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@ -38,7 +38,7 @@ from utils.torch_utils import select_device, time_sync
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@torch.no_grad()
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def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
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imgsz=640, # inference size (pixels)
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imgsz=(640, 640), # inference size (height, width)
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conf_thres=0.25, # confidence threshold
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iou_thres=0.45, # NMS IOU threshold
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max_det=1000, # maximum detections per image
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@ -139,5 +139,6 @@ if __name__ == '__main__':
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np.zeros((320, 640, 3))] # numpy
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results = model(imgs) # batched inference
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results.print()
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results.save()
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rl = results.tolist()
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print(rl[0].pandas().xyxy[0])
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@ -525,7 +525,7 @@ class AutoShape(nn.Module):
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class Detections:
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# YOLOv5 detections class for inference results
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def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
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def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None):
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super().__init__()
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d = pred[0].device # device
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gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations
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@ -533,6 +533,7 @@ class Detections:
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self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
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self.names = names # class names
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self.files = files # image filenames
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self.times = times # profiling times
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self.xyxy = pred # xyxy pixels
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self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
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self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
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@ -612,10 +613,11 @@ class Detections:
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def tolist(self):
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# return a list of Detections objects, i.e. 'for result in results.tolist():'
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x = [Detections([self.imgs[i]], [self.pred[i]], names=self.names, shape=self.s) for i in range(self.n)]
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for d in x:
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for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
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setattr(d, k, getattr(d, k)[0]) # pop out of list
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r = range(self.n) # iterable
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x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
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# for d in x:
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# for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
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# setattr(d, k, getattr(d, k)[0]) # pop out of list
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return x
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def __len__(self):
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