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Add random interpolation method augmentation (#6826) 

* add random_interpolation option to make model robust to interpolation methods

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fix precommit error

* Update augmentations.py

* Update augmentations.py

* Update augmentations.py

* Update datasets.py

* Update datasets.py

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Yonghye Kwon 2022-05-13 21:26:11 +09:00 committed by GitHub
parent 5e077bfd39
commit 5a1ef32553
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1 changed files with 3 additions and 2 deletions
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utils/datasets.py
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@ -397,6 +397,7 @@ def img2label_paths(img_paths):
class LoadImagesAndLabels(Dataset): class LoadImagesAndLabels(Dataset):
# YOLOv5 train_loader/val_loader, loads images and labels for training and validation # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
cache_version = 0.6 # dataset labels *.cache version cache_version = 0.6 # dataset labels *.cache version
rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
def __init__(self, def __init__(self,
path, path,
@ -665,8 +666,8 @@ class LoadImagesAndLabels(Dataset):
h0, w0 = im.shape[:2] # orig hw h0, w0 = im.shape[:2] # orig hw
r = self.img_size / max(h0, w0) # ratio r = self.img_size / max(h0, w0) # ratio
if r != 1: # if sizes are not equal if r != 1: # if sizes are not equal
im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interp = cv2.INTER_LINEAR if self.augment else cv2.INTER_AREA # random.choice(self.rand_interp_methods)
interpolation=cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA) im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp)
return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized
else: else:
return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized