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

Usage - sources:
    $ python path/to/detect.py --weights yolov5s.pt --source 0              # webcam
                                                             img.jpg        # image
                                                             vid.mp4        # video
                                                             path/          # directory
                                                             path/*.jpg     # glob
                                                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
                                         yolov5s.torchscript        # TorchScript
                                         yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                         yolov5s.xml                # OpenVINO
                                         yolov5s.engine             # TensorRT
                                         yolov5s.mlmodel            # CoreML (MacOS-only)
                                         yolov5s_saved_model        # TensorFlow SavedModel
                                         yolov5s.pb                 # TensorFlow GraphDef
                                         yolov5s.tflite             # TensorFlow Lite
                                         yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
"""

import argparse
import os
import sys
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn

from yolov5.models.common import DetectMultiBackend
from cnn_runner.yolo.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from yolov5.utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from yolov5.utils.plots import Annotator, colors, save_one_box
from yolov5.utils.torch_utils import select_device, time_sync
import glob

class YoloDetector():
    def __init__(self, source_img_path, cfg, weights, out):
        self.source_img_path = source_img_path
        self.cfg = cfg
        self.out = out
        self.weights = weights

    @torch.no_grad()
    def run(self,
            # weights='yolo_weights/last.pt',  # model.pt path(s)
            # source='../selected_areas',  # file/dir/URL/glob, 0 for webcam
            # data='yamls/aes.yaml',  # dataset.yaml path
            imgsz=(640, 640),  # inference size (height, width)
            conf_thres=0.25,  # confidence threshold
            iou_thres=0.25,  # 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=True,  # save results to *.txt
            save_conf=True,  # 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='../detect',  # save results to project/name
            name='',  # save results to project/name
            exist_ok=False,  # existing project/name ok, do not increment
            line_thickness=None,  # 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
            classes_names=None,
            box_colors=None,
            fontsize=None
            ):

        # self.detect_signal_percent.emit(0)
        source = self.source_img_path

        # print(source)
        data = self.cfg
        # print(data)
        weights = self.weights

        # print(weights)
        project = self.out

        if not line_thickness or not fontsize:

            if os.path.isdir(source):
                files = sorted(glob.glob(os.path.join(source, '*.*')))  # dir
                im = cv2.imread(files[0])
            else:
                im = cv2.imread(source)
            if not line_thickness:
                line_thickness = max(round(sum(im.shape) / 2 * 0.003), 2)
                # print("Img shape: ", im.shape)
                # thickness = 2
            if not fontsize:
                fontsize = max(round(sum(im.shape) / 2 * 0.02), 12)
                # print("Img size: ", im.size)
                # fontsize = 13

        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)
        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)

        # model.names = ['reactor', 'engine room', 'spillway station', 'build', 'parking' , 'water pumping station', 'cooling tower', 'switchgear', 'turbine', 'cool sq' , 'cool tower vent', 'reactor sq' ]
        # model.names = ['реактор', 'машинный зал', 'БСС', 'парковка', 'ВНС',
        #                'градирня', 'РУ', 'турбина', 'градирня вент кв', 'градирня вент', 'реактор кв']
        if not classes_names:
            model.names = ['реактор', 'реактор кв', 'градирня', 'градирня вент кв', 'градирня вент', 'РУ',
             'ВНС', 'турбина', 'БСС', 'машинный зал', 'парковка']
        else:
            model.names = classes_names
        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()

        # Dataloader
        if webcam:
            view_img = check_imshow()
            cudnn.benchmark = True  # set True to speed up constant image size inference
            dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
            bs = len(dataset)  # batch_size
        else:
            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 if pt else bs, 3, *imgsz))  # warmup
        dt, seen = [0.0, 0.0, 0.0], 0

        dataset_size = len(dataset)
        img_tek_num = 0

        for path, im, im0s, vid_cap, s in dataset:
            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)
            percent_base = 90
            pred_num = len(pred)

            # 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
                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, example=str(names), line_width=line_thickness, font_size=fontsize)
                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 os.path.exists(txt_path + '.txt'):
                        file_txt = open(txt_path + '.txt', 'w')
                        file_txt.close()

                    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(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 not box_colors:
                                annotator.box_label(xyxy, label, color=colors(c, True))
                            else:
                                annotator.box_label(xyxy, label, color=box_colors[c])
                            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:
                    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)

                img_tek_num += 1
            # Print time (inference-only)
            LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')

        # 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 parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='yolo_weights/best.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default='../selected_areas', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--data', type=str, default='yams/aes.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=0.1, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.1, 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', default=False, action='store_true', help='show results')
    parser.add_argument('--save-txt', default=True, action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', default=True, 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='../detect', 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')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(FILE.stem, opt)
    return opt

