trackers = []
for _ in range(bs):
tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
trackers.append(tracker)
return trackers
def run(
weights=ROOT / 'yolov5s.pt', # model path or triton URL
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
tracker_yaml = "./tracking/trackers/cfg/botsort.yaml",
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_csv=False, # save results in CSV format
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
save_img = True,
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidencesL
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
vid_stride=1, # video frame-rate stride
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
):
assert isinstance(source,list), "source must be a list"
fulldir, imgname = os.path.split(source[0])
imgbase, ext = os.path.splitext(imgname)
# 事件名、相机类型
EventName = fulldir.split('\\')[-2] + "_" + str(Path(fulldir).stem)
CamerType = imgbase.split('_')[1]
save_dir = Path(project) / Path(EventName)
if save_dir.exists():
print(Path(fulldir).stem)
# save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
# save_dir.mkdir(parents=True, exist_ok=True) # make dir
else:
save_dir.mkdir(parents=True, exist_ok=True)
save_path_video = os.path.join(str(save_dir), f"{EventName}_{CamerType}")
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size(imgsz, s=stride) # check image size
bs = 1 # batch_size
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
seen, dt = 0, (Profile(), Profile(), Profile())
tracker = init_trackers(tracker_yaml, bs)[0]
track_boxes = np.empty((0, 10), dtype = np.float32)
k = 0
for path, im, im0s, vid_cap, s in dataset:
# k +=1
# if k==60:
# break
timeStamp = Path(path).stem.split('_')[2]
with dt[0]:
im = torch.from_numpy(im).to(model.device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
# NMS
with dt[2]:
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
im0 = im0s.copy()
s += '%gx%g ' % im.shape[2:] # print string
annotator = Annotator(im0s, line_width=line_thickness, example=str(names))
nd = len(det)
if nd:
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index, timestamp]
0 1 2 3 4 5 6 7 8
这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变
'''
det_tracking = Boxes(det, im0.shape).cpu().numpy()
tracks = tracker.update(det_tracking, im0)
if len(tracks) == 0:
continue
tracks[:, 7] = dataset.count
stamp = np.ones((len(tracks), 1)) * int(timeStamp)
tracks = np.concatenate((tracks, stamp), axis=1)
'''================== 1. 存储 dets/subimgs/features Dict ============='''
# imgs, features = inference_image(im0, tracks)
track_boxes = np.concatenate([track_boxes, tracks], axis=0)
for *xyxy, id, conf, cls, fid, bid, t in reversed(tracks):
name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)]
label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}')
if id >=0 and cls==0:
color = colors(int(cls), True)
elif id >=0 and cls!=0:
color = colors(int(id), True)
else:
color = colors(19, True) # 19为调色板的最后一个元素
annotator.box_label(xyxy, label, color=color)
# Save results (image and video with tracking)
im0 = annotator.result()
p = Path(path) # to Path
if save_img:
imgpath = str(save_dir/p.stem) + f"_{dataset.count}.png"
cv2.imwrite(Path(imgpath), im0)
if vid_path[i] != save_path_video: # new video
vid_path[i] = save_path_video
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
fps, w, h = 30, im0.shape[1], im0.shape[0]
vpath = str(Path(save_path_video).with_suffix('.mp4'))
vid_writer[i] = cv2.VideoWriter(vpath, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
for v in vid_writer:
v.release()
if track_boxes.size == 0:
return CamerType, []
save_path_np = os.path.join(str(fulldir), f"{EventName}_{CamerType}")
np.save(save_path_np, track_boxes)
# 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)
return CamerType, track_boxes
def parse_opt():
modelpath = ROOT / 'ckpts/best_cls10_0906.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt, best_yolov5m_250000'
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='', 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 run_yolo(eventdir, savedir):
opt = parse_opt()
optdict = vars(opt)
optdict["project"] = savedir
optdict["source"] = eventdir
run(**vars(opt))