text: # 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 streamf
  
  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 traceback
  import argparse
  import os
  import sys
  from pathlib import Path
  
  import cv2
  import torch
  import torch.backends.cudnn as cudnn
  import base64
  import numpy as np
  import os
  import shutil
  import magic
  
  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
  
  from models.common import DetectMultiBackend
  from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
  from 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 utils.plots import Annotator, colors, save_one_box
  from utils.torch_utils import select_device, time_sync
  
  
  @torch.no_grad()
  def run(
      weights=ROOT / "logodetectdefault.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
      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_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 = Path("../../cnvrg")
      (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()
  
      # 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, 3, *imgsz), half=half)  # warmup
      dt, seen = [0.0, 0.0, 0.0], 0
      all_results = {}
      all_results["output"] = []
      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
  
          # 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
              json_path = p.stem + ".json"  # im.txt
              json_path = os.path.join(save_dir, json_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
  
                  # Write results
                  jdict = {}
                  jdict[p.name] = []
  
                  width = im0.shape[1]
                  height = im0.shape[0]
                  for *xyxy, conf, cls in reversed(det):
                      tempdict = {}
  
                      if True:  # 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
                          tempdict["class"] = names[int(line[0])]
                          tempdict["conf"] = round(float(conf), 2)
                          tempdict["bbox"] = [
                              int(line[1] * width),
                              int(line[2] * height),
                              int(line[3] * width),
                              int(line[4] * height),
                          ]
                          jdict[p.name].append(tempdict)
                      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,
                              )
  
              # Print time (inference-only)
              LOGGER.info(f"{s}Done. ({t3 - t2:.3f}s)")
  
              # 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 += ".mp4"
                          vid_writer[i] = cv2.VideoWriter(
                              save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)
                          )
                      vid_writer[i].write(im0)
              all_results["output"].append(jdict)
  
      
      # 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)
      return all_results
  
  
  def parse_opt():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--weights",
          nargs="+",
          type=str,
          default=ROOT / "logodetectdefault.pt",
          help="model path(s)",
      )
      parser.add_argument(
          "--source",
          type=str,
          default=ROOT / "data/images",
          help="file/dir/URL/glob, 0 for 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/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
  
  
  def predict(sourc):
  
      image_dir = os.path.join(os.getcwd(), "images")
      os.mkdir(image_dir)
      try:
          for image in sourc["img"]:
              decoded = base64.b64decode(image)
              file_ext = magic.from_buffer(decoded, mime=True).split('/')[-1]
              nparr = np.fromstring(decoded, np.uint8)
              img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
              savepath = os.path.join(image_dir, f'img.{file_ext}')
              cv2.imwrite(savepath, img)
          if os.path.exists("/input/retrain/weights/best.pt"):
              results = run(
                  weights="/input/retrain/weights/best.pt",
                  source="images",
                  nosave=True,
              )
              shutil.rmtree("images")
              return results
          else:
              results = run(source="images", nosave=True)
              shutil.rmtree("images")
              return results
      except Exception as e:
          traceback.print_exc()
          shutil.rmtree("images")
          return {}
  
  
  def main(opt):
      check_requirements(exclude=("tensorboard", "thop"))
      run(**vars(opt))
  
  
  if __name__ == "__main__":
      opt = parse_opt()
      main(opt)
  