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answered 2018-04-06 05:01:22 -0600

Robb gravatar image

I threw zideks graph into tensorflow and Opencv with the following scripts (some test functions i copy pasted and changed ) basically you only need to look at OpenCVTest and TensorflowResult.

import sys
import tensorflow as tf
import numpy as np
import cv2
import time
import locale
import argparse

def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph


def OpenCVTest(picture,graphToLoad, labels):

    load_graph(graphToLoad)
    image = cv2.imread(picture)
    print("Size of Picture " + str(sys.getsizeof(image)))

    blob= cv2.dnn.blobFromImage(image,1,(299,299))
    print("size of blob " + str(sys.getsizeof(blob)) )

    Net=  cv2.dnn.readNetFromTensorflow(graphToLoad)

    print("size of Net " + str(sys.getsizeof(Net)) )
    Net.setInput(blob)
    start = time.time()

    preds = Net.forward()
    end = time.time()
    print("[INFO] classification took {:.5} seconds".format(end - start))
    top = np.argsort(preds[0])[::-1][:5]

    print("OpenCV: ")
    for i in top:
      print(i)
      print(labels[i],preds[0][i])

def TensorflowResult(pic, graphName, labels):
  print("Tensorflow:")  
  file_name = pic
  model_file = graphName
  #label_file = "jan_mobile_min.txt"
  input_height = 299
  input_width = 299
  input_mean = 128
  input_std = 128
  input_layer = "Mul"
  output_layer = "final_result"

  parser = argparse.ArgumentParser()
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", help="name of file containing labels")
  parser.add_argument("--input_height", type=int, help="input height")
  parser.add_argument("--input_width", type=int, help="input width")
  parser.add_argument("--input_mean", type=int, help="input mean")
  parser.add_argument("--input_std", type=int, help="input std")
  parser.add_argument("--input_layer", help="name of input layer")
  parser.add_argument("--output_layer", help="name of output layer")
  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer

  graph = load_graph(model_file)
  t = read_tensor_from_image_file(file_name,
                                  input_height=input_height,
                                  input_width=input_width,
                                  input_mean=input_mean,
                                  input_std=input_std)

  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name);
  output_operation = graph.get_operation_by_name(output_name);

  with tf.Session(graph=graph) as sess:
    results = sess.run(output_operation.outputs[0],
                      {input_operation.outputs[0]: t})
  results = np.squeeze(results)

  top_k = results.argsort()[-5:][::-1]
  #labels = load_labels(label_file)
  for i in top_k:
    print(i)
    print(labels[i], results[i])

print("prefferred encoding: "+ locale.getpreferredencoding(False))
print("Tensorflow Version: "+tf.__version__)
print("Opencv Version: "+cv2.__version__)


labels = ["daisy","dandelion","roses","sunflower","tulip"]
graphToLoad = "final_graph.pb"
picture = "sun.jpg"
OpenCVTest(picture,graphToLoad, labels)
TensorflowResult(picture, graphToLoad, labels)

The following picture was used and is one of the training pictures of that Tutorial. The positions of the labels i have from his printout in his c++ script. .C:\fakepath\sun.jpg

Could be that i made a horrible mistake or something but it seems Opencv puts out the same numbers on different indexes.

the Ouptut:

prefferred encoding: cp1252 Tensorflow Version: 1.5.0 Opencv Version: 3.4.1 Size of Picture 562628 size of blob 1072956 size of Net 32 [INFO] classification took 0.27402 seconds OpenCV:

2 roses 0.98177

1 dandelion 0.0157247

4 tulip 0.00239711

0 daisy 8.73592e-05

3 sunflower 2.08934e-05

Tensorflow:

3 sunflower 0.984398

1 dandelion 0.00944433

4 tulip 0.00270193

0 daisy 0.00195503

2 roses 0.0015011

I threw zideks graph into tensorflow and Opencv with the following scripts (some test functions i copy pasted and changed ) basically you only need to look at OpenCVTest and TensorflowResult.

import sys
import tensorflow as tf
import numpy as np
import cv2
import time
import locale
import argparse

def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph


def OpenCVTest(picture,graphToLoad, labels):

 labels): 
   load_graph(graphToLoad)
    image = cv2.imread(picture)
    print("Size of Picture " + str(sys.getsizeof(image)))

 str(sys.getsizeof(image))) 
   blob= cv2.dnn.blobFromImage(image,1,(299,299))
    print("size of blob " + str(sys.getsizeof(blob)) )

    Net=  cv2.dnn.readNetFromTensorflow(graphToLoad)

    print("size of Net " + str(sys.getsizeof(Net)) )
    Net.setInput(blob)
    start = time.time()

    preds = Net.forward()
    end = time.time()
    print("[INFO] classification took {:.5} seconds".format(end - start))
    top = np.argsort(preds[0])[::-1][:5]

    print("OpenCV: ")
    for i in top:
      print(i)
      print(labels[i],preds[0][i])

def TensorflowResult(pic, graphName, labels):
  print("Tensorflow:")  
  file_name = pic
  model_file = graphName
  #label_file = "jan_mobile_min.txt"
  input_height = 299
  input_width = 299
  input_mean = 128
  input_std = 128
  input_layer = "Mul"
  output_layer = "final_result"

  parser = argparse.ArgumentParser()
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", help="name of file containing labels")
  parser.add_argument("--input_height", type=int, help="input height")
  parser.add_argument("--input_width", type=int, help="input width")
  parser.add_argument("--input_mean", type=int, help="input mean")
  parser.add_argument("--input_std", type=int, help="input std")
  parser.add_argument("--input_layer", help="name of input layer")
  parser.add_argument("--output_layer", help="name of output layer")
  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer

  graph = load_graph(model_file)
  t = read_tensor_from_image_file(file_name,
                                  input_height=input_height,
                                  input_width=input_width,
                                  input_mean=input_mean,
                                  input_std=input_std)

  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name);
  output_operation = graph.get_operation_by_name(output_name);

  with tf.Session(graph=graph) as sess:
    results = sess.run(output_operation.outputs[0],
                      {input_operation.outputs[0]: t})
  results = np.squeeze(results)

  top_k = results.argsort()[-5:][::-1]
  #labels = load_labels(label_file)
  for i in top_k:
    print(i)
    print(labels[i], results[i])

print("prefferred encoding: "+ locale.getpreferredencoding(False))
print("Tensorflow Version: "+tf.__version__)
print("Opencv Version: "+cv2.__version__)


labels = ["daisy","dandelion","roses","sunflower","tulip"]
graphToLoad = "final_graph.pb"
picture = "sun.jpg"
OpenCVTest(picture,graphToLoad, labels)
TensorflowResult(picture, graphToLoad, labels)

The following picture was used and is one of the training pictures of that Tutorial. The positions of the labels i have from his printout in his c++ script. .C:\fakepath\sun.jpg

Could be that i made a horrible mistake or something but it seems Opencv puts out the same numbers on different indexes.

the Ouptut:

prefferred encoding: cp1252 Tensorflow Version: 1.5.0 Opencv Version: 3.4.1 Size of Picture 562628 size of blob 1072956 size of Net 32 [INFO] classification took 0.27402 seconds OpenCV:

2 roses 0.98177

1 dandelion 0.0157247

4 tulip 0.00239711

0 daisy 8.73592e-05

3 sunflower 2.08934e-05

Tensorflow:

3 sunflower 0.984398

1 dandelion 0.00944433

4 tulip 0.00270193

0 daisy 0.00195503

2 roses 0.0015011

I threw zideks graph into tensorflow and Opencv with the following scripts (some test functions i copy pasted and changed ) basically you only need to look at OpenCVTest and TensorflowResult.

import sys
import tensorflow as tf
import numpy as np
import cv2
import time
import locale
import argparse

def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph


def OpenCVTest(picture,graphToLoad, labels): 
   load_graph(graphToLoad)
   image = cv2.imread(picture)
   print("Size of Picture " + str(sys.getsizeof(image))) 
   blob= cv2.dnn.blobFromImage(image,1,(299,299))
   print("size of blob " + str(sys.getsizeof(blob)) )

   Net=  cv2.dnn.readNetFromTensorflow(graphToLoad)

   print("size of Net " + str(sys.getsizeof(Net)) )
   Net.setInput(blob)
   start = time.time()

   preds = Net.forward()
   end = time.time()
   print("[INFO] classification took {:.5} seconds".format(end - start))
   top = np.argsort(preds[0])[::-1][:5]

   print("OpenCV: ")
   for i in top:
     print(i)
     print(labels[i],preds[0][i])

def TensorflowResult(pic, graphName, labels):
  print("Tensorflow:")  
  file_name = pic
  model_file = graphName
  input_height = 299
  input_width = 299
  input_mean = 128
  input_std = 128
  input_layer = "Mul"
  output_layer = "final_result"

  parser = argparse.ArgumentParser()
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", help="name of file containing labels")
  parser.add_argument("--input_height", type=int, help="input height")
  parser.add_argument("--input_width", type=int, help="input width")
  parser.add_argument("--input_mean", type=int, help="input mean")
  parser.add_argument("--input_std", type=int, help="input std")
  parser.add_argument("--input_layer", help="name of input layer")
  parser.add_argument("--output_layer", help="name of output layer")
  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer

  graph = load_graph(model_file)
  t = read_tensor_from_image_file(file_name,
                                  input_height=input_height,
                                  input_width=input_width,
                                  input_mean=input_mean,
                                  input_std=input_std)

  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name);
  output_operation = graph.get_operation_by_name(output_name);

  with tf.Session(graph=graph) as sess:
    results = sess.run(output_operation.outputs[0],
                      {input_operation.outputs[0]: t})
  results = np.squeeze(results)

  top_k = results.argsort()[-5:][::-1]
  #labels = load_labels(label_file)
  for i in top_k:
    print(i)
    print(labels[i], results[i])

print("prefferred encoding: "+ locale.getpreferredencoding(False))
print("Tensorflow Version: "+tf.__version__)
print("Opencv Version: "+cv2.__version__)


labels = ["daisy","dandelion","roses","sunflower","tulip"]
graphToLoad = "final_graph.pb"
picture = "sun.jpg"
OpenCVTest(picture,graphToLoad, labels)
TensorflowResult(picture, graphToLoad, labels)

The following picture was used and is one of the training pictures of that Tutorial. The positions of the labels i have from his printout in his c++ script. .C:\fakepath\sun.jpg

Could be that i made a horrible mistake or something but it seems Opencv puts out the same numbers on different indexes.

the Ouptut:

prefferred encoding: cp1252 Tensorflow Version: 1.5.0 Opencv Version: 3.4.1 Size of Picture 562628 size of blob 1072956 size of Net 32 [INFO] classification took 0.27402 seconds OpenCV:

seconds

OpenCV:

2 roses 0.98177

1 dandelion 0.0157247

4 tulip 0.00239711

0 daisy 8.73592e-05

3 sunflower 2.08934e-05

Tensorflow:

3 sunflower 0.984398

1 dandelion 0.00944433

4 tulip 0.00270193

0 daisy 0.00195503

2 roses 0.0015011

edit: Fixed it it was my error (but the lines below could have some use for others) it was the wrong blob: blob= cv2.dnn.blobFromImage(image,1.0/127.5,(299,299),(127.5,127.5,127.5), True, False)

I threw zideks graph into tensorflow and Opencv with the following scripts (some test functions i copy pasted and changed ) basically you only need to look at OpenCVTest and TensorflowResult.

import sys
import tensorflow as tf
import numpy as np
import cv2
import time
import locale
import argparse

def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph


def OpenCVTest(picture,graphToLoad, labels): 
   load_graph(graphToLoad)
   image = cv2.imread(picture)
   print("Size of Picture " + str(sys.getsizeof(image))) 
   blob= cv2.dnn.blobFromImage(image,1,(299,299))
   print("size of blob " + str(sys.getsizeof(blob)) )

   Net=  cv2.dnn.readNetFromTensorflow(graphToLoad)

   print("size of Net " + str(sys.getsizeof(Net)) )
   Net.setInput(blob)
   start = time.time()

   preds = Net.forward()
   end = time.time()
   print("[INFO] classification took {:.5} seconds".format(end - start))
   top = np.argsort(preds[0])[::-1][:5]

   print("OpenCV: ")
   for i in top:
     print(i)
     print(labels[i],preds[0][i])

def TensorflowResult(pic, graphName, labels):
  print("Tensorflow:")  
  file_name = pic
  model_file = graphName
  input_height = 299
  input_width = 299
  input_mean = 128
  input_std = 128
  input_layer = "Mul"
  output_layer = "final_result"

  parser = argparse.ArgumentParser()
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", help="name of file containing labels")
  parser.add_argument("--input_height", type=int, help="input height")
  parser.add_argument("--input_width", type=int, help="input width")
  parser.add_argument("--input_mean", type=int, help="input mean")
  parser.add_argument("--input_std", type=int, help="input std")
  parser.add_argument("--input_layer", help="name of input layer")
  parser.add_argument("--output_layer", help="name of output layer")
  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer

  graph = load_graph(model_file)
  t = read_tensor_from_image_file(file_name,
                                  input_height=input_height,
                                  input_width=input_width,
                                  input_mean=input_mean,
                                  input_std=input_std)

  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name);
  output_operation = graph.get_operation_by_name(output_name);

  with tf.Session(graph=graph) as sess:
    results = sess.run(output_operation.outputs[0],
                      {input_operation.outputs[0]: t})
  results = np.squeeze(results)

  top_k = results.argsort()[-5:][::-1]
  #labels = load_labels(label_file)
  for i in top_k:
    print(i)
    print(labels[i], results[i])

print("prefferred encoding: "+ locale.getpreferredencoding(False))
print("Tensorflow Version: "+tf.__version__)
print("Opencv Version: "+cv2.__version__)


labels = ["daisy","dandelion","roses","sunflower","tulip"]
graphToLoad = "final_graph.pb"
picture = "sun.jpg"
OpenCVTest(picture,graphToLoad, labels)
TensorflowResult(picture, graphToLoad, labels)

The following picture was used and is one of the training pictures of that Tutorial. The positions of the labels i have from his printout in his c++ script. .C:\fakepath\sun.jpg

Could be that i made a horrible mistake or something but it seems Opencv puts out the same numbers on different indexes.

the Ouptut:

prefferred encoding: cp1252 Tensorflow Version: 1.5.0 Opencv Version: 3.4.1 Size of Picture 562628 size of blob 1072956 size of Net 32 [INFO] classification took 0.27402 seconds

OpenCV:

2 roses 0.98177

1 dandelion 0.0157247

4 tulip 0.00239711

0 daisy 8.73592e-05

3 sunflower 2.08934e-05

Tensorflow:

3 sunflower 0.984398

1 dandelion 0.00944433

4 tulip 0.00270193

0 daisy 0.00195503

2 roses 0.0015011

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