detect.py

import argparse
import os
import platform
import sys
from pathlib import Path

import torch

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  

from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode

import torch.nn.functional as F         
import numpy as np                      

Cityscapes_COLORMAP = [         
    [0, 0, 0],
    [255, 255, 255],
    [70, 70, 70],
    [102, 102, 156],
    [190, 153, 153],
    [153, 153, 153],
    [250, 170, 30],
    [220, 220, 0],
    [107, 142, 35],
    [152, 251, 152],
    [0, 130, 180],
    [220, 20, 60],
    [255, 0, 0],
    [0, 0, 142],
    [0, 0, 70],
    [0, 60, 100],
    [0, 80, 100],
    [0, 0, 230],
    [119, 11, 32],
]

Cityscapes_IDMAP = [                    
    [7],
    [8],
    [11],
    [12],
    [13],
    [17],
    [19],
    [20],
    [21],
    [22],
    [23],
    [24],
    [25],
    [26],
    [27],
    [28],
    [31],
    [32],
    [33],
]
Cityscapes_Class = ["road", "sidewalk", "building", "wall", "fence",        
               "pole", "traffic light", "traffic sign", "vegetation",
               "terrain", "sky", "person", "rider", "car", "truck",
               "bus", "train", "motorcycle", "bicyle"]

def label2image(pred, COLORMAP=Cityscapes_COLORMAP):                    
    colormap = np.array(COLORMAP, dtype='uint8')
    X = pred.astype('int32')
    return colormap[X, :]

def trainid2id(pred, IDMAP=Cityscapes_IDMAP):                           
    colormap = np.array(IDMAP, dtype='uint8')
    X = pred.astype('int32')
    return colormap[X, :]
@smart_inference_mode()
def run(
        weights=ROOT / 'yolo.pt',  
        source=ROOT / 'data/images',  
        data=ROOT / 'data/coco.yaml',  
        imgsz=(640, 640),  
        conf_thres=0.25,  
        iou_thres=0.45,  
        max_det=1000,  
        device='',  
        view_img=False,  
        save_txt=False,  
        save_conf=False,  
        save_crop=False,  
        nosave=False,  
        classes=None,  
        agnostic_nms=False,  
        augment=False,  
        visualize=False,  
        update=False,  
        project=ROOT / 'runs/detect',  
        name='exp',  
        exist_ok=False,  
        line_thickness=3,  
        hide_labels=False,  
        hide_conf=False,  
        half=False,  
        dnn=False,  
        vid_stride=1,  
        save_as_video=False,
        submit=False
):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    screenshot = source.lower().startswith('screen')
    if is_url and is_file:
        source = check_file(source)  

    
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  

    if opt.submit:                                          
        sub_dir = str(save_dir) + "/results/"
        if not os.path.exists(sub_dir):
            os.mkdir(sub_dir)


    
    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)  

    
    bs = 1  
    if webcam:
        view_img = check_imshow(warn=True)
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = len(dataset)
    elif screenshot:
        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
    
    vid_path, vid_writer, s_writer = None, None, None       
    
    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  
    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
    for path, im, im0s, vid_cap, s in dataset:
        with dt[0]:
            im = torch.from_numpy(im).to(model.device)
            im = im.half() if model.fp16 else im.float()  
            im /= 255  
            if len(im.shape) == 3:
                im = im[None]  

        
        with dt[1]:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            
            out = model(im, augment=augment, visualize=visualize)
            pred = out[0][0]               
            seg = out[1]                  
        
        with dt[2]:
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

        
        

        
        for i, det in enumerate(pred):  
            seen += 1
            if webcam:  
                p, im0, frame = path[i], im0s[i].copy(), dataset.count
                s += f'{i}: '
            else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  
            save_path = str(save_dir / p.name)  
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  
            s += '%gx%g ' % im.shape[2:]  
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  
            imc = im0.copy() if save_crop else im0  
            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
            if len(det):
                
                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()

                
                for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum()  
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  

                
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  
                        with open(f'{txt_path}.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or save_crop or view_img:  
                        c = int(cls)  
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        annotator.box_label(xyxy, label, color=colors(c, True))
                    if save_crop:
                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
            
            seg = F.interpolate(seg, (im0.shape[0], im0.shape[1]), mode='bilinear', align_corners=True)[0]  
            mask = label2image(seg.max(axis=0)[1].cpu().numpy(), Cityscapes_COLORMAP)[:, :, ::-1]  
            
            
            dst = cv2.addWeighted(mask, 0.4, im0, 0.6, 0)  
            
            im0 = annotator.result()
            if view_img:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                cv2.imshow(str(p), im0)
                cv2.imshow("segmentation", mask)  
                cv2.imshow("mix", dst)  
                
                cv2.waitKey(0)  

            if opt.submit:                                  
                sub_path = sub_dir+str(p.name)
                sub_path = sub_path[:-4] + "_pred.png"
                result = trainid2id(seg.max(axis=0)[1].cpu().numpy(), Cityscapes_IDMAP)
                cv2.imwrite(sub_path, result)

            
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                    cv2.imwrite(save_path[:-4] + "_mask" + save_path[-4:], mask)  
                    cv2.imwrite(save_path[:-4] + "_dst" + save_path[-4:], dst)    
                else:  
                    if vid_path[i] != save_path:  
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  
                        if vid_cap:  
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path = str(Path(save_path).with_suffix('.mp4'))  
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    
                    vid_writer[i].write(dst)  
            if opt.save_as_video:           
                if not s_writer:
                    fps, w, h = 30, dst.shape[1], dst.shape[0]
                    s_writer = cv2.VideoWriter(str(save_dir)+"out.mp4", cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                s_writer.write(dst)
        
        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")

    
    t = tuple(x.t / seen * 1E3 for x in dt)  
    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 s_writer != None:                
        s_writer.release()              

    if update:
        strip_optimizer(weights[0])  


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / '/home/wuren123/yb/multi task/niou/niou1/runs/train/ce SLSIoULoss55/weights/best.pt', help='model path or triton URL')
    parser.add_argument('--source', type=str, default=ROOT / 'figure', help='file/dir/URL/glob/screen/0(webcam)')
    parser.add_argument('--data', type=str, default=ROOT / 'data/custom.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-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='1', 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')
    parser.add_argument('--save-as-video', action='store_true', help='save same size images as a video')  
    parser.add_argument('--submit', action='store_true', help='get submit file in folder submit')  
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  
    print_args(vars(opt))
    return opt


def main(opt):
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)