it can be tested with model file generated with below python script: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpg') in_img = in_img.astype(np.float32)/255.0 in_data = in_img[np.newaxis, :] x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in') x1 = tf.minimum(0.7, x) x2 = tf.maximum(x1, 0.4) y = tf.identity(x2, name='dnn_out') sess=tf.Session() sess.run(tf.global_variables_initializer()) graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False) print("image_process.pb generated, please use \ path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n") output = sess.run(y, feed_dict={x: in_data}) imageio.imsave("out.jpg", np.squeeze(output))
Showing the changes to tools/python/convert_header.py (1 of 4 files). Show all
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| @@ -23,4 +23,4 @@ str = 'FFMPEGDNNNATIVE' · 22 unmodified lines | |||
| 23 | major = 1 | 23 | major = 1 |
| 24 | 24 | ||
| 25 | # increase minor when we don't have to re-convert the model file | 25 | # increase minor when we don't have to re-convert the model file |
| 26 | minor = 4 | 26 | minor = 5 |