source='data/images', # file/dir/URL/glob, 0 for webcam

imgsz=640, # inference size (pixels)

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

update=False, # update all models

project='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

):

save_img = not nosave and not source.endswith('.txt') # save inference images

webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(

('rtsp://', 'rtmp://', 'http://', 'https://'))

# 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

# Initialize

set_logging()

device = select_device(device)

half &= device.type != 'cpu' # half precision only supported on CUDA

# Load model

model = attempt_load(weights, map_location=device) # load FP32 model

stride = int(model.stride.max()) # model stride

imgsz = check_img_size(imgsz, s=stride) # check image size

names = model.module.names if hasattr(model, 'module') else model.names # get class names

if half:

model.half() # to FP16

# Second-stage classifier

classify = False

if classify:

modelc = load_classifier(name='resnet50', n=2) # initialize

modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()

# 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)

bs = len(dataset) # batch_size

else:

dataset = LoadImages(source, img_size=imgsz, stride=stride)

bs = 1 # batch_size

vid_path, vid_writer = [None] * bs, [None] * bs

# Run inference

if device.type != 'cpu':

model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once

t0 = time.time()

for path, img, im0s, vid_cap in dataset:

img = torch.from_numpy(img).to(device)

img = img.half() if half else img.float() # uint8 to fp16/32

img /= 255.0 # 0 - 255 to 0.0 - 1.0

if img.ndimension() == 3:

img = img.unsqueeze(0)

# Inference

t1 = time_synchronized()

pred = model(img, augment=augment)[0]

# Apply NMS

pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

t2 = time_synchronized()

# Apply Classifier

if classify:

pred = apply_classifier(pred, modelc, img, im0s)

# Process detections

for i, det in enumerate(pred): # detections per image

if webcam: # batch_size >= 1

p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count

else:

p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

p = Path(p) # to Path

save_path = str(save_dir / p.name) # img.jpg

txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt

s += '%gx%g ' % img.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

if len(det):

# Rescale boxes from img_size to im0 size

det[:, :4] = scale_coords(img.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

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}')

plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)

if save_crop:

save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

# Print time (inference + NMS)

print(f'{s}Done. ({t2 - t1:.3f}s)')

# Stream results

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)

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 ''

print(f"Results saved to {save_dir}{s}")

if update:

strip_optimizer(weights) # update model (to fix SourceChangeWarning)

parser = argparse.ArgumentParser()

parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')

parser.add_argument('--source', type=str, default='data/images', help='file/dir/URL/glob, 0 for webcam')

parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')

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: --class 0, or --class 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('--update', action='store_true', help='update all models')

parser.add_argument('--project', default='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')

opt = parser.parse_args()