Tensorflow 之物体检测

1. # yum info protobuf protobuf-compiler
2. 2.5.0 <-版本太低需要protobuf 2.6.1以上版本
3. # yum -y install autoconf automake libtool curl make g++ unzip
4. # cd /usr/local/src/
5. # wget https://github.com/google/protobuf/archive/v3.3.1.tar.gz -O protobuf-3.3.1.tar.gz
6. # tar -zxvf protobuf-3.3.1.tar.gz
7. # cd protobuf-3.3.1
8. # ./autogen.sh
9. # ./configure --prefix=/usr/local/protobuf
10. # make
11. # make install
12. # ldconfig
13. # export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/protobuf/lib
14. # export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/protobuf/lib
15. # export PATH=$PATH:/usr/local/protobuf/bin
16. # protoc --version
17. libprotoc 3.3.1

1. # source /usr/local/tensorflow2/bin/activate
2. # cd /usr/local/tensorflow2/tensorflow-models

1. # pip install pillow
2. # pip install lxml
3. # pip install jupyter
4. # pip install matplotlib

1. # protoc object_detection/protos/*.proto --python_out=.

1. # export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
2. # ldconfig

1. # python object_detection/builders/model_builder_test.py

1. # source /usr/local/tensorflow2/bin/activate
2. # cd /usr/local/tensorflow2/tensorflow-models/object_detection/
3. # jupyter notebook --generate-config --allow-root
4. # python -c 'from notebook.auth import passwd;print(passwd())'
5. Enter password:123456
6. Verify password:123456
7. sha1:7d026454901a:009ae34a09296674d4a13521b80b8527999fd3da
8. # vi /root/.jupyter/jupyter_notebook_config.py
9. c.NotebookApp.password = 'sha1:7d026454901a:009ae34a09296674d4a13521b80b8527999fd3da'
10. # jupyter notebook --ip=127.0.0.1 --allow-root

1. # TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
2. TEST_IMAGE_PATHS = ['<your image path>']

1. plt.figure(figsize=IMAGE_SIZE)
2. plt.imshow(image_np)

1. print(image_path.split('.')[0]+'_labeled.jpg') # Add
2. plt.figure(figsize=IMAGE_SIZE, dpi=300) # Modify
3. plt.imshow(image_np)
4. plt.savefig(image_path.split('.')[0] + '_labeled.jpg') # Add

1. # vi object_detect_demo.py
2. # python object_detect_demo.py

1. import numpy as np
2. import os
3. import six.moves.urllib as urllib
4. import sys
5. import tarfile
6. import tensorflow as tf
7. import zipfile
8. import matplotlib
9. # Matplotlib chooses Xwindows backend by default.
10. matplotlib.use('Agg')
11. from collections import defaultdict
12. from io import StringIO
13. from matplotlib import pyplot as plt
14. from PIL import Image
15. from utils import label_map_util
16. from utils import visualization_utils as vis_util
17. ##################### Download Model
18. # What model to download.
19. MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
20. MODEL_FILE = MODEL_NAME + '.tar.gz'
21. DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
22. # Path to frozen detection graph. This is the actual model that is used for the object detection.
23. PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
24. # List of the strings that is used to add correct label for each box.
25. PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
26. NUM_CLASSES = 90
27. # Download model if not already downloaded
28. if not os.path.exists(PATH_TO_CKPT):
29. print('Downloading model... (This may take over 5 minutes)')
30. opener = urllib.request.URLopener()
31. opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
32. print('Extracting...')
33. tar_file = tarfile.open(MODEL_FILE)
34. for file in tar_file.getmembers():
35. file_name = os.path.basename(file.name)
36. if 'frozen_inference_graph.pb' in file_name:
37. tar_file.extract(file, os.getcwd())
38. else:
39. print('Model already downloaded.')
40. ##################### Load a (frozen) Tensorflow model into memory.
41. print('Loading model...')
42. detection_graph = tf.Graph()
43. with detection_graph.as_default():
44. od_graph_def = tf.GraphDef()
45. with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
46. serialized_graph = fid.read()
47. od_graph_def.ParseFromString(serialized_graph)
48. tf.import_graph_def(od_graph_def, name='')
49. ##################### Loading label map
50. print('Loading label map...')
51. label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
52. categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
53. category_index = label_map_util.create_category_index(categories)
54. ##################### Helper code
55. def load_image_into_numpy_array(image):
56. (im_width, im_height) = image.size
57. return np.array(image.getdata()).reshape(
58. (im_height, im_width, 3)).astype(np.uint8)
59. ##################### Detection
60. # Path to test image
61. TEST_IMAGE_PATH = 'test_images/2008_004037.jpg'
62. # Size, in inches, of the output images.
63. IMAGE_SIZE = (12, 8)
64. print('Detecting...')
65. with detection_graph.as_default():
66. with tf.Session(graph=detection_graph) as sess:
67. print(TEST_IMAGE_PATH)
68. image = Image.open(TEST_IMAGE_PATH)
69. image_np = load_image_into_numpy_array(image)
70. image_np_expanded = np.expand_dims(image_np, axis=0)
71. image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
72. boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
73. scores = detection_graph.get_tensor_by_name('detection_scores:0')
74. classes = detection_graph.get_tensor_by_name('detection_classes:0')
75. num_detections = detection_graph.get_tensor_by_name('num_detections:0')
76. # Actual detection.
77. (boxes, scores, classes, num_detections) = sess.run(
78. [boxes, scores, classes, num_detections],
79. feed_dict={image_tensor: image_np_expanded})
80. # Print the results of a detection.
81. print(scores)
82. print(classes)
83. print(category_index)
84. # Visualization of the results of a detection.
85. vis_util.visualize_boxes_and_labels_on_image_array(
86. image_np,
87. np.squeeze(boxes),
88. np.squeeze(classes).astype(np.int32),
89. np.squeeze(scores),
90. category_index,
91. use_normalized_coordinates=True,
92. line_thickness=8)
93. print(TEST_IMAGE_PATH.split('.')[0]+'_labeled.jpg')
94. plt.figure(figsize=IMAGE_SIZE, dpi=300)
95. plt.imshow(image_np)
96. plt.savefig(TEST_IMAGE_PATH.split('.')[0] + '_labeled.jpg')

引用

[[ 0.91731095  0.80875194  0.67557526  0.67192227  0.3568708   0.23992854

0.21897335  0.21443138  0.17383011  0.15901341  0.15674619  0.1558814

0.15265906  0.1489363   0.14805503  0.13470834  0.132047    0.12655555

0.12086334  0.11752894  0.10897312  0.10791111  0.10386674  0.10181901

0.09687284  0.09644313  0.0929096   0.09187065  0.08420605  0.08250966

0.08131051  0.07928694  0.07632151  0.07570603  0.0749495   0.07267584

0.07258119  0.07075463  0.06964011  0.06901822  0.06894562  0.06892171

0.06805679  0.06769397  0.06536105  0.06501643  0.06417865  0.06416738

0.06377003  0.0634084   0.06247949  0.06245064  0.06173467  0.06126672

0.06037482  0.05930964  0.05813492  0.05751488  0.05747007  0.05746768

0.05737954  0.05694786  0.05581251  0.05559204  0.05539726  0.054422

0.05410738  0.05389332  0.05359224  0.05349119  0.05328105  0.05284562

0.0527565   0.05231072  0.05224103  0.05190464  0.05123441  0.05110639

0.05002856  0.04982324  0.04956287  0.04943769  0.04906119  0.04891028

0.04835404  0.04812568  0.0470486   0.04596276  0.04592303  0.04565331

0.04564101  0.04550403  0.04531116  0.04507401  0.04495776  0.04489629

0.04475424  0.0447024   0.04434219  0.04395287]]

[[  1.   1.  44.  44.  44.  44.  44.  75.  44.  44.  44.  82.  44.  88.

79.  44.  44.  44.  88.  44.  88.  79.  44.  82.   1.  47.  88.  67.

44.  70.  47.  79.  67.  67.  67.  67.  79.  72.  47.   1.  44.  44.

44.   1.  67.  75.  72.  62.   1.   1.  44.  82.  79.  47.  79.  67.

44.   1.  51.  75.  79.  51.  79.  62.  67.  44.  82.  82.  79.  82.

79.  75.  72.  82.   1.   1.  46.  88.  82.  82.  82.  44.  67.  62.

82.  79.  62.   1.  67.   1.  82.   1.  67.   1.  44.  88.  79.  51.

44.  82.]]

{1: {'id': 1, 'name': u'person'}, 2: {'id': 2, 'name': u'bicycle'},
3: {'id': 3, 'name': u'car'}, 4: {'id': 4, 'name': u'motorcycle'}, 5:
{'id': 5, 'name': u'airplane'}, 6: {'id': 6, 'name': u'bus'}, 7: {'id':
7, 'name': u'train'}, 8: {'id': 8, 'name': u'truck'}, 9: {'id': 9,
'name': u'boat'}, 10: {'id': 10, 'name': u'traffic light'}, 11: {'id':
11, 'name': u'fire hydrant'}, 13: {'id': 13, 'name': u'stop sign'}, 14:
{'id': 14, 'name': u'parking meter'}, 15: {'id': 15, 'name': u'bench'},
16: {'id': 16, 'name': u'bird'}, 17: {'id': 17, 'name': u'cat'}, 18:
{'id': 18, 'name': u'dog'}, 19: {'id': 19, 'name': u'horse'}, 20: {'id':
20, 'name': u'sheep'}, 21: {'id': 21, 'name': u'cow'}, 22: {'id': 22,
'name': u'elephant'}, 23: {'id': 23, 'name': u'bear'}, 24: {'id': 24,
'name': u'zebra'}, 25: {'id': 25, 'name': u'giraffe'}, 27: {'id': 27,
'name': u'backpack'}, 28: {'id': 28, 'name': u'umbrella'}, 31: {'id':
31, 'name': u'handbag'}, 32: {'id': 32, 'name': u'tie'}, 33: {'id': 33,
'name': u'suitcase'}, 34: {'id': 34, 'name': u'frisbee'}, 35: {'id': 35,
'name': u'skis'}, 36: {'id': 36, 'name': u'snowboard'}, 37: {'id': 37,
'name': u'sports ball'}, 38: {'id': 38, 'name': u'kite'}, 39: {'id': 39,
'name': u'baseball bat'}, 40: {'id': 40, 'name': u'baseball glove'},
41: {'id': 41, 'name': u'skateboard'}, 42: {'id': 42, 'name':
u'surfboard'}, 43: {'id': 43, 'name': u'tennis racket'}, 44: {'id': 44,
'name': u'bottle'}, 46: {'id': 46, 'name': u'wine glass'}, 47: {'id':
47, 'name': u'cup'}, 48: {'id': 48, 'name': u'fork'}, 49: {'id': 49,
'name': u'knife'}, 50: {'id': 50, 'name': u'spoon'}, 51: {'id': 51,
'name': u'bowl'}, 52: {'id': 52, 'name': u'banana'}, 53: {'id': 53,
'name': u'apple'}, 54: {'id': 54, 'name': u'sandwich'}, 55: {'id': 55,
'name': u'orange'}, 56: {'id': 56, 'name': u'broccoli'}, 57: {'id': 57,
'name': u'carrot'}, 58: {'id': 58, 'name': u'hot dog'}, 59: {'id': 59,
'name': u'pizza'}, 60: {'id': 60, 'name': u'donut'}, 61: {'id': 61,
'name': u'cake'}, 62: {'id': 62, 'name': u'chair'}, 63: {'id': 63,
'name': u'couch'}, 64: {'id': 64, 'name': u'potted plant'}, 65: {'id':
65, 'name': u'bed'}, 67: {'id': 67, 'name': u'dining table'}, 70: {'id':
70, 'name': u'toilet'}, 72: {'id': 72, 'name': u'tv'}, 73: {'id': 73,
'name': u'laptop'}, 74: {'id': 74, 'name': u'mouse'}, 75: {'id': 75,
'name': u'remote'}, 76: {'id': 76, 'name': u'keyboard'}, 77: {'id': 77,
'name': u'cell phone'}, 78: {'id': 78, 'name': u'microwave'}, 79: {'id':
79, 'name': u'oven'}, 80: {'id': 80, 'name': u'toaster'}, 81: {'id':
81, 'name': u'sink'}, 82: {'id': 82, 'name': u'refrigerator'}, 84:
{'id': 84, 'name': u'book'}, 85: {'id': 85, 'name': u'clock'}, 86:
{'id': 86, 'name': u'vase'}, 87: {'id': 87, 'name': u'scissors'}, 88:
{'id': 88, 'name': u'teddy bear'}, 89: {'id': 89, 'name': u'hair
drier'}, 90: {'id': 90, 'name': u'toothbrush'}}

引用

scores - 0.91731095  0.80875194  0.67557526  0.67192227

classes - 1.   1.  44.  44.

category_index - 1: {'id': 1, 'name': u'person'} 44: {'id': 44, 'name': u'bottle'}

1. # cd /usr/local/tensorflow2/tensorflow-models/object_detection
2. # wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
3. # wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
4. # tar -zxvf annotations.tar.gz
5. # tar -zxvf images.tar.gz
6. # python create_pet_tf_record.py --data_dir=`pwd` --output_dir=`pwd`

1. # wget http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_coco_11_06_2017.tar.gz
2. # tar -zxvf faster_rcnn_resnet101_coco_11_06_2017.tar.gz
3. # cp samples/configs/faster_rcnn_resnet101_pets.config mypet.config
4. # vi mypet.config

引用

fine_tune_checkpoint:

"/usr/local/tensorflow2/tensorflow-models/object_detection/faster_rcnn_resnet101_coco_11_06_2017/model.ckpt"

from_detection_checkpoint: true

train_input_reader: {

tf_record_input_reader {

input_path: "/usr/local/tensorflow2/tensorflow-models/object_detection/pet_train.record"

}

label_map_path: "/usr/local/tensorflow2/tensorflow-models/object_detection/data/pet_label_map.pbtxt"

}

eval_input_reader: {

tf_record_input_reader {

input_path: "/usr/local/tensorflow2/tensorflow-models/object_detection/pet_val.record"

}

label_map_path: "/usr/local/tensorflow2/tensorflow-models/object_detection/data/pet_label_map.pbtxt"

}

1. # mkdir -p /usr/local/tensorflow2/tensorflow-models/object_detection/model/train
2. # mkdir -p /usr/local/tensorflow2/tensorflow-models/object_detection/model/eval

1. # python object_detection/train.py \
2. --logtostderr \
3. --pipeline_config_path='/usr/local/tensorflow2/tensorflow-models/object_detection/mypet.config' \
4. --train_dir='/usr/local/tensorflow2/tensorflow-models/object_detection/model/train'

引用

INFO:tensorflow:Starting Session.

INFO:tensorflow:Saving checkpoint to path /usr/local/tensorflow2/tensorflow-models/object_detection/model/train/model.ckpt

INFO:tensorflow:Starting Queues.

INFO:tensorflow:global_step/sec: 0

INFO:tensorflow:Recording summary at step 0.

1. # python object_detection/eval.py \
2. --logtostderr \
3. --pipeline_config_path='/usr/local/tensorflow2/tensorflow-models/object_detection/mypet.config' \
4. --checkpoint_dir='/usr/local/tensorflow2/tensorflow-models/object_detection/model/train' \
5. --eval_dir='/usr/local/tensorflow2/tensorflow-models/object_detection/model/eval'

1. # tensorboard --logdir=/usr/local/tensorflow/tensorflow-models/object_detection/model/

引用

Traceback (most recent call last):

File "create_pet_tf_record.py", line 213, in <module>

tf.app.run()

File "/usr/local/tensorflow/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 48, in run

_sys.exit(main(_sys.argv[:1] + flags_passthrough))

File "create_pet_tf_record.py", line 208, in main

image_dir, train_examples)

File "create_pet_tf_record.py", line 177, in create_tf_record

tf_example = dict_to_tf_example(data, label_map_dict, image_dir)

File "create_pet_tf_record.py", line 131, in dict_to_tf_example

'image/filename': dataset_util.bytes_feature(data['filename']),

File "/usr/local/tensorflow/tensorflow-models/object_detection/utils/dataset_util.py", line 30, in bytes_feature

return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

TypeError: 'leonberger_185.jpg' has type str, but expected one of: bytes

引用

Traceback (most recent call last):

File "object_detection/train.py", line 198, in <module>

tf.app.run()

File "/usr/local/tensorflow/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 48, in run

_sys.exit(main(_sys.argv[:1] + flags_passthrough))

File "object_detection/train.py", line 194, in main

worker_job_name, is_chief, FLAGS.train_dir)

File "/usr/local/tensorflow/tensorflow-models/object_detection/trainer.py", line 184, in train

data_augmentation_options)

File "/usr/local/tensorflow/tensorflow-models/object_detection/trainer.py", line 77, in _create_input_queue

prefetch_queue_capacity=prefetch_queue_capacity)

File "/usr/local/tensorflow/tensorflow-models/object_detection/core/batcher.py", line 81, in __init__

{key: tensor.get_shape() for key, tensor in tensor_dict.iteritems()})

AttributeError: 'dict' object has no attribute 'iteritems'

引用

Traceback (most recent call last):

File "object_detection/train.py", line 198, in <module>

tf.app.run()

File "/usr/local/tensorflow/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 48, in run

_sys.exit(main(_sys.argv[:1] + flags_passthrough))

File "object_detection/train.py", line 194, in main

worker_job_name, is_chief, FLAGS.train_dir)

File "/usr/local/tensorflow/tensorflow-models/object_detection/trainer.py", line 184, in train

data_augmentation_options)

File "/usr/local/tensorflow/tensorflow-models/object_detection/trainer.py", line 77, in _create_input_queue

prefetch_queue_capacity=prefetch_queue_capacity)

File "/usr/local/tensorflow/tensorflow-models/object_detection/core/batcher.py", line 93, in __init__

num_threads=num_batch_queue_threads)

File "/usr/local/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/input.py", line 919, in batch

name=name)

File "/usr/local/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/input.py", line 697, in _batch

tensor_list = _as_tensor_list(tensors)

File "/usr/local/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/input.py", line 385, in _as_tensor_list

return [tensors[k] for k in sorted(tensors)]

TypeError: '<' not supported between instances of 'tuple' and 'str'