# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

import os
import sys
from pathlib import Path
import cv2

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

import torch
from yolov5.utils.torch_utils import select_device, time_sync
from yolov5.utils.plots import Annotator, colors, save_one_box
from yolov5.utils.general import (check_img_size,
                        increment_path, non_max_suppression, scale_coords, xyxy2xywh)
from yolov5.utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages
from yolov5.models.common import DetectMultiBackend

def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for 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
        save_img = False,
        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/detect',  # 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
        ):
    
    import torch
    from utils.torch_utils import select_device, time_sync
    from utils.plots import Annotator, colors, save_one_box
    from utils.general import (check_img_size,
                            increment_path, non_max_suppression, scale_coords, xyxy2xywh)
    from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages
    from models.common import DetectMultiBackend
    source = str(source)

    save_dir = None
    save_path = None
    # 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)
    
    # Directories
    if project is not None:
        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)
    stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Half
    half &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16 supported on limited backends with CUDA
    if pt or jit:
        model.model.half() if half else model.model.float()

    dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
    bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1, 3, *imgsz), half=half)  # warmup
    dt, seen = [0.0, 0.0, 0.0], 0

    #with tqdm(dataset) as pbar:
    #    pbar.set_description("Document Image Analysis")
    for path, im, im0s, vid_cap, s in dataset:
        #print(path)
        t1 = time_sync()
        im = torch.from_numpy(im).to(device)
        im = im.half() if half 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
        t2 = time_sync()
        dt[0] += t2 - t1

        # Inference
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2

        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3

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

        # Process predictions
        preds = []
        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
            if save_dir is not None:
                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
            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_coords(im.shape[2:], det[:, :4], im0.shape).round()

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

                # Write results
                if save_txt:
                    with open(txt_path + '.txt', 'w') as f:
                        for *xyxy, conf, cls in reversed(det):
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                            preds.append({"class": str(int(cls)), "xmin": int(xyxy[0]), "ymin": int(xyxy[1]), "xmax": int(xyxy[2]),"ymax": int(xyxy[3]), "conf": float(conf)})
                            if save_txt:  # Write to file
                                line = (int(cls), *xywh, conf) if save_conf else (cls, *xywh)  # label format
                                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))
                                if save_crop:
                                    save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
                else:
                    for *xyxy, conf, cls in reversed(det):
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        preds.append({"class": str(int(cls)), "xmin": int(xyxy[0]), "ymin": int(xyxy[1]), "xmax": int(xyxy[2]),"ymax": int(xyxy[3]), "conf": float(conf)})
                        
            # Print time (inference-only)
            # LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')

            # Stream results
            if save_img:
                im0 = annotator.result()
                if view_img:
                    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)
        yield preds, save_path
    # Print results
    #t = tuple(x / 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)  # update model (to fix SourceChangeWarning) """


def load_yolo_model(weights, device="cpu", imgsz=[1280, 1280]):
    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=False, data=ROOT / 'data/coco128.yaml')
    stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    half = False
    # Half
    half &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16 supported on limited backends with CUDA
    if pt or jit:
        model.model.half() if half else model.model.float()
    model.warmup(imgsz=(1, 3, *imgsz), half=half)
    
    return model, stride, names, pt, jit, onnx, engine


def predict(model,  # model.pt path(s)
            stride, names, pt, jit, onnx, engine,
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.5,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        save_img = False,
        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/detect',  # 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
        
        ):
    
    
    source = str(source)

    save_dir = None
    save_path = None
    # 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)
    
    # Directories
    if project is not None:
        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

    dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
    #bs = 1  # batch_size
    #vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference

    dt, seen = [0.0, 0.0, 0.0], 0

    #with tqdm(dataset) as pbar:
    #    pbar.set_description("Document Image Analysis")
    for path, im, im0s, vid_cap, s in dataset:
        #print(path)
        t1 = time_sync()
        im = torch.from_numpy(im).to(device)
        im = im.half() if half 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
        t2 = time_sync()
        dt[0] += t2 - t1

        # Inference
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2

        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3

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

        # Process predictions
        preds = []
        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
            
            #s += '%gx%g ' % im.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_coords(im.shape[2:], det[:, :4], im0.shape).round()

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

                for *xyxy, conf, cls in reversed(det):
                    # xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    preds.append({"class": str(int(cls)), "xmin": int(xyxy[0]), "ymin": int(xyxy[1]), "xmax": int(xyxy[2]),"ymax": int(xyxy[3]), "conf": float(conf)})
                    
        yield preds, save_path