detect.py

import platform
import argparse
import sys
from pathlib import Path
import logging
import cv2

import torch
from tqdm import tqdm

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # ObjectBox root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = ROOT.relative_to(Path.cwd())  # relative

from datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams, LoadScreenshots
from utils import check_img_size, check_suffix, xyxy2xywh, non_max_suppression, select_device, \
    increment_path, colorstr, check_file, Profile, save_one_box, strip_optimizer, scale_boxes, attempt_load
from plots import Annotator, colors, check_imshow
from flag_sets import *
LOGGER = logging.getLogger(__name__)



@torch.no_grad()
def run(weights=None,  # model.pt path
        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
        batch_size=32,  # batch size
        imgsz=640,  # inference size (pixels)
        conf_thres=0.001,  # confidence threshold
        iou_thres=0.6,  # NMS IoU threshold
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        single_cls=False,  # treat as single-class dataset
        augment=False,  # augmented inference
        save_txt=False,  # save results to *.txt
        save_hybrid=False,  # save label+prediction hybrid results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        project=ROOT / 'runs/val',  # save to project/name
        name='exp',  # save to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        half=True,  # use FP16 half-precision inference
        model=None,
        save_dir=Path(''),
        nosave=False,  # do not save images/videos
        vid_stride=1,  # video frame-rate stride
        line_thickness=2,  # bounding box thickness (pixels)
        view_img=False,  # show results
        update=False,  # update all models
        save_crop=False,  # save cropped prediction boxes
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        visualize=False,  # visualize features
        vis_conf_thres=0.25,  # conf thresh to visualize objects
        ):
    
    device = select_device(device, batch_size=batch_size)

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Load model
    check_suffix(weights, '.pt')
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride, names, pt = max(int(model.stride.max()), 32), model.names, weights
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Half
    half &= device.type != 'cpu'  # half precision only supported on CUDA
    model.half() if half else model.float()

    # Configure
    model.eval()
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    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)  # download

    # Dataloader
    bs = nb = 1  # batch_size
    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 = [None] * bs, [None] * bs

    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    
    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
    for path, im, im0s, vid_cap, s in dataset:
        with dt[0]:
            img = torch.from_numpy(im).to(device).unsqueeze(0)
            img = img.half() if half else img.float()  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            targets = torch.empty([0,6])
            targets = targets.to(device)
            _, _, height, width = img.shape  # batch size, channels, height, width

        # Inference
        with dt[1]:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            out, _ = model(img, augment=augment)  # inference and training outputs

        # NMS
        with dt[2]:
            targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
            lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
            pred = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)

        # Process predictions
        for i, det in enumerate(pred):  # per image
            seen += 1
            if webcam:  # batch_size >= 1
                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)  # to Path
            save_path = str(save_dir / p.name)  # im.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        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:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        if conf > vis_conf_thres:
                            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)

            # Stream results
            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)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  # release previous video writer
                        if vid_cap:  # video
                            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:  # 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
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)

        # Print time (inference-only)
        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].start * 1E3:.1f}ms")

    # Print results
    t = tuple(x.start / seen * 1E3 for x in dt)  # speeds per image
    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *[imgsz])}' % t)
    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
    if update:
        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)




def main(_argv):
    parser = argparse.ArgumentParser()
    opt = parser.parse_args()

    for k, v in FLAGS.__flags.items():
        if k in ('weights', 'source', 'view_img', 'visualize', 'vis_conf_thres', 'hide_conf', 
                'hide_labels', 'save_crop', 'nosave', 'batch_size', 'imgsz', 'conf_thres', 'iou_thres', 
                'device', 'single_cls', 'augment', 'save_txt', 'save_hybrid', 'save_conf', 'project', 'name', 
                'exist_ok', 'half', 'model', 'save_dir'):
            setattr(opt, k, v.value)

    run(**vars(opt))



if __name__ == "__main__":
    app.run(main)