Questo è quello che sto usando in questo momento:
# Assumption: img is a tensor of the size [img_width, img_height, 3], normalized to the range [-1, 1].
with tf.variable_scope('color_hist_producer') as scope:
bin_size = 0.2
hist_entries = []
# Split image into single channels
img_r, img_g, img_b = tf.split(2, 3, img)
for img_chan in [img_r, img_g, img_b]:
for idx, i in enumerate(np.arange(-1, 1, bin_size)):
gt = tf.greater(img_chan, i)
leq = tf.less_equal(img_chan, i + bin_size)
# Put together with logical_and, cast to float and sum up entries -> gives count for current bin.
hist_entries.append(tf.reduce_sum(tf.cast(tf.logical_and(gt, leq), tf.float32)))
# Pack scalars together to a tensor, then normalize histogram.
hist = tf.nn.l2_normalize(tf.pack(hist_entries), 0)
Vuoi utilizzare l'istogramma nella computazione dopo TF, o è l'output l'istogramma della obiettivo finale? – dga
@dga Lo sto usando in un computo TF. – panmari