Import tensorflow to create text graphs if import cv is failed

samples/dnn/tf_text_graph_common.py

@@ -302,3 +302,26 @@ def removeUnusedNodesAndAttrs(to_remove, graph_def):
         for i in reversed(range(len(node.input))):
             if node.input[i] in removedNodes:
                 del node.input[i]
+
+
+def writeTextGraph(modelPath, outputPath, outNodes):
+    try:
+        import cv2 as cv
+
+        cv.dnn.writeTextGraph(modelPath, outputPath)
+    except:
+        import tensorflow as tf
+        from tensorflow.tools.graph_transforms import TransformGraph
+
+        with tf.gfile.FastGFile(modelPath, 'rb') as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+
+            graph_def = TransformGraph(graph_def, ['image_tensor'], outNodes, ['sort_by_execution_order'])
+
+            for node in graph_def.node:
+                if node.op == 'Const':
+                    if 'value' in node.attr:
+                        del node.attr['value']
+
+        tf.train.write_graph(graph_def, "", outputPath, as_text=True)

samples/dnn/tf_text_graph_faster_rcnn.py

 import argparse
 import numpy as np
-import cv2 as cv
 from tf_text_graph_common import *
 
 
@@ -42,7 +41,7 @@ def createFasterRCNNGraph(modelPath, configPath, outputPath):
     print('Features stride:   %f' % features_stride)
 
     # Read the graph.
-    cv.dnn.writeTextGraph(modelPath, outputPath)
+    writeTextGraph(modelPath, outputPath, ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes'])
     graph_def = parseTextGraph(outputPath)
 
     removeIdentity(graph_def)

samples/dnn/tf_text_graph_mask_rcnn.py

 import argparse
 import numpy as np
-import cv2 as cv
 from tf_text_graph_common import *
 
 parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
@@ -48,7 +47,7 @@ print('Height stride:     %f' % height_stride)
 print('Features stride:   %f' % features_stride)
 
 # Read the graph.
-cv.dnn.writeTextGraph(args.input, args.output)
+writeTextGraph(args.input, args.output, ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes', 'detection_masks'])
 graph_def = parseTextGraph(args.output)
 
 removeIdentity(graph_def)

samples/dnn/tf_text_graph_ssd.py

@@ -11,7 +11,6 @@
 # See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
 import argparse
 from math import sqrt
-import cv2 as cv
 from tf_text_graph_common import *
 
 def createSSDGraph(modelPath, configPath, outputPath):
@@ -52,12 +51,12 @@ def createSSDGraph(modelPath, configPath, outputPath):
     print('Input image size: %dx%d' % (image_width, image_height))
 
     # Read the graph.
-    cv.dnn.writeTextGraph(modelPath, outputPath)
-    graph_def = parseTextGraph(outputPath)
-
     inpNames = ['image_tensor']
     outNames = ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes']
 
+    writeTextGraph(modelPath, outputPath, outNames)
+    graph_def = parseTextGraph(outputPath)
+
     def getUnconnectedNodes():
         unconnected = []
         for node in graph_def.node: