├── .gitignore
├── LICENSE
├── README.md
├── docs
└── img
│ └── screenshot.png
├── object_detection_app
├── app.py
├── decorator.py
├── object-detection.service
└── templates
│ ├── _formhelpers.html
│ └── upload.html
└── object_detection_app_p3
├── app.py
├── decorator.py
├── object-detection.service
└── templates
├── _formhelpers.html
└── upload.html
/.gitignore:
--------------------------------------------------------------------------------
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2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
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7 |
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9 | *.so
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12 | .Python
13 | env/
14 | build/
15 | develop-eggs/
16 | dist/
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18 | eggs/
19 | .eggs/
20 | lib/
21 | lib64/
22 | parts/
23 | sdist/
24 | var/
25 | wheels/
26 | *.egg-info/
27 | .installed.cfg
28 | *.egg
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
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33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
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45 | .cache
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48 | *.cover
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55 | # Django stuff:
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58 |
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60 | instance/
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69 | # PyBuilder
70 | target/
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72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # dotenv
85 | .env
86 |
87 | # virtualenv
88 | .venv
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90 | ENV/
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95 |
96 | # Rope project settings
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99 | # mkdocs documentation
100 | /site
101 |
102 | # mypy
103 | .mypy_cache/
104 |
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/README.md:
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1 | # TensorFlow Object Detection API demo
2 |
3 | Disclaimer: This is not an official Google product.
4 |
5 | This is an example application demonstrating how
6 | [TensorFlow Object Detection API][1] and [pretrained models][2]
7 | can be used to create a general object detection service.
8 |
9 | ## Products
10 | - [TensorFlow][3]
11 | - [Google Compute Engine][4]
12 |
13 | ## Language
14 | - [Python][5]
15 |
16 | [1]: https://github.com/tensorflow/models/tree/master/research/object_detection
17 | [2]: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
18 | [3]: https://www.tensorflow.org/
19 | [4]: https://cloud.google.com/compute/
20 | [5]: https://python.org
21 |
22 | ## Prerequisites
23 | 1. A Google Cloud Platform Account
24 | 2. [A new Google Cloud Platform Project][6] for this lab with billing enabled
25 |
26 | [6]: https://console.developers.google.com/project
27 |
28 | ## Do this first
29 | First you launch a GCE instance with the following configuration.
30 |
31 | - vCPU x 8
32 | - Memory 8GB
33 | - Debian GNU/Linux 9 as a guest OS
34 | - Allow HTTP traffic
35 | - Assign a static IP address
36 |
37 | You can leave other settings as default. Once the instance has started,
38 | log in to the guest OS using SSH and change the OS user to root.
39 |
40 | ```
41 | $ sudo -i
42 | ```
43 |
44 | All remaining operations should be done from the root user.
45 |
46 | ## Install packages
47 |
48 | ```
49 | # apt-get update
50 | # apt-get install -y protobuf-compiler python3-pil python3-lxml python3-pip python3-dev git
51 | # pip3 install -U pip
52 | # python3 -m pip install Flask==1.1.1 WTForms==2.2.1 Flask_WTF==0.14.2 Werkzeug==0.16.0 tensorflow==2.0.0
53 | ```
54 |
55 | ## Install the Object Detection API library
56 |
57 | ```
58 | # cd /opt
59 | # git clone https://github.com/tensorflow/models
60 | # cd models/research
61 | # protoc object_detection/protos/*.proto --python_out=.
62 | ```
63 |
64 | ## Install the demo application
65 |
66 | ```
67 | # cd $HOME
68 | # git clone https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example
69 | # cp -a tensorflow-object-detection-example/object_detection_app_p3 /opt/
70 | # chmod u+x /opt/object_detection_app_p3/app.py
71 | # cp /opt/object_detection_app_p3/object-detection.service /etc/systemd/system/
72 | ```
73 |
74 | This application provides a simple user authentication mechanism.
75 | You can change the username and password by modifying the following
76 | part in `/opt/object_detection_app_p3/decorator.py`.
77 |
78 | ```
79 | USERNAME = 'username'
80 | PASSWORD = 'passw0rd'
81 | ```
82 |
83 | ## Launch the demo application
84 |
85 | ```
86 | # systemctl daemon-reload
87 | # systemctl enable object-detection
88 | # systemctl start object-detection
89 | # systemctl status object-detection
90 | ```
91 |
92 | The last command outputs the application status, as in the
93 | following example:
94 | ```
95 | ● object-detection.service - Object Detection API Demo
96 | Loaded: loaded (/etc/systemd/system/object-detection.service; enabled; vendor preset: enabled)
97 | Active: active (running) since Thu 2020-01-02 23:55:17 UTC; 38s ago
98 | Main PID: 17136 (python3)
99 | Tasks: 29 (limit: 4915)
100 | CGroup: /system.slice/object-detection.service
101 | └─17136 python3 /opt/object_detection_app_p3/app.py
102 |
103 | Jan 02 23:55:32 od-test app.py[17136]: 2020-01-02 23:55:32.930129: I tensorflow/core/platform/cpu_feature_guard.cc:142] You
104 | Jan 02 23:55:32 od-test app.py[17136]: 2020-01-02 23:55:32.936310: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94
105 | Jan 02 23:55:32 od-test app.py[17136]: 2020-01-02 23:55:32.937050: I tensorflow/compiler/xla/service/service.cc:168] XLA se
106 | Jan 02 23:55:32 od-test app.py[17136]: 2020-01-02 23:55:32.937078: I tensorflow/compiler/xla/service/service.cc:175] Stre
107 | Jan 02 23:55:40 od-test app.py[17136]: * Serving Flask app "app" (lazy loading)
108 | Jan 02 23:55:40 od-test app.py[17136]: * Environment: production
109 | Jan 02 23:55:40 od-test app.py[17136]: WARNING: This is a development server. Do not use it in a production deployment.
110 | Jan 02 23:55:40 od-test app.py[17136]: Use a production WSGI server instead.
111 | Jan 02 23:55:40 od-test app.py[17136]: * Debug mode: off
112 | Jan 02 23:55:40 od-test app.py[17136]: * Running on http://0.0.0.0:80/ (Press CTRL+C to quit)
113 | ```
114 |
115 | You have to wait around 60secs for the application to finish loading
116 | the pretrained model graph. You'll see the message
117 | `Running on http://0.0.0.0:80/ (Press CTRL+C to quit)` when it's ready.
118 |
119 | Now you can access the instance's static IP address using a web browser.
120 | When you upload an image file with a `jpeg`, `jpg`, or `png` extension,
121 | the application shows the result of the object detection inference.
122 | The inference may take up to 30 seconds, depending on the image.
123 |
124 | The following example shows "cup" in the image. You can also check
125 | other objects such as fork, dining table, person and knife by clicking
126 | labels shown to the right of the image.
127 |
128 | 
129 |
130 | (Image from http://www.ashinari.com/en/)
131 |
132 | ## How to use different models
133 | There are pretrained models that can be used by the application.
134 | They have diffrent characteristics in terms of accuracy and speed.
135 | You can change the model used by the application with the following
136 | steps.
137 |
138 | 1. Choose one of COCO-trained models from [Tensorflow detection model zoo][7]. (The "Outputs" column should be "Boxes".)
139 | 2. Copy an URL of the model from a link on the "Model name" column.
140 | 3. Open `/opt/object_detection_app_p3/app.py` and replace the URL in the following part.
141 |
142 | ```
143 | MODEL_URL = 'http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz'
144 | ```
145 |
146 | 4. Restart the application with the following command.
147 |
148 | ```
149 | # systemctl restart object-detection
150 | ```
151 |
152 | [7]: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
153 |
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/object_detection_app/app.py:
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1 | #!/usr/bin/python
2 | # -*- coding: utf-8 -*-
3 |
4 | # Copyright 2017 Google Inc.
5 | #
6 | # Licensed under the Apache License, Version 2.0 (the "License");
7 | # you may not use this file except in compliance with the License.
8 | # You may obtain a copy of the License at
9 | #
10 | # http://www.apache.org/licenses/LICENSE-2.0
11 | #
12 | # Unless required by applicable law or agreed to in writing, software
13 | # distributed under the License is distributed on an "AS IS" BASIS,
14 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 | # See the License for the specific language governing permissions and
16 | # limitations under the License.
17 |
18 |
19 | import base64
20 | import cStringIO
21 | import sys
22 | import tempfile
23 |
24 | MODEL_BASE = '/opt/models/research'
25 | sys.path.append(MODEL_BASE)
26 | sys.path.append(MODEL_BASE + '/object_detection')
27 | sys.path.append(MODEL_BASE + '/slim')
28 |
29 | from decorator import requires_auth
30 | from flask import Flask
31 | from flask import redirect
32 | from flask import render_template
33 | from flask import request
34 | from flask import url_for
35 | from flask_wtf.file import FileField
36 | import numpy as np
37 | from PIL import Image
38 | from PIL import ImageDraw
39 | import tensorflow as tf
40 | from utils import label_map_util
41 | from werkzeug.datastructures import CombinedMultiDict
42 | from wtforms import Form
43 | from wtforms import ValidationError
44 |
45 |
46 | app = Flask(__name__)
47 |
48 |
49 | @app.before_request
50 | @requires_auth
51 | def before_request():
52 | pass
53 |
54 |
55 | PATH_TO_CKPT = '/opt/graph_def/frozen_inference_graph.pb'
56 | PATH_TO_LABELS = MODEL_BASE + '/object_detection/data/mscoco_label_map.pbtxt'
57 |
58 | content_types = {'jpg': 'image/jpeg',
59 | 'jpeg': 'image/jpeg',
60 | 'png': 'image/png'}
61 | extensions = sorted(content_types.keys())
62 |
63 |
64 | def is_image():
65 | def _is_image(form, field):
66 | if not field.data:
67 | raise ValidationError()
68 | elif field.data.filename.split('.')[-1].lower() not in extensions:
69 | raise ValidationError()
70 |
71 | return _is_image
72 |
73 |
74 | class PhotoForm(Form):
75 | input_photo = FileField(
76 | 'File extension should be: %s (case-insensitive)' % ', '.join(extensions),
77 | validators=[is_image()])
78 |
79 |
80 | class ObjectDetector(object):
81 |
82 | def __init__(self):
83 | self.detection_graph = self._build_graph()
84 | self.sess = tf.Session(graph=self.detection_graph)
85 |
86 | label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
87 | categories = label_map_util.convert_label_map_to_categories(
88 | label_map, max_num_classes=90, use_display_name=True)
89 | self.category_index = label_map_util.create_category_index(categories)
90 |
91 | def _build_graph(self):
92 | detection_graph = tf.Graph()
93 | with detection_graph.as_default():
94 | od_graph_def = tf.GraphDef()
95 | with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
96 | serialized_graph = fid.read()
97 | od_graph_def.ParseFromString(serialized_graph)
98 | tf.import_graph_def(od_graph_def, name='')
99 |
100 | return detection_graph
101 |
102 | def _load_image_into_numpy_array(self, image):
103 | (im_width, im_height) = image.size
104 | return np.array(image.getdata()).reshape(
105 | (im_height, im_width, 3)).astype(np.uint8)
106 |
107 | def detect(self, image):
108 | image_np = self._load_image_into_numpy_array(image)
109 | image_np_expanded = np.expand_dims(image_np, axis=0)
110 |
111 | graph = self.detection_graph
112 | image_tensor = graph.get_tensor_by_name('image_tensor:0')
113 | boxes = graph.get_tensor_by_name('detection_boxes:0')
114 | scores = graph.get_tensor_by_name('detection_scores:0')
115 | classes = graph.get_tensor_by_name('detection_classes:0')
116 | num_detections = graph.get_tensor_by_name('num_detections:0')
117 |
118 | (boxes, scores, classes, num_detections) = self.sess.run(
119 | [boxes, scores, classes, num_detections],
120 | feed_dict={image_tensor: image_np_expanded})
121 |
122 | boxes, scores, classes, num_detections = map(
123 | np.squeeze, [boxes, scores, classes, num_detections])
124 |
125 | return boxes, scores, classes.astype(int), num_detections
126 |
127 |
128 | def draw_bounding_box_on_image(image, box, color='red', thickness=4):
129 | draw = ImageDraw.Draw(image)
130 | im_width, im_height = image.size
131 | ymin, xmin, ymax, xmax = box
132 | (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
133 | ymin * im_height, ymax * im_height)
134 | draw.line([(left, top), (left, bottom), (right, bottom),
135 | (right, top), (left, top)], width=thickness, fill=color)
136 |
137 |
138 | def encode_image(image):
139 | image_buffer = cStringIO.StringIO()
140 | image.save(image_buffer, format='PNG')
141 | imgstr = 'data:image/png;base64,{:s}'.format(
142 | base64.b64encode(image_buffer.getvalue()))
143 | return imgstr
144 |
145 |
146 | def detect_objects(image_path):
147 | image = Image.open(image_path).convert('RGB')
148 | boxes, scores, classes, num_detections = client.detect(image)
149 | image.thumbnail((480, 480), Image.ANTIALIAS)
150 |
151 | new_images = {}
152 | for i in range(num_detections):
153 | if scores[i] < 0.7: continue
154 | cls = classes[i]
155 | if cls not in new_images.keys():
156 | new_images[cls] = image.copy()
157 | draw_bounding_box_on_image(new_images[cls], boxes[i],
158 | thickness=int(scores[i]*10)-4)
159 |
160 | result = {}
161 | result['original'] = encode_image(image.copy())
162 |
163 | for cls, new_image in new_images.iteritems():
164 | category = client.category_index[cls]['name']
165 | result[category] = encode_image(new_image)
166 |
167 | return result
168 |
169 |
170 | @app.route('/')
171 | def upload():
172 | photo_form = PhotoForm(request.form)
173 | return render_template('upload.html', photo_form=photo_form, result={})
174 |
175 |
176 | @app.route('/post', methods=['GET', 'POST'])
177 | def post():
178 | form = PhotoForm(CombinedMultiDict((request.files, request.form)))
179 | if request.method == 'POST' and form.validate():
180 | with tempfile.NamedTemporaryFile() as temp:
181 | form.input_photo.data.save(temp)
182 | temp.flush()
183 | result = detect_objects(temp.name)
184 |
185 | photo_form = PhotoForm(request.form)
186 | return render_template('upload.html',
187 | photo_form=photo_form, result=result)
188 | else:
189 | return redirect(url_for('upload'))
190 |
191 |
192 | client = ObjectDetector()
193 |
194 |
195 | if __name__ == '__main__':
196 | app.run(host='0.0.0.0', port=80, debug=False)
197 |
--------------------------------------------------------------------------------
/object_detection_app/decorator.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 |
3 | # Copyright 2017 Google Inc.
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the "License");
6 | # you may not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # http://www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an "AS IS" BASIS,
13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 |
18 | from functools import wraps
19 |
20 | from flask import request
21 | from flask import Response
22 |
23 |
24 | USERNAME = 'username'
25 | PASSWORD = 'passw0rd'
26 |
27 |
28 | def check_auth(username, password):
29 | return username == USERNAME and password == PASSWORD
30 |
31 |
32 | def authenticate():
33 | return Response(
34 | 'You have to login with proper credentials', 401,
35 | {'WWW-Authenticate': 'Basic realm="Login Required"'})
36 |
37 |
38 | def requires_auth(f):
39 | @wraps(f)
40 | def decorated(*args, **kwargs):
41 | auth = request.authorization
42 | if not auth or not check_auth(auth.username, auth.password):
43 | return authenticate()
44 | return f(*args, **kwargs)
45 | return decorated
46 |
--------------------------------------------------------------------------------
/object_detection_app/object-detection.service:
--------------------------------------------------------------------------------
1 | [Unit]
2 | Description=Object Detection API Demo
3 | After=syslog.target network.target auditd.service
4 |
5 | [Service]
6 | ExecStart=/opt/object_detection_app/app.py
7 | ExecStop=/bin/kill -TERM $MAINPID
8 |
9 | [Install]
10 | WantedBy=multi-user.target
11 |
--------------------------------------------------------------------------------
/object_detection_app/templates/_formhelpers.html:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 |
3 | {% macro render_field(field) %}
4 |