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https://git.ffmpeg.org/ffmpeg.git
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8ce9d88f93
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')
z1 = 2 / x
z2 = 1 / z1
z3 = z2 / 0.25 + 0.3
z4 = z3 - x * 1.5 - 0.3
y = tf.identity(z4, 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))
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
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|---|---|---|
| .. | ||
| dnn_backend_native_layer_conv2d.c | ||
| dnn_backend_native_layer_conv2d.h | ||
| dnn_backend_native_layer_depth2space.c | ||
| dnn_backend_native_layer_depth2space.h | ||
| dnn_backend_native_layer_mathbinary.c | ||
| dnn_backend_native_layer_mathbinary.h | ||
| dnn_backend_native_layer_maximum.c | ||
| dnn_backend_native_layer_maximum.h | ||
| dnn_backend_native_layer_pad.c | ||
| dnn_backend_native_layer_pad.h | ||
| dnn_backend_native_layers.c | ||
| dnn_backend_native_layers.h | ||
| dnn_backend_native.c | ||
| dnn_backend_native.h | ||
| dnn_backend_tf.c | ||
| dnn_backend_tf.h | ||
| dnn_interface.c | ||
| Makefile | ||