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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-12-02 15:26:36 -08:00
parent ebb4d4c884
commit cba3120ca6
1 changed files with 20 additions and 24 deletions
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44
utils/datasets.py
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@ -411,6 +411,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
img_path = self.img_files[index] img_path = self.img_files[index]
label_path = self.label_files[index] label_path = self.label_files[index]
hyp = self.hyp
mosaic = True and self.augment # load 4 images at a time into a mosaic (only during training) mosaic = True and self.augment # load 4 images at a time into a mosaic (only during training)
if mosaic: if mosaic:
# Load mosaic # Load mosaic
@ -444,17 +445,16 @@ class LoadImagesAndLabels(Dataset): # for training/testing
labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1] labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
if self.augment: if self.augment:
# Augment colorspace
augment_hsv(img, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v'])
# Augment imagespace # Augment imagespace
g = 0.0 if mosaic else 1.0 # do not augment mosaics if not mosaic:
hyp = self.hyp img, labels = random_affine(img, labels,
img, labels = random_affine(img, labels, degrees=hyp['degrees'],
degrees=hyp['degrees'] * g, translate=hyp['translate'],
translate=hyp['translate'] * g, scale=hyp['scale'],
scale=hyp['scale'] * g, shear=hyp['shear'])
shear=hyp['shear'] * g)
# Augment colorspace
augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
# Apply cutouts # Apply cutouts
# if random.random() < 0.9: # if random.random() < 0.9:
@ -573,21 +573,18 @@ def load_mosaic(self, index):
labels = np.zeros((0, 5), dtype=np.float32) labels = np.zeros((0, 5), dtype=np.float32)
labels4.append(labels) labels4.append(labels)
# Concat/clip labels
if len(labels4): if len(labels4):
labels4 = np.concatenate(labels4, 0) labels4 = np.concatenate(labels4, 0)
np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])
# hyp = self.hyp # Augment
# img4, labels4 = random_affine(img4, labels4, img4, labels4 = random_affine(img4, labels4,
# degrees=hyp['degrees'], degrees=self.hyp['degrees'],
# translate=hyp['translate'], translate=self.hyp['translate'],
# scale=hyp['scale'], scale=self.hyp['scale'],
# shear=hyp['shear']) shear=self.hyp['shear'],
border=-s // 2) # border to remove
# Center crop
a = s // 2
img4 = img4[a:a + s, a:a + s]
if len(labels4):
labels4[:, 1:] -= a
return img4, labels4 return img4, labels4
@ -626,13 +623,12 @@ def letterbox(img, new_shape=(416, 416), color=(128, 128, 128),
return img, ratio, (dw, dh) return img, ratio, (dw, dh)
def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10): def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, border=0):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4 # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
if targets is None: # targets = [cls, xyxy] if targets is None: # targets = [cls, xyxy]
targets = [] targets = []
border = 0 # width of added border (optional)
height = img.shape[0] + border * 2 height = img.shape[0] + border * 2
width = img.shape[1] + border * 2 width = img.shape[1] + border * 2