Move instruction

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -47,9 +47,9 @@
 #include "opencv2/core/async.hpp"
 
 #if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS
-#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v15 {
+#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v16 {
 #define CV__DNN_EXPERIMENTAL_NS_END }
-namespace cv { namespace dnn { namespace experimental_dnn_34_v15 { } using namespace experimental_dnn_34_v15; }}
+namespace cv { namespace dnn { namespace experimental_dnn_34_v16 { } using namespace experimental_dnn_34_v16; }}
 #else
 #define CV__DNN_EXPERIMENTAL_NS_BEGIN
 #define CV__DNN_EXPERIMENTAL_NS_END

samples/dnn/human_parsing.py

+#!/usr/bin/env python
+'''
+    You can download the converted pb model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
+    or convert the model yourself.
+
+    Follow these steps if you want to convert the original model yourself:
+        To get original .meta pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view
+        For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet
+        Change script evaluate_parsing_JPPNet-s2.py for human parsing
+        1. Remove preprocessing to create image_batch_origin:
+            with tf.name_scope("create_inputs"):
+            ...
+        Add
+            image_batch_origin = tf.placeholder(tf.float32, shape=(2, None, None, 3), name='input')
+
+        2. Create input
+            image = cv2.imread(path/to/image)
+            image_rev = np.flip(image, axis=1)
+            input = np.stack([image, image_rev], axis=0)
+
+        3. Hardcode image_h and image_w shapes to determine output shapes.
+           We use default INPUT_SIZE = (384, 384) from evaluate_parsing_JPPNet-s2.py.
+            parsing_out1 = tf.reduce_mean(tf.stack([tf.image.resize_images(parsing_out1_100, INPUT_SIZE),
+                                                    tf.image.resize_images(parsing_out1_075, INPUT_SIZE),
+                                                    tf.image.resize_images(parsing_out1_125, INPUT_SIZE)]), axis=0)
+           Do similarly with parsing_out2, parsing_out3
+        4. Remove postprocessing. Last net operation:
+            raw_output = tf.reduce_mean(tf.stack([parsing_out1, parsing_out2, parsing_out3]), axis=0)
+           Change:
+            parsing_ = sess.run(raw_output, feed_dict={'input:0': input})
+
+        5. To save model after sess.run(...) add:
+            input_graph_def = tf.get_default_graph().as_graph_def()
+            output_node = "Mean_3"
+            output_graph_def = tf.graph_util.convert_variables_to_constants(sess, input_graph_def, output_node)
+
+            output_graph = "LIP_JPPNet.pb"
+            with tf.gfile.GFile(output_graph, "wb") as f:
+                f.write(output_graph_def.SerializeToString())'
+'''
+
 import argparse
-import cv2 as cv
 import numpy as np
+import cv2 as cv
 
 
 backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
@@ -116,7 +157,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet',
                                      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument('--input', '-i', required=True, help='Path to input image.')
-    parser.add_argument('--model', '-m', required=True, help='Path to pb model(https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0).')
+    parser.add_argument('--model', '-m', required=True, help='Path to pb model.')
     parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
                         help="Choose one of computation backends: "
                              "%d: automatically (by default), "
@@ -135,38 +176,3 @@ if __name__ == '__main__':
     cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
     cv.imshow(winName, output)
     cv.waitKey()
-
-
-# To get original .meta pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view
-# For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet
-# Change script evaluate_parsing_JPPNet-s2.py for human parsing
-# 1. Remove preprocessing to create image_batch_origin:
-# - with tf.name_scope("create_inputs"):
-#     ...
-# Add
-# -    image_batch_origin = tf.placeholder(tf.float32, shape=(2, None, None, 3), name='input')
-#
-# 2. Create input
-#     image = cv2.imread(path/to/image)
-#     image_rev = np.flip(image, axis=1)
-#     input = np.stack([image, image_rev], axis=0)
-#
-# 3. Hardcode image_h and image_w shapes to determine output shapes.
-#    We use default INPUT_SIZE = (384, 384) from evaluate_parsing_JPPNet-s2.py.
-# -  parsing_out1 = tf.reduce_mean(tf.stack([tf.image.resize_images(parsing_out1_100, INPUT_SIZE),
-#                                            tf.image.resize_images(parsing_out1_075, INPUT_SIZE),
-#                                            tf.image.resize_images(parsing_out1_125, INPUT_SIZE)]), axis=0)
-#    Do similarly with parsing_out2, parsing_out3
-# 4. Remove postprocessing. Last net operation:
-#      raw_output = tf.reduce_mean(tf.stack([parsing_out1, parsing_out2, parsing_out3]), axis=0)
-#    Change:
-#      parsing_ = sess.run(raw_output, feed_dict={'input:0': input})
-#
-# 5. To save model after sess.run(...) add:
-#     input_graph_def = tf.get_default_graph().as_graph_def()
-#     output_node = "Mean_3"
-#     output_graph_def = tf.graph_util.convert_variables_to_constants(sess, input_graph_def, output_node)
-#
-#     output_graph = "LIP_JPPNet.pb"
-#     with tf.gfile.GFile(output_graph, "wb") as f:
-#         f.write(output_graph_def.SerializeToString())