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=0.45, # 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_format=0, # save boxes coordinates in YOLO format or Pascal-VOC format (0 for YOLO and 1 for Pascal-VOC)

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 += "{:g}x{:g} ".format(*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

if save_format == 0:

coords = (

(xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()

) # normalized xywh

else:

coords = (torch.tensor(xyxy).view(1, 4) / gn).view(-1).tolist() # xyxy

line = (cls, *coords, conf) if save_conf else (cls, *coords) # 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:

"""

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-format",

type=int,

default=0,

help="whether to save boxes coordinates in YOLO format or Pascal-VOC format when save-txt is True, 0 for YOLO and 1 for Pascal-VOC",

)

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))

"""

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