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Diffstat (limited to 'Codes/flownet2/test.py')
| -rw-r--r-- | Codes/flownet2/test.py | 163 |
1 files changed, 163 insertions, 0 deletions
diff --git a/Codes/flownet2/test.py b/Codes/flownet2/test.py new file mode 100644 index 0000000..2fcb380 --- /dev/null +++ b/Codes/flownet2/test.py @@ -0,0 +1,163 @@ +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 |