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	# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
	"""
	Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.

	Usage - sources:
	$ python detect.py --weights yolov5s.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 detect.py --weights yolov5s.pt # PyTorch
	yolov5s.torchscript # TorchScript
	yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
	yolov5s_openvino_model # 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
	yolov5s_paddle_model # PaddlePaddle
	"""

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

	import torch

	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 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,
	strip_optimizer,
	xyxy2xywh,
	)
	from utils.torch_utils import select_device, smart_inference_mode


	@smart_inference_mode()
	def run(
	weights=ROOT / "yolov5s.pt", # model path or triton URL
	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=0.025, # 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_csv=False, # save results in CSV format
	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
	vid_stride=1, # video frame-rate stride
	):
	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 or model.triton 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
	if model.xml and im.shape[0] > 1:
	ims = torch.chunk(im, im.shape[0], 0)

	# Inference
	with dt[1]:
	visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
	if model.xml and im.shape[0] > 1:
	pred = None
	for image in ims:
	if pred is None:
	pred = model(image, augment=augment, visualize=visualize).unsqueeze(0)
	else:
	pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0)
	pred = [pred, None]
	else:
	pred = model(im, augment=augment, visualize=visualize)
	# NMS
	with dt[2]:
	pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

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

	# Define the path for the CSV file
	csv_path = save_dir / "predictions.csv"

	# Create or append to the CSV file
	def write_to_csv(image_name, prediction, confidence):
	"""Writes prediction data for an image to a CSV file, appending if the file exists."""
	data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
	with open(csv_path, mode="a", newline="") as f:
	writer = csv.DictWriter(f, fieldnames=data.keys())
	if not csv_path.is_file():
	writer.writeheader()
	writer.writerow(data)

	# 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, line_width=line_thickness, example=str(names))
	if len(det):
	# Rescale boxes from img_size to im0 size
	det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()

	# 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

	# Write results
	for *xyxy, conf, cls in reversed(det):
	c = int(cls) # integer class
	label = names[c] if hide_conf else f"{names[c]}"
	confidence = float(conf)
	confidence_str = f"{confidence:.2f}"

	if save_csv:
	write_to_csv(p.name, label, confidence_str)

	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(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))
	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)
	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)

	# 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():
	"""Parses command-line arguments for YOLOv5 detection, setting inference options and model configurations."""
	parser = argparse.ArgumentParser()
	parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL")
	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-csv", action="store_true", help="save results in CSV format")
	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")
	parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
	opt = parser.parse_args()
	opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
	print_args(vars(opt))
	return opt


	def main(opt):
	"""Executes YOLOv5 model inference with given options, checking requirements before running the model."""
	check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
	run(**vars(opt))


	if __name__ == "__main__":
	opt = parse_opt()
	main(opt)