def step(i, probs, counts, images):
# Sample image patch
H = sample_homography(shape, **config['homographies'])
H_inv = invert_homography(H)
warped = H_transform(image, H, interpolation='BILINEAR')
count = H_transform(tf.expand_dims(tf.ones(tf.shape(image)[:3]), -1),
H_inv,
interpolation='NEAREST')[..., 0]
# Predict detection probabilities
warped_shape = tf.to_int32(
tf.to_float(shape) * config['homographies']['patch_ratio'])
input_warped = tf.image.resize_images(warped, warped_shape)
prob = net(input_warped)['prob']
prob = tf.image.resize_images(tf.expand_dims(prob, axis=-1),
shape)[..., 0]
prob_proj = H_transform(tf.expand_dims(prob, -1),
H_inv,
interpolation='BILINEAR')[..., 0]
probs = tf.concat([probs, tf.expand_dims(prob_proj, -1)], axis=-1)
counts = tf.concat([counts, tf.expand_dims(count, -1)], axis=-1)
images = tf.concat([images, tf.expand_dims(warped, -1)], axis=-1)
return i + 1, probs, counts, images
def compute_valid_mask(image_shape, homography, erosion_radius=0):
"""
Compute a boolean mask of the valid pixels resulting from an homography applied to
an image of a given shape. Pixels that are False correspond to bordering artifacts.
A margin can be discarded using erosion.
Arguments:
input_shape: Tensor of rank 2 representing the image shape, i.e. `[H, W]`.
homography: Tensor of shape (B, 8) or (8,), where B is the batch size.
erosion_radius: radius of the margin to be discarded.
Returns: a Tensor of type `tf.int32` and shape (H, W).
"""
mask = H_transform(tf.ones(image_shape), homography, interpolation='NEAREST')
if erosion_radius > 0:
kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (erosion_radius*2,)*2)
mask = tf.nn.erosion2d(
mask[tf.newaxis, ..., tf.newaxis],
tf.to_float(tf.constant(kernel)[..., tf.newaxis]),
[1, 1, 1, 1], [1, 1, 1, 1], 'SAME')[0, ..., 0] + 1.
return tf.to_int32(mask)
def step(i, probs, counts, images):
#Sample image patch
H = sample_homography(shape, **config['homographies'])
H_inv = invert_homography(H)
#############################################
H_ = shape[0]
W = shape[1]
row_c = tf.random_uniform(shape=[],
minval=0,
maxval=tf.cast(H_, tf.float32),
dtype=tf.float32)
col_c = tf.random_uniform(shape=[],
minval=0,
maxval=tf.cast(W, tf.float32),
dtype=tf.float32)
lambda_ = tf.constant(0.000006)
#############################################
#apply the homography
warped = H_transform(image, H, interpolation='BILINEAR')
#############################################
#apply the radial distortion
warped = distort(warped, lambda_, (row_c, col_c))
#count = warp_points_dist(tf.expand_dims(tf.ones(tf.shape(image)[:3]),-1), lambda_, (row_c,col_c), inverse=True)
count = undistort(tf.expand_dims(tf.ones(tf.shape(image)[:3]), -1),
lambda_, (row_c, col_c))
#count = tf.round(count)
count = H_transform(count, H_inv, interpolation='NEAREST')
mask = H_transform(tf.expand_dims(tf.ones(tf.shape(image)[:3]), -1),
H,
interpolation='NEAREST')
mask = distort(mask, lambda_, (row_c, col_c))
#############################################
# Ignore the detections too close to the border to avoid artifacts
if config['valid_border_margin']:
kernel = cv.getStructuringElement(
cv.MORPH_ELLIPSE, (config['valid_border_margin'] * 2, ) * 2)
with tf.device('/cpu:0'):
count = tf.nn.erosion2d(
count, tf.to_float(tf.constant(kernel)[..., tf.newaxis]),
[1, 1, 1, 1], [1, 1, 1, 1], 'SAME')[..., 0] + 1.
mask = tf.nn.erosion2d(
mask, tf.to_float(tf.constant(kernel)[..., tf.newaxis]),
[1, 1, 1, 1], [1, 1, 1, 1], 'SAME')[..., 0] + 1.
# Predict detection probabilities
prob = net(warped)['prob']
prob = prob * mask
prob_proj = undistort(tf.expand_dims(prob, -1), lambda_,
(row_c, col_c))
prob_proj = H_transform(prob_proj, H_inv,
interpolation='BILINEAR')[..., 0]
prob_proj = prob_proj * count
probs = tf.concat([probs, tf.expand_dims(prob_proj, -1)], axis=-1)
counts = tf.concat([counts, tf.expand_dims(count, -1)], axis=-1)
images = tf.concat([images, tf.expand_dims(warped, -1)], axis=-1)
return i + 1, probs, counts, images
