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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-20 14:04:50 +02:00
parent 44b340321f
commit cb30d60f4e
1 changed files with 12 additions and 12 deletions
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24
utils/datasets.py
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@ -294,8 +294,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
S = img_hsv[:, :, 1].astype(np.float32) # saturation
V = img_hsv[:, :, 2].astype(np.float32) # value
a = (random.random() * 2 - 1) * fraction + 1
b = (random.random() * 2 - 1) * fraction + 1
a = random.uniform(-1, 1) * fraction + 1
b = random.uniform(-1, 1) * fraction + 1
S *= a
V *= b
@ -331,7 +331,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Augment image and labels
if self.augment:
img, labels = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10))
img, labels = random_affine(img, labels, degrees=(-3, 3), translate=(0.05, 0.05), scale=(0.90, 1.10))
nL = len(labels) # number of labels
if nL:
@ -376,7 +376,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
return torch.stack(img, 0), torch.cat(label, 0), path, hw
def letterbox(img, new_shape=416, color=(127.5, 127.5, 127.5), mode='auto'):
def letterbox(img, new_shape=416, color=(128, 128, 128), mode='auto'):
# Resize a rectangular image to a 32 pixel multiple rectangle
# https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
@ -411,7 +411,7 @@ def letterbox(img, new_shape=416, color=(127.5, 127.5, 127.5), mode='auto'):
def random_affine(img, targets=(), degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
borderValue=(127.5, 127.5, 127.5)):
borderValue=(128, 128, 128)):
# 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
@ -423,20 +423,20 @@ def random_affine(img, targets=(), degrees=(-10, 10), translate=(.1, .1), scale=
# Rotation and Scale
R = np.eye(3)
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
s = random.random() * (scale[1] - scale[0]) + scale[0]
a = random.uniform(degrees[0], degrees[1])
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(scale[0], scale[1])
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.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
T[0, 2] = random.uniform(-1, 1) * translate[0] * img.shape[0] + border # x translation (pixels)
T[1, 2] = random.uniform(-1, 1) * translate[1] * img.shape[1] + border # y translation (pixels)
# Shear
S = np.eye(3)
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
S[0, 1] = math.tan(random.uniform(shear[0], shear[1]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(shear[0], shear[1]) * math.pi / 180) # y shear (deg)
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
imw = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA,