def make_transformed_samples(dataset, args):
'''
Given a dataset, it picks a random sample from it and returns a batch
of (kGeneratedExamplesPerImage+1) samples. The batch contains true sample
from dataset and kGeneratedExamplesPerImage samples, which are created
artifically with augmentation by GOTURN smooth motion model.
'''
idx = np.random.randint(dataset.len, size=1)[0]
# unscaled original sample (single image and bb)
orig_sample = dataset.get_orig_sample(idx)
# cropped scaled sample (two frames and bb)
true_sample, _ = dataset.get_sample(idx)
true_tensor = transform(true_sample)
x1_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 3, input_size,
input_size)
x2_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 3, input_size,
input_size)
x1_batch_x2 = torch.Tensor(kGeneratedExamplesPerImage + 1, 3,
input_size * 2, input_size * 2)
x2_batch_x2 = torch.Tensor(kGeneratedExamplesPerImage + 1, 3,
input_size * 2, input_size * 2)
y_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 4)
# initialize batch with the true sample
x1_batch[0] = true_tensor['previmg']
x2_batch[0] = true_tensor['currimg']
x1_batch_x2[0] = true_tensor['previmg_x2']
x2_batch_x2[0] = true_tensor['currimg_x2']
y_batch[0] = true_tensor['currbb']
scale = Rescale((input_size, input_size))
for i in range(kGeneratedExamplesPerImage):
sample = orig_sample
# unscaled current image crop with box
curr_sample, opts_curr = shift_crop_training_sample(sample, bb_params)
# unscaled previous image crop with box
prev_sample, opts_prev = crop_sample(sample)
prev_sample_x2, opts_prev_x2 = crop_sample(sample, contextFactor=4)
prev_sample['image_x2'] = prev_sample_x2['image']
scaled_curr_obj = scale(curr_sample, opts_curr)
scaled_prev_obj = scale(prev_sample, opts_prev)
training_sample = {
'previmg': scaled_prev_obj['image'],
'currimg': scaled_curr_obj['image'],
'previmg_x2': scaled_prev_obj['image_x2'],
'currimg_x2': scaled_curr_obj['image_x2'],
'currbb': scaled_curr_obj['bb']
}
sample = transform(training_sample)
x1_batch[i + 1] = sample['previmg']
x2_batch[i + 1] = sample['currimg']
x1_batch_x2[i + 1] = sample['previmg_x2']
x2_batch_x2[i + 1] = sample['currimg_x2']
y_batch[i + 1] = sample['currbb']
return x1_batch, x2_batch, x1_batch_x2, x2_batch_x2, y_batch
def make_transformed_samples(dataset, args):
idx = np.random.randint(dataset.len, size=1)[0]
# unscaled original sample (single image and bb)
orig_sample = dataset.get_orig_sample(idx)
# cropped scaled sample (two frames and bb)
true_sample, _ = dataset.get_sample(idx)
true_tensor = transform(true_sample)
x1_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 3, input_size,
input_size)
x2_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 3, input_size,
input_size)
y_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 4)
# initialize batch with the true sample
x1_batch[0, :, :, :] = true_tensor['previmg']
x2_batch[0, :, :, :] = true_tensor['currimg']
y_batch[0, :] = true_tensor['currbb']
scale = Rescale((input_size, input_size))
for i in range(kGeneratedExamplesPerImage):
sample = orig_sample
# unscaled current image crop with box
curr_sample, opts_curr = shift_crop_training_sample(sample, bb_params)
# unscaled previous image crop with box
prev_sample, opts_prev = crop_sample(sample)
scaled_curr_obj = scale(curr_sample, opts_curr)
scaled_prev_obj = scale(prev_sample, opts_prev)
training_sample = {
'previmg': scaled_prev_obj['image'],
'currimg': scaled_curr_obj['image'],
'currbb': scaled_curr_obj['bb']
}
sample = transform(training_sample)
x1_batch[i + 1, :, :, :] = sample['previmg']
x2_batch[i + 1, :, :, :] = sample['currimg']
y_batch[i + 1, :] = sample['currbb']
return x1_batch, x2_batch, y_batch
def get_sample(self, idx):
"""
Returns sample without transformation for visualization.
Sample consists of resized previous and current frame with target
which is passed to the network. Bounding box values are normalized
between 0 and 1 with respect to the target frame and then scaled by
factor of 10.
"""
sample = self.get_orig_sample(idx)
# unscaled current image crop with box
curr_sample, opts_curr = shift_crop_training_sample(
sample, self.bb_params)
# unscaled previous image crop with box
prev_sample, opts_prev = crop_sample(sample)
scale = Rescale((self.sz, self.sz))
scaled_curr_obj = scale(curr_sample, opts_curr)
scaled_prev_obj = scale(prev_sample, opts_prev)
training_sample = {
'previmg': scaled_prev_obj['image'],
'currimg': scaled_curr_obj['image'],
'currbb': scaled_curr_obj['bb']
}
return training_sample, opts_curr