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optimize-for-streams-detection #2538

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optimize-for-streams-detection #2538

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13 changes: 11 additions & 2 deletions detect.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@


def detect(save_img=False):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
source, weights, view_img, save_txt, imgsz, skip_step = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.skip_step
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://'))

Expand Down Expand Up @@ -47,7 +47,7 @@ def detect(save_img=False):
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)
dataset = LoadStreams(source, img_size=imgsz, stride=stride, skip_step=skip_step)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz, stride=stride)
Expand All @@ -60,7 +60,15 @@ def detect(save_img=False):
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time()

prev_img = [None] * dataset.count if webcam else [None]
for path, img, im0s, vid_cap in dataset:
# Optimize repeated img for steams
if webcam:
if (prev_img == img).all():
continue
prev_img = img

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
Expand Down Expand Up @@ -149,6 +157,7 @@ def detect(save_img=False):
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='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--skip-step', type=int, default=4, help='default read every 4th frame for streams')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
Expand Down
10 changes: 6 additions & 4 deletions utils/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -258,10 +258,11 @@ def __len__(self):


class LoadStreams: # multiple IP or RTSP cameras
def __init__(self, sources='streams.txt', img_size=640, stride=32):
def __init__(self, sources='streams.txt', img_size=640, stride=32, skip_step=4):
self.mode = 'stream'
self.img_size = img_size
self.stride = stride
self.skip_step = skip_step

if os.path.isfile(sources):
with open(sources, 'r') as f:
Expand All @@ -270,6 +271,7 @@ def __init__(self, sources='streams.txt', img_size=640, stride=32):
sources = [sources]

n = len(sources)
self.count = n
self.imgs = [None] * n
self.sources = [clean_str(x) for x in sources] # clean source names for later
for i, s in enumerate(sources):
Expand All @@ -281,7 +283,7 @@ def __init__(self, sources='streams.txt', img_size=640, stride=32):
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS) % 100
_, self.imgs[i] = cap.read() # guarantee first frame
thread = Thread(target=self.update, args=([i, cap]), daemon=True)
thread = Thread(target=self.update, args=([i, cap, self.skip_step]), daemon=True)
print(f' success ({w}x{h} at {fps:.2f} FPS).')
thread.start()
print('') # newline
Expand All @@ -292,14 +294,14 @@ def __init__(self, sources='streams.txt', img_size=640, stride=32):
if not self.rect:
print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')

def update(self, index, cap):
def update(self, index, cap, skip_step):
# Read next stream frame in a daemon thread
n = 0
while cap.isOpened():
n += 1
# _, self.imgs[index] = cap.read()
cap.grab()
if n == 4: # read every 4th frame
if n == skip_step: # read every {skip_step}th frame
success, im = cap.retrieve()
self.imgs[index] = im if success else self.imgs[index] * 0
n = 0
Expand Down