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import os
import tensorflow as tf
import numpy as np
from scipy.misc import imread
import matplotlib
from src.flowlib import read_flow, flow_to_image
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
_preprocessing_ops = tf.load_op_library(
tf.resource_loader.get_path_to_datafile("./src/ops/build/preprocessing.so"))
def display(img, c):
plt.subplot(int('22' + str(c + 1)))
plt.imshow(img[0, :, :, :])
def main():
"""
.Input("image_a: float32")
.Input("image_b: float32")
.Attr("crop: list(int) >= 2")
.Attr("params_a_name: list(string)")
.Attr("params_a_rand_type: list(string)")
.Attr("params_a_exp: list(bool)")
.Attr("params_a_mean: list(float32)")
.Attr("params_a_spread: list(float32)")
.Attr("params_a_prob: list(float32)")
.Attr("params_b_name: list(string)")
.Attr("params_b_rand_type: list(string)")
.Attr("params_b_exp: list(bool)")
.Attr("params_b_mean: list(float32)")
.Attr("params_b_spread: list(float32)")
.Attr("params_b_prob: list(float32)")
.Output("aug_image_a: float32")
.Output("aug_image_b: float32")
.Output("spatial_transform_a: float32")
.Output("inv_spatial_transform_b: float32")
"""
crop = [364, 492]
params_a_name = ['translate_x', 'translate_y']
params_a_rand_type = ['uniform_bernoulli', 'uniform_bernoulli']
params_a_exp = [False, False]
params_a_mean = [0.0, 0.0]
params_a_spread = [0.4, 0.4]
params_a_prob = [1.0, 1.0]
params_b_name = []
params_b_rand_type = []
params_b_exp = []
params_b_mean = []
params_b_spread = []
params_b_prob = []
with tf.Session() as sess:
with tf.device('/gpu:0'):
image_a = imread('./img0.ppm') / 255.0
image_b = imread('./img1.ppm') / 255.0
flow = read_flow('./flow.flo')
image_a_tf = tf.expand_dims(tf.to_float(tf.constant(image_a, dtype=tf.float64)), 0)
image_b_tf = tf.expand_dims(tf.to_float(tf.constant(image_b, dtype=tf.float64)), 0)
preprocess = _preprocessing_ops.data_augmentation(image_a_tf,
image_b_tf,
crop,
params_a_name,
params_a_rand_type,
params_a_exp,
params_a_mean,
params_a_spread,
params_a_prob,
params_b_name,
params_b_rand_type,
params_b_exp,
params_b_mean,
params_b_spread,
params_b_prob)
out = sess.run(preprocess)
trans = out.spatial_transform_a
inv_trans = out.inv_spatial_transform_b
print(trans.shape)
print(inv_trans.shape)
flow_tf = tf.expand_dims(tf.to_float(tf.constant(flow)), 0)
aug_flow_tf = _preprocessing_ops.flow_augmentation(flow_tf, trans, inv_trans, crop)
aug_flow = sess.run(aug_flow_tf)[0, :, :, :]
# Plot img0, img0aug
plt.subplot(321)
plt.imshow(image_a)
plt.subplot(322)
plt.imshow(out.aug_image_a[0, :, :, :])
# Plot img1, img1aug
plt.subplot(323)
plt.imshow(image_b)
plt.subplot(324)
plt.imshow(out.aug_image_b[0, :, :, :])
# Plot flow, flowaug
plt.subplot(325)
plt.imshow(flow_to_image(flow))
plt.subplot(326)
plt.imshow(flow_to_image(aug_flow))
plt.show()
# image_b_aug = sess.run(image_b_tf)
#
# display(np.expand_dims(image_a, 0), 0)
# display(np.expand_dims(image_b, 0), 1)
# display(image_a_aug, 2)
# display(image_b_aug, 3)
# plt.show()
# o = _preprocessing_ops.flow_augmentation(flow, trans, inv_t, [4, 8])
# print n[:, :, :]
# print n[0, 0, 1], n[0, 0, 0]
# print n[1, 0, 1], n[1, 0, 0]
# print n[2, 0, 1], n[2, 0, 0]
# print '---'
# print sess.run(o)
"""# Goes along width first!!
// Caffe, NKHW: ((n * K + k) * H + h) * W + w at point (n, k, h, w)
// TF, NHWK: ((n * H + h) * W + w) * K + k at point (n, h, w, k)
H=5, W=10, K=2
n=0, h=1, w=5, k=0
(2 * 10) + c
30 49 n[0, 1, 5, 0]"""
print(os.getpid())
input("Press Enter to continue...")
main()
# Last index is channel!!
# K
# value 13 should be at [0, 2, 7, 1] aka batch=0, height=1, width=0, channel=0. it is at index=20.
#
# items = {
# 'N': [0, 0],
# 'H': [5, 2],
# 'W': [10, 7],
# 'K': [2, 1],
# }
#
# for (i1, v1) in items.iteritems():
# for (i2, v2) in items.iteritems():
# for (i3, v3) in items.iteritems():
# for (i4, v4) in items.iteritems():
# if ((v1[1] * v2[0] + v2[1]) * v3[0] + v3[1]) * v4[0] + v4[1] == 55:
# print 'found it: ', i1, i2, i3, i4
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