|
|
|
|
|
|
|
|
self.names = names # class names |
|
|
self.names = names # class names |
|
|
self.xyxy = pred # xyxy pixels |
|
|
self.xyxy = pred # xyxy pixels |
|
|
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels |
|
|
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels |
|
|
gn = [torch.Tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.]) for im in imgs] # normalization gains |
|
|
|
|
|
|
|
|
d = pred[0].device # device |
|
|
|
|
|
gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations |
|
|
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized |
|
|
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized |
|
|
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized |
|
|
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized |
|
|
self.n = len(self.pred) |
|
|
self.n = len(self.pred) |