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872b0a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import numpy as np
import tensorflow as tf
def atan2(y, x):
angle = tf.where(tf.greater(x,0.0), tf.atan(y/x), tf.zeros_like(x))
angle = tf.where(tf.logical_and(tf.less(x,0.0), tf.greater_equal(y,0.0)),
tf.atan(y/x) + np.pi, angle)
angle = tf.where(tf.logical_and(tf.less(x,0.0), tf.less(y,0.0)),
tf.atan(y/x) - np.pi, angle)
angle = tf.where(tf.logical_and(tf.equal(x,0.0), tf.greater(y,0.0)),
np.pi * tf.ones_like(x), angle)
angle = tf.where(tf.logical_and(tf.equal(x,0.0), tf.less(y,0.0)),
-np.pi * tf.ones_like(x), angle)
angle = tf.where(tf.logical_and(tf.equal(x,0.0),tf.equal(y,0.0)),
np.nan * tf.zeros_like(x), angle)
return angle
def flow_to_color(flow, mask=None, max_flow=None):
"""Converts flow to 3-channel color image.
Args:
flow: tensor of shape [num_batch, height, width, 2].
mask: flow validity mask of shape [num_batch, height, width, 1].
"""
n = 8
num_batch, height, width, _ = tf.unstack(tf.shape(flow))
mask = tf.ones([num_batch, height, width, 1]) if mask is None else mask
flow_u, flow_v = tf.unstack(flow, axis=3)
if max_flow is not None:
max_flow = tf.maximum(max_flow, 1)
else:
max_flow = tf.reduce_max(tf.abs(flow * mask))
mag = tf.sqrt(tf.reduce_sum(tf.square(flow), 3))
angle = atan2(flow_v, flow_u)
im_h = tf.mod(angle / (2 * np.pi) + 1.0, 1.0)
im_s = tf.clip_by_value(mag * n / max_flow, 0, 1)
im_v = tf.clip_by_value(n - im_s, 0, 1)
im_hsv = tf.stack([im_h, im_s, im_v], 3)
im = tf.image.hsv_to_rgb(im_hsv)
return im * mask
def flow_error_image(flow_1, flow_2, mask_occ, mask_noc=None, log_colors=True):
"""Visualize the error between two flows as 3-channel color image.
Adapted from the KITTI C++ devkit.
Args:
flow_1: first flow of shape [num_batch, height, width, 2].
flow_2: second flow (ground truth)
mask_occ: flow validity mask of shape [num_batch, height, width, 1].
Equals 1 at (occluded and non-occluded) valid pixels.
mask_noc: Is 1 only at valid pixels which are not occluded.
"""
mask_noc = tf.ones(tf.shape(mask_occ)) if mask_noc is None else mask_noc
diff_sq = (flow_1 - flow_2) ** 2
diff = tf.sqrt(tf.reduce_sum(diff_sq, [3], keepdims=True))
if log_colors:
num_batch, height, width, _ = tf.unstack(tf.shape(flow_1))
colormap = [
[0,0.0625,49,54,149],
[0.0625,0.125,69,117,180],
[0.125,0.25,116,173,209],
[0.25,0.5,171,217,233],
[0.5,1,224,243,248],
[1,2,254,224,144],
[2,4,253,174,97],
[4,8,244,109,67],
[8,16,215,48,39],
[16,1000000000.0,165,0,38]]
colormap = np.asarray(colormap, dtype=np.float32)
colormap[:, 2:5] = colormap[:, 2:5] / 255
mag = tf.sqrt(tf.reduce_sum(tf.square(flow_2), 3, keepdims=True))
error = tf.minimum(diff / 3, 20 * diff / mag)
im = tf.zeros([num_batch, height, width, 3])
for i in range(colormap.shape[0]):
colors = colormap[i, :]
cond = tf.logical_and(tf.greater_equal(error, colors[0]),
tf.less(error, colors[1]))
im = tf.where(tf.tile(cond, [1, 1, 1, 3]),
tf.ones([num_batch, height, width, 1]) * colors[2:5],
im)
im = tf.where(tf.tile(tf.cast(mask_noc, tf.bool), [1, 1, 1, 3]),
im, im * 0.5)
im = im * mask_occ
else:
error = (tf.minimum(diff, 5) / 5) * mask_occ
im_r = error # errors in occluded areas will be red
im_g = error * mask_noc
im_b = error * mask_noc
im = tf.concat(axis=3, values=[im_r, im_g, im_b])
return im
def flow_error_avg(flow_1, flow_2, mask):
"""Evaluates the average endpoint error between flow batches."""
with tf.variable_scope('flow_error_avg'):
diff = euclidean(flow_1 - flow_2) * mask
error = tf.reduce_sum(diff) / tf.reduce_sum(mask)
return error
def outlier_ratio(gt_flow, flow, mask, threshold=3.0, relative=0.05):
diff = euclidean(gt_flow - flow) * mask
if relative is not None:
threshold = tf.maximum(threshold, euclidean(gt_flow) * relative)
outliers = tf.cast(tf.greater_equal(diff, threshold), tf.float32)
else:
outliers = tf.cast(tf.greater_equal(diff, threshold), tf.float32)
ratio = tf.reduce_sum(outliers) / tf.reduce_sum(mask)
return ratio
def outlier_pct(gt_flow, flow, mask, threshold=3.0, relative=0.05):
frac = outlier_ratio(gt_flow, flow, mask, threshold, relative) * 100
return frac
def euclidean(t):
return tf.sqrt(tf.reduce_sum(t ** 2, [3], keepdims=True))
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