411 lines
13 KiB
Python
Executable File
411 lines
13 KiB
Python
Executable File
# from https://github.com/amdegroot/ssd.pytorch
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import torch
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from torchvision import transforms
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import cv2
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import numpy as np
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import types
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from numpy import random
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def intersect(box_a, box_b):
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max_xy = np.minimum(box_a[:, 2:], box_b[2:])
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min_xy = np.maximum(box_a[:, :2], box_b[:2])
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inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf)
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return inter[:, 0] * inter[:, 1]
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def jaccard_numpy(box_a, box_b):
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"""Compute the jaccard overlap of two sets of boxes. The jaccard overlap
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is simply the intersection over union of two boxes.
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E.g.:
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A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
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Args:
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box_a: Multiple bounding boxes, Shape: [num_boxes,4]
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box_b: Single bounding box, Shape: [4]
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Return:
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jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]]
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"""
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inter = intersect(box_a, box_b)
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area_a = ((box_a[:, 2] - box_a[:, 0]) *
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(box_a[:, 3] - box_a[:, 1])) # [A,B]
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area_b = ((box_b[2] - box_b[0]) *
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(box_b[3] - box_b[1])) # [A,B]
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union = area_a + area_b - inter
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return inter / union # [A,B]
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class Compose(object):
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"""Composes several augmentations together.
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Args:
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transforms (List[Transform]): list of transforms to compose.
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Example:
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>>> augmentations.Compose([
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>>> transforms.CenterCrop(10),
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>>> transforms.ToTensor(),
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>>> ])
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"""
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def __init__(self, transforms):
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self.transforms = transforms
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def __call__(self, img, boxes=None, labels=None):
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for t in self.transforms:
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img, boxes, labels = t(img, boxes, labels)
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return img, boxes, labels
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class Lambda(object):
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"""Applies a lambda as a transform."""
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def __init__(self, lambd):
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assert isinstance(lambd, types.LambdaType)
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self.lambd = lambd
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def __call__(self, img, boxes=None, labels=None):
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return self.lambd(img, boxes, labels)
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class ConvertFromInts(object):
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def __call__(self, image, boxes=None, labels=None):
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return image.astype(np.float32), boxes, labels
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class SubtractMeans(object):
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def __init__(self, mean):
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self.mean = np.array(mean, dtype=np.float32)
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def __call__(self, image, boxes=None, labels=None):
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image = image.astype(np.float32)
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image -= self.mean
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return image.astype(np.float32), boxes, labels
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class ToAbsoluteCoords(object):
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def __call__(self, image, boxes=None, labels=None):
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height, width, channels = image.shape
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boxes[:, 0] *= width
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boxes[:, 2] *= width
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boxes[:, 1] *= height
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boxes[:, 3] *= height
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return image, boxes, labels
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class ToPercentCoords(object):
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def __call__(self, image, boxes=None, labels=None):
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height, width, channels = image.shape
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boxes[:, 0] /= width
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boxes[:, 2] /= width
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boxes[:, 1] /= height
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boxes[:, 3] /= height
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return image, boxes, labels
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class Resize(object):
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def __init__(self, size=300):
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self.size = size
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def __call__(self, image, boxes=None, labels=None):
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image = cv2.resize(image, (self.size,
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self.size))
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return image, boxes, labels
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class RandomSaturation(object):
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def __init__(self, lower=0.5, upper=1.5):
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self.lower = lower
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self.upper = upper
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assert self.upper >= self.lower, "contrast upper must be >= lower."
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assert self.lower >= 0, "contrast lower must be non-negative."
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def __call__(self, image, boxes=None, labels=None):
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if random.randint(2):
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image[:, :, 1] *= random.uniform(self.lower, self.upper)
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return image, boxes, labels
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class RandomHue(object):
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def __init__(self, delta=18.0):
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assert delta >= 0.0 and delta <= 360.0
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self.delta = delta
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def __call__(self, image, boxes=None, labels=None):
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if random.randint(2):
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image[:, :, 0] += random.uniform(-self.delta, self.delta)
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image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
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image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
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return image, boxes, labels
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class RandomLightingNoise(object):
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def __init__(self):
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self.perms = ((0, 1, 2), (0, 2, 1),
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(1, 0, 2), (1, 2, 0),
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(2, 0, 1), (2, 1, 0))
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def __call__(self, image, boxes=None, labels=None):
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if random.randint(2):
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swap = self.perms[random.randint(len(self.perms))]
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shuffle = SwapChannels(swap) # shuffle channels
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image = shuffle(image)
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return image, boxes, labels
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class ConvertColor(object):
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def __init__(self, current, transform):
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self.transform = transform
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self.current = current
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def __call__(self, image, boxes=None, labels=None):
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if self.current == 'BGR' and self.transform == 'HSV':
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image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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elif self.current == 'RGB' and self.transform == 'HSV':
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image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
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elif self.current == 'BGR' and self.transform == 'RGB':
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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elif self.current == 'HSV' and self.transform == 'BGR':
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image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
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elif self.current == 'HSV' and self.transform == "RGB":
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image = cv2.cvtColor(image, cv2.COLOR_HSV2RGB)
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else:
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raise NotImplementedError
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return image, boxes, labels
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class RandomContrast(object):
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def __init__(self, lower=0.5, upper=1.5):
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self.lower = lower
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self.upper = upper
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assert self.upper >= self.lower, "contrast upper must be >= lower."
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assert self.lower >= 0, "contrast lower must be non-negative."
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# expects float image
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def __call__(self, image, boxes=None, labels=None):
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if random.randint(2):
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alpha = random.uniform(self.lower, self.upper)
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image *= alpha
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return image, boxes, labels
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class RandomBrightness(object):
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def __init__(self, delta=32):
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assert delta >= 0.0
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assert delta <= 255.0
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self.delta = delta
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def __call__(self, image, boxes=None, labels=None):
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if random.randint(2):
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delta = random.uniform(-self.delta, self.delta)
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image += delta
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return image, boxes, labels
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class ToCV2Image(object):
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def __call__(self, tensor, boxes=None, labels=None):
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return tensor.cpu().numpy().astype(np.float32).transpose((1, 2, 0)), boxes, labels
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class ToTensor(object):
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def __call__(self, cvimage, boxes=None, labels=None):
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return torch.from_numpy(cvimage.astype(np.float32)).permute(2, 0, 1), boxes, labels
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class RandomSampleCrop(object):
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"""Crop
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Arguments:
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img (Image): the image being input during training
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boxes (Tensor): the original bounding boxes in pt form
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labels (Tensor): the class labels for each bbox
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mode (float tuple): the min and max jaccard overlaps
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Return:
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(img, boxes, classes)
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img (Image): the cropped image
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boxes (Tensor): the adjusted bounding boxes in pt form
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labels (Tensor): the class labels for each bbox
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"""
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def __init__(self):
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self.sample_options = (
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# using entire original input image
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None,
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# sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9
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(0.1, None),
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(0.3, None),
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(0.7, None),
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(0.9, None),
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# randomly sample a patch
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(None, None),
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)
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def __call__(self, image, boxes=None, labels=None):
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# guard against no boxes
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if boxes is not None and boxes.shape[0] == 0:
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return image, boxes, labels
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height, width, _ = image.shape
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while True:
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# randomly choose a mode
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mode = random.choice(self.sample_options)
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if mode is None:
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return image, boxes, labels
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min_iou, max_iou = mode
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if min_iou is None:
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min_iou = float('-inf')
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if max_iou is None:
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max_iou = float('inf')
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# max trails (50)
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for _ in range(50):
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current_image = image
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w = random.uniform(0.3 * width, width)
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h = random.uniform(0.3 * height, height)
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# aspect ratio constraint b/t .5 & 2
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if h / w < 0.5 or h / w > 2:
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continue
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left = random.uniform(width - w)
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top = random.uniform(height - h)
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# convert to integer rect x1,y1,x2,y2
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rect = np.array([int(left), int(top), int(left + w), int(top + h)])
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# calculate IoU (jaccard overlap) b/t the cropped and gt boxes
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overlap = jaccard_numpy(boxes, rect)
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# is min and max overlap constraint satisfied? if not try again
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if overlap.max() < min_iou or overlap.min() > max_iou:
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continue
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# cut the crop from the image
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current_image = current_image[rect[1]:rect[3], rect[0]:rect[2],
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:]
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# keep overlap with gt box IF center in sampled patch
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centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0
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# mask in all gt boxes that above and to the left of centers
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m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
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# mask in all gt boxes that under and to the right of centers
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m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])
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# mask in that both m1 and m2 are true
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mask = m1 * m2
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# have any valid boxes? try again if not
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if not mask.any():
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continue
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# take only matching gt boxes
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current_boxes = boxes[mask, :].copy()
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# take only matching gt labels
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current_labels = labels[mask]
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# should we use the box left and top corner or the crop's
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current_boxes[:, :2] = np.maximum(current_boxes[:, :2],
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rect[:2])
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# adjust to crop (by substracting crop's left,top)
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current_boxes[:, :2] -= rect[:2]
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current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],
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rect[2:])
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# adjust to crop (by substracting crop's left,top)
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current_boxes[:, 2:] -= rect[:2]
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return current_image, current_boxes, current_labels
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class Expand(object):
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def __init__(self, mean):
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self.mean = mean
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def __call__(self, image, boxes, labels):
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if random.randint(2):
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return image, boxes, labels
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height, width, depth = image.shape
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ratio = random.uniform(1, 4)
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left = random.uniform(0, width * ratio - width)
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top = random.uniform(0, height * ratio - height)
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expand_image = np.zeros(
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(int(height * ratio), int(width * ratio), depth),
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dtype=image.dtype)
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expand_image[:, :, :] = self.mean
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expand_image[int(top):int(top + height),
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int(left):int(left + width)] = image
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image = expand_image
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boxes = boxes.copy()
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boxes[:, :2] += (int(left), int(top))
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boxes[:, 2:] += (int(left), int(top))
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return image, boxes, labels
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class RandomMirror(object):
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def __call__(self, image, boxes, classes):
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_, width, _ = image.shape
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if random.randint(2):
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image = image[:, ::-1]
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boxes = boxes.copy()
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boxes[:, 0::2] = width - boxes[:, 2::-2]
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return image, boxes, classes
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class SwapChannels(object):
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"""Transforms a tensorized image by swapping the channels in the order
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specified in the swap tuple.
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Args:
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swaps (int triple): final order of channels
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eg: (2, 1, 0)
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"""
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def __init__(self, swaps):
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self.swaps = swaps
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def __call__(self, image):
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"""
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Args:
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image (Tensor): image tensor to be transformed
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Return:
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a tensor with channels swapped according to swap
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"""
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# if torch.is_tensor(image):
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# image = image.data.cpu().numpy()
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# else:
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# image = np.array(image)
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image = image[:, :, self.swaps]
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return image
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class PhotometricDistort(object):
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def __init__(self):
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self.pd = [
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RandomContrast(), # RGB
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ConvertColor(current="RGB", transform='HSV'), # HSV
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RandomSaturation(), # HSV
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RandomHue(), # HSV
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ConvertColor(current='HSV', transform='RGB'), # RGB
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RandomContrast() # RGB
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]
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self.rand_brightness = RandomBrightness()
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self.rand_light_noise = RandomLightingNoise()
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def __call__(self, image, boxes, labels):
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im = image.copy()
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im, boxes, labels = self.rand_brightness(im, boxes, labels)
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if random.randint(2):
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distort = Compose(self.pd[:-1])
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else:
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distort = Compose(self.pd[1:])
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im, boxes, labels = distort(im, boxes, labels)
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return self.rand_light_noise(im, boxes, labels)
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