segment/predict.py

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.

Usage - sources:
    $ python segment/predict.py --weights yolov5s-seg.pt --source 0                               # webcam
                                                                  img.jpg                         # image
                                                                  vid.mp4                         # video
                                                                  screen                          # screenshot
                                                                  path/                           # directory
                                                                  list.txt                        # list of images
                                                                  list.streams                    # list of streams
                                                                  'path/*.jpg'                    # glob
                                                                  'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                                  'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python segment/predict.py --weights yolov5s-seg.pt                 # PyTorch
                                          yolov5s-seg.torchscript        # TorchScript
                                          yolov5s-seg.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                          yolov5s-seg_openvino_model     # OpenVINO
                                          yolov5s-seg.engine             # TensorRT
                                          yolov5s-seg.mlmodel            # CoreML (macOS-only)
                                          yolov5s-seg_saved_model        # TensorFlow SavedModel
                                          yolov5s-seg.pb                 # TensorFlow GraphDef
                                          yolov5s-seg.tflite             # TensorFlow Lite
                                          yolov5s-seg_edgetpu.tflite     # TensorFlow Edge TPU
                                          yolov5s-seg_paddle_model       # PaddlePaddle
"""

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

import torch

FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # 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.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,
    scale_segments,
    strip_optimizer,
)
from utils.plots import Annotator, colors, save_one_box
from utils.segment.general import masks2segments, process_mask, process_mask_native
from utils.torch_utils import select_device, smart_inference_mode


@smart_inference_mode()
def run(
    weights=ROOT / "yolov5s-seg.pt",  # model.pt path(s)
    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
    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_conf=False,  # save confidences in --save-txt labels
    save_crop=False,  # save cropped prediction boxes
    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
    project=ROOT / "runs/predict-seg",  # save results to project/name
    name="exp",  # save results to project/name
    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 confidences
    half=False,  # use FP16 half-precision inference
    dnn=False,  # use OpenCV DNN for ONNX inference
    vid_stride=1,  # video frame-rate stride
    retina_masks=False,
):
    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(".streams")
        or (is_url and not is_file)
    )
    screenshot = source.lower().startswith("screen")
    if is_url and is_file:
        source = check_file(source)  # download

    # 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
    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

    # Dataloader
    bs = 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

    # Run inference
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
    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()  # 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, proto = model(im, augment=augment, visualize=visualize)[:2]

        # NMS
        with dt[2]:
            pred = non_max_suppression(
                pred,
                conf_thres,
                iou_thres,
                classes,
                agnostic_nms,
                max_det=max_det,
                nm=32,
            )

        # Second-stage classifier (optional)
        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

        # 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 " % im.shape[2:]  # print string
            imc = im0.copy() if save_crop else im0  # for save_crop
            annotator = Annotator(
                im0, line_width=line_thickness, example=str(names)
            )
            if len(det):
                if retina_masks:
                    # scale bbox first the crop masks
                    det[:, :4] = scale_boxes(
                        im.shape[2:], det[:, :4], im0.shape
                    ).round()  # rescale boxes to im0 size
                    masks = process_mask_native(
                        proto[i], det[:, 6:], det[:, :4], im0.shape[:2]
                    )  # HWC
                else:
                    masks = process_mask(
                        proto[i],
                        det[:, 6:],
                        det[:, :4],
                        im.shape[2:],
                        upsample=True,
                    )  # HWC
                    det[:, :4] = scale_boxes(
                        im.shape[2:], det[:, :4], im0.shape
                    ).round()  # rescale boxes to im0 size

                # Segments
                if save_txt:
                    segments = [
                        scale_segments(
                            im0.shape if retina_masks else im.shape[2:],
                            x,
                            im0.shape,
                            normalize=True,
                        )
                        for x in reversed(masks2segments(masks))
                    ]

                # 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

                # Mask plotting
                annotator.masks(
                    masks,
                    colors=[colors(x, True) for x in det[:, 5]],
                    im_gpu=torch.as_tensor(im0, dtype=torch.float16)
                    .to(device)
                    .permute(2, 0, 1)
                    .flip(0)
                    .contiguous()
                    / 255
                    if retina_masks
                    else im[i],
                )

                # Write results
                for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
                    if save_txt:  # Write to file
                        seg = segments[j].reshape(-1)  # (n,2) to (n*2)
                        line = (
                            (cls, *seg, conf) if save_conf else (cls, *seg)
                        )  # 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}"
                            )
                        )
                        annotator.box_label(xyxy, label, color=colors(c, True))
                        # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
                    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)
                if cv2.waitKey(1) == ord("q"):  # 1 millisecond
                    exit()

            # 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].dt * 1E3:.1f}ms"
        )

    # 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)


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--weights",
        nargs="+",
        type=str,
        default=ROOT / "yolov5s-seg.pt",
        help="model path(s)",
    )
    parser.add_argument(
        "--source",
        type=str,
        default=ROOT / "data/images",
        help="file/dir/URL/glob/screen/0(webcam)",
    )
    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-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/predict-seg",
        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"
    )
    parser.add_argument(
        "--retina-masks",
        action="store_true",
        help="whether to plot masks in native resolution",
    )
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    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)