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replace random_affine() with random_perspective()

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
pull/1/head
Glenn Jocher 5 years ago
parent bb87276d80
commit bcd452c482
2 changed files with 48 additions and 26 deletions
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2
train.py
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@ -32,7 +32,7 @@ hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
'hsv_s': 0.7, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.4, # image HSV-Value augmentation (fraction)
'degrees': 0.0, # image rotation (+/- deg)
'translate': 0.0, # image translation (+/- fraction)
'translate': 0.5, # image translation (+/- fraction)
'scale': 0.5, # image scale (+/- gain)
'shear': 0.0} # image shear (+/- deg)

72
utils/datasets.py
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@ -485,9 +485,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# MixUp https://arxiv.org/pdf/1710.09412.pdf
# if random.random() < 0.5:
# img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
# img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
# r = np.random.beta(0.3, 0.3) # mixup ratio, alpha=beta=0.3
# img = (img * r + img2 * (1 - r)).astype(np.uint8)
# img = (img * r + img2 * (1 - r)).astype(np.uint8)
# labels = np.concatenate((labels, labels2), 0)
else:
@ -513,11 +513,11 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if self.augment:
# Augment imagespace
if not self.mosaic:
img, labels = random_affine(img, labels,
degrees=hyp['degrees'],
translate=hyp['translate'],
scale=hyp['scale'],
shear=hyp['shear'])
img, labels = random_perspective(img, labels,
degrees=hyp['degrees'],
translate=hyp['translate'],
scale=hyp['scale'],
shear=hyp['shear'])
# Augment colorspace
augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
@ -610,7 +610,7 @@ def load_mosaic(self, index):
labels4 = []
s = self.img_size
yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y
yc, xc = s, s # mosaic center x, y
indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices
for i, index in enumerate(indices):
# Load image
@ -656,12 +656,12 @@ def load_mosaic(self, index):
# Augment
# img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)] # center crop (WARNING, requires box pruning)
img4, labels4 = random_affine(img4, labels4,
degrees=self.hyp['degrees'],
translate=self.hyp['translate'],
scale=self.hyp['scale'],
shear=self.hyp['shear'],
border=self.mosaic_border) # border to remove
img4, labels4 = random_perspective(img4, labels4,
degrees=self.hyp['degrees'],
translate=self.hyp['translate'],
scale=self.hyp['scale'],
shear=self.hyp['shear'],
border=self.mosaic_border) # border to remove
return img4, labels4
@ -716,36 +716,54 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale
return img, ratio, (dw, dh)
def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, border=(0, 0)):
def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
# 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
# targets = [cls, xyxy]
height = img.shape[0] + border[0] * 2 # shape(h,w,c)
width = img.shape[1] + border[1] * 2
# Center
C = np.eye(3)
C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
# Perspective
P = np.eye(3)
P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
# Rotation and Scale
R = np.eye(3)
a = random.uniform(-degrees, degrees)
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(1 - scale, 1 + scale)
# s = 2 ** random.uniform(-scale, scale)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
# Translation
T = np.eye(3)
T[0, 2] = random.uniform(-translate, translate) * img.shape[1] + border[1] # x translation (pixels)
T[1, 2] = random.uniform(-translate, translate) * img.shape[0] + border[0] # y translation (pixels)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
# Shear
S = np.eye(3)
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
# Translation
T = np.eye(3)
T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
# Combined rotation matrix
M = S @ T @ R # ORDER IS IMPORTANT HERE!!
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=(114, 114, 114))
if perspective:
img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
else: # affine
img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
# Visualize
# import matplotlib.pyplot as plt
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
# ax[0].imshow(img[:, :, ::-1]) # base
# ax[1].imshow(img2[:, :, ::-1]) # warped
# Transform label coordinates
n = len(targets)
@ -753,7 +771,11 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
# warp points
xy = np.ones((n * 4, 3))
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = (xy @ M.T)[:, :2].reshape(n, 8)
xy = xy @ M.T # transform
if perspective:
xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale
else: # affine
xy = xy[:, :2].reshape(n, 8)
# create new boxes
x = xy[:, [0, 2, 4, 6]]

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