|
|
@@ -232,10 +232,10 @@ class AutoShape(nn.Module): |
|
|
|
@torch.no_grad() |
|
|
|
def forward(self, imgs, size=640, augment=False, profile=False): |
|
|
|
# Inference from various sources. For height=640, width=1280, RGB images example inputs are: |
|
|
|
# filename: imgs = 'data/images/zidane.jpg' # str or PosixPath |
|
|
|
# file: imgs = 'data/images/zidane.jpg' # str or PosixPath |
|
|
|
# URI: = 'https://ultralytics.com/images/zidane.jpg' |
|
|
|
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) |
|
|
|
# PIL: = Image.open('image.jpg') # HWC x(640,1280,3) |
|
|
|
# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) |
|
|
|
# numpy: = np.zeros((640,1280,3)) # HWC |
|
|
|
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) |
|
|
|
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images |