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Centernet dataaug #36

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6769348
data augmentation start
patel996 Apr 1, 2021
f6d3a9b
added augmentations to data pipeline
Apr 2, 2021
836e8d0
added data augmentations to centernet_input
Apr 7, 2021
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97 changes: 88 additions & 9 deletions centernet/dataloaders/centernet_input.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,8 @@
import tensorflow as tf
from official.vision.beta.dataloaders import parser

from centernet.ops import preprocessing_ops
from official.vision.beta.ops import box_ops, preprocess_ops
from centernet.ops import preprocessing_ops as ops
from yolo.ops import preprocessing_ops

class CenterNetParser(parser.Parser):
def __init__(
Expand All @@ -10,12 +11,22 @@ def __init__(
max_num_instances: int,
gaussian_iou: float,

aug_rand_saturation=True,
aug_rand_brightness=True,
aug_rand_zoom=True,
aug_rand_hue=True,
seed=1,
):
self._num_classes = num_classes
self._max_num_instances = max_num_instances
self._gaussian_iou = gaussian_iou
self._gaussian_bump = True
self._gaussian_rad = -1
self._aug_rand_zoom = aug_rand_zoom
# self._mosaic_frequency
# self._jitter_im
self._seed = seed
# self._random_flip

def _generate_heatmap(self, boxes, output_size, input_size):
boxes = tf.cast(boxes, dtype=tf.float32)
Expand Down Expand Up @@ -68,17 +79,17 @@ def _generate_heatmap(self, boxes, output_size, input_size):
height = tf.math.ceil(height * height_ratio)

if self._gaussian_rad == -1:
radius = preprocessing_ops.gaussian_radius((height, width), self._gaussian_iou)
radius = ops.gaussian_radius((height, width), self._gaussian_iou)
radius = tf.math.maximum(0, tf.math.floor(radius))
else:
radius = self._gaussian_rad

# test
# tl_heatmaps = preprocessing_ops.draw_gaussian(tl_heatmaps[category], category, [xtl, ytl], radius)
# inputs heatmap, center, radius, k=1
tl_heatmaps = preprocessing_ops.draw_gaussian(tl_heatmaps, [[category, xtl, ytl, radius]])
br_heatmaps = preprocessing_ops.draw_gaussian(br_heatmaps, [[category, xbr, ybr, radius]])
ct_heatmaps = preprocessing_ops.draw_gaussian(ct_heatmaps, [[category, xct, yct, radius]], scaling_factor=5)
tl_heatmaps = ops.draw_gaussian(tl_heatmaps, [[category, xtl, ytl, radius]])
br_heatmaps = ops.draw_gaussian(br_heatmaps, [[category, xbr, ybr, radius]])
ct_heatmaps = ops.draw_gaussian(ct_heatmaps, [[category, xct, yct, radius]], scaling_factor=5)

else:
# TODO: See if this is a typo
Expand Down Expand Up @@ -126,12 +137,78 @@ def _parse_train_data(self, decoded_tensors):
images: the image tensor.
labels: a dict of Tensors that contains labels.
"""
print('running _parse_train_data')
# TODO: input size, output size
image = decoded_tensors["image"]
image = decoded_tensors["image"] / 255

labels = self._generate_heatmap(
decoded_tensors["groundtruth_boxes"],
output_size, input_size
)
boxes = decoded_tensors['groundtruth_boxes']
classes = decoded_tensors['groundtruth_classes']

image_shape = tf.shape(image)[:2]

print('image_shape: ', image_shape)
#CROP
# if self._aug_rand_zoom > 0.0 and self._mosaic_frequency > 0.0:
# zfactor = preprocessing_ops.rand_uniform_strong(self._aug_rand_zoom, 1.0)
if self._aug_rand_zoom > 0.0:
zfactor = preprocessing_ops.rand_scale(self._aug_rand_zoom)
else:
zfactor = tf.convert_to_tensor(1.0)

# # TODO: random_op_image not defined
# image, crop_info = preprocessing_ops.random_op_image(
# image, self._jitter_im, zfactor, zfactor, self._aug_rand_translate)

image = tf.image.stateless_random_crop(image, size=[image_shape[0]*zfactor, image_shape[1]*zfactor, 3], seed = seed)

#RESIZE
shape = tf.shape(image)
width = shape[1]
height = shape[0]
image, boxes, classes = preprocessing_ops.resize_crop_filter(
image,
boxes,
classes,
default_width=width, # randscale * self._net_down_scale,
default_height=height, # randscale * self._net_down_scale,
target_width=self._image_w,
target_height=self._image_h,
randomize=False)

#CLIP DETECTION TO BOUNDARIES
boxes = box_ops.clip_boxes(boxes, shape)

#RANDOM HORIZONTAL FLIP
if self._random_flip:
image, boxes, _ = preprocess_ops.random_horizontal_flip(
image, boxes, seed=self._seed)

# Color and lighting jittering
image = tf.image.rgb_to_hsv(image)
i_h, i_s, i_v = tf.split(image, 3, axis=-1)
if self._aug_rand_hue:
delta = preprocessing_ops.rand_uniform_strong(
-0.1, 0.1
) # tf.random.uniform([], minval= -0.1,maxval=0.1, seed=self._seed, dtype=tf.float32)
i_h = i_h + delta # Hue
i_h = tf.clip_by_value(i_h, 0.0, 1.0)
if self._aug_rand_saturation:
delta = preprocessing_ops.rand_scale(
0.75
) # tf.random.uniform([], minval= 0.5,maxval=1.1, seed=self._seed, dtype=tf.float32)
i_s = i_s * delta
if self._aug_rand_brightness:
delta = preprocessing_ops.rand_scale(
0.75
) # tf.random.uniform([], minval= -0.15,maxval=0.15, seed=self._seed, dtype=tf.float32)
i_v = i_v * delta
image = tf.concat([i_h, i_s, i_v], axis=-1)
image = tf.image.hsv_to_rgb(image)

return image, labels

def _parse_eval_data(self, data):
Expand Down Expand Up @@ -170,7 +247,9 @@ def generate_heatmaps(self, dectections):

# tl_heatmaps, br_heatmaps, ct_heatmaps = generate_heatmaps(1, 2, (416, 416), detections)
# ct_heatmaps[batch_id, class_id, ...]
plt.imshow(ct_heatmaps[0, ...])
plt.show()

# plt.imshow(ct_heatmaps[0, ...])
# plt.show()

# This is to run the test
# tf.test.main()
8 changes: 4 additions & 4 deletions centernet/ops/preprocessing_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,9 +149,9 @@ def draw_gaussian(heatmap, blobs, scaling_factor=1, dtype=tf.float32):
left, right = tf.math.minimum(x, radius), tf.math.minimum(width - x, radius + 1)
top, bottom = tf.math.minimum(y, radius), tf.math.minimum(height - y, radius + 1)

print('heatmap ',heatmap)
print(len(heatmap))
print('category ',category)
# print('heatmap ',heatmap)
# print(len(heatmap))
# print('category ',category)

# TODO: make sure this replicates original functionality
# masked_heatmap = heatmap[0, category, y - top:y + bottom, x - left:x + right]
Expand Down Expand Up @@ -180,7 +180,7 @@ def draw_gaussian(heatmap, blobs, scaling_factor=1, dtype=tf.float32):
heatmap_mask = heatmap_mask_ta.stack()
heatmap_mask = tf.reshape(heatmap_mask, (-1, 3))
heatmap = tf.tensor_scatter_nd_max(heatmap, heatmap_mask, masked_gaussian * scaling_factor)
print('after ',heatmap)
# print('after ',heatmap)
return heatmap

# def draw_gaussian(heatmap, category, center, radius, scaling_factor=1):
Expand Down