# YOLOv5 🚀 by Ultralytics, AGPL-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/LNwODJXcvt4'  # 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 ultralytics.utils.plotting import Annotator, colors, save_one_box

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.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(device=device), Profile(device=device), Profile(device=device))

    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(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))

    run(**vars(opt))

if __name__ == '__main__':

    opt = parse_opt()

    main(opt)