augmentations for 16bit grayscaled
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@ -163,7 +163,7 @@ class Mosaic(BaseMixTransform):
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# Place img in img4
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if i == 0: # top left
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img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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img4 = np.full((s * 2, s * 2, 1), 114, dtype=np.float32) # base image with 4 tiles
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x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
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x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
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elif i == 1: # top right
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@ -176,7 +176,7 @@ class Mosaic(BaseMixTransform):
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x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
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x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
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img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
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img4[y1a:y2a, x1a:x2a, 0] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
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padw = x1a - x1b
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padh = y1a - y1b
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@ -199,7 +199,7 @@ class Mosaic(BaseMixTransform):
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# Place img in img9
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if i == 0: # center
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img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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img9 = np.full((s * 3, s * 3, 1), 114, dtype=np.float32) # base image with 4 tiles
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h0, w0 = h, w
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c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
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elif i == 1: # top
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@ -223,7 +223,7 @@ class Mosaic(BaseMixTransform):
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x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords
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# Image
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img9[y1:y2, x1:x2] = img[y1 - padh:, x1 - padw:] # img9[ymin:ymax, xmin:xmax]
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img9[y1:y2, x1:x2, 0] = img[y1 - padh:, x1 - padw:] # img9[ymin:ymax, xmin:xmax]
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hp, wp = h, w # height, width previous for next iteration
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# Labels assuming imgsz*2 mosaic size
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@ -485,6 +485,7 @@ class RandomHSV:
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def __call__(self, labels):
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"""Applies random horizontal or vertical flip to an image with a given probability."""
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img = labels['img']
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return labels # TODO:
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if self.hgain or self.sgain or self.vgain:
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r = np.random.uniform(-1, 1, 3) * [self.hgain, self.sgain, self.vgain] + 1 # random gains
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hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
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@ -712,7 +713,7 @@ class Format:
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def __call__(self, labels):
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"""Return formatted image, classes, bounding boxes & keypoints to be used by 'collate_fn'."""
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img = labels.pop('img')
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h, w = img.shape[:2]
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h, w = img.shape[0], img.shape[1]
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cls = labels.pop('cls')
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instances = labels.pop('instances')
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instances.convert_bbox(format=self.bbox_format)
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@ -906,5 +907,5 @@ class ToTensor:
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im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
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im = torch.from_numpy(im) # to torch
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im = im.half() if self.half else im.float() # uint8 to fp16/32
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im /= 255.0 # 0-255 to 0.0-1.0
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im /= 65535
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return im
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@ -145,7 +145,6 @@ class YOLODataset(BaseDataset):
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# TODO: use hyp config to set all these augmentations
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def build_transforms(self, hyp=None):
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"""Builds and appends transforms to the list."""
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self.augment = False
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if self.augment:
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hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
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hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
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@ -53,7 +53,7 @@ class DetectionTrainer(BaseTrainer):
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def preprocess_batch(self, batch):
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"""Preprocesses a batch of images by scaling and converting to float."""
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batch['img'] = batch['img'].to(self.device, non_blocking=True).float() / 255
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batch['img'] = batch['img'].to(self.device, non_blocking=True).float() / 65535.0 # uint16 to float16
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return batch
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def set_model_attributes(self):
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@ -44,7 +44,7 @@ class DetectionValidator(BaseValidator):
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def preprocess(self, batch):
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"""Preprocesses batch of images for YOLO training."""
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batch['img'] = batch['img'].to(self.device, non_blocking=True)
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batch['img'] = (batch['img'].half() if self.args.half else batch['img'].float()) / 255
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batch['img'] = (batch['img'].half() if self.args.half else batch['img'].float()) / 65535.0 # uint16 to float16
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for k in ['batch_idx', 'cls', 'bboxes']:
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batch[k] = batch[k].to(self.device)
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