├── .gitignore ├── Dockerfile ├── LICENSE ├── README.md ├── app.py ├── doc └── cat_detections_output.jpg ├── docker-compose.yml ├── docker └── service │ ├── app │ └── run │ └── jupyter │ └── run ├── example_object_detection_inference.ipynb └── test_images ├── carrot-kale-walnuts-tomatoes.jpeg ├── pexels-photo-297755.jpeg └── pexels-photo-342214.jpeg /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | *.pyc 3 | .ipynb_checkpoints 4 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM phusion/baseimage:0.9.22 2 | 3 | ENV HOME /root 4 | ENV DEBIAN_FRONTEND noninteractive 5 | ENV LANG en_US.UTF-8 6 | ENV LANGUAGE en_US 7 | ENV LC_ALL en_US.UTF-8 8 | ENV EDITOR vim 9 | ENV TERM xterm 10 | 11 | RUN locale-gen en_US.UTF-8 12 | 13 | RUN sed -i -e 's,http://archive.ubuntu.com,http://de.archive.ubuntu.com,g' /etc/apt/sources.list 14 | RUN apt-get update && \ 15 | apt-get -y install git wget curl jq && \ 16 | apt-get upgrade -y -o Dpkg::Options::="--force-confold" && \ 17 | apt-get clean && \ 18 | rm -rf /var/lib/apt/lists/* 19 | 20 | # Deactivate unused services 21 | RUN mv /etc/service/cron /etc/service/.cron 22 | RUN mv /etc/service/sshd /etc/service/.sshd 23 | RUN mv /etc/service/syslog-ng /etc/service/.syslog-ng 24 | RUN mv /etc/service/syslog-forwarder /etc/service/.syslog-forwarder 25 | RUN chmod 444 /etc/my_init.d/00_regen_ssh_host_keys.sh 26 | 27 | RUN apt-get update && apt-get install -y \ 28 | wget \ 29 | bzip2 \ 30 | ca-certificates \ 31 | libglib2.0-0 \ 32 | libxext6 \ 33 | libsm6 \ 34 | libxrender1 \ 35 | build-essential 36 | 37 | WORKDIR /tmp 38 | RUN wget --quiet https://github.com/google/protobuf/releases/download/v2.6.1/protobuf-2.6.1.tar.gz && \ 39 | tar xzf protobuf-2.6.1.tar.gz && \ 40 | mv protobuf-2.6.1 /opt/protobuf 41 | RUN cd /opt/protobuf && \ 42 | ./configure && \ 43 | make && \ 44 | make check && \ 45 | make install && \ 46 | ldconfig 47 | RUN protoc --version \ 48 | 49 | RUN echo 'export PATH=/opt/conda/bin:$PATH' > /etc/profile.d/conda.sh && \ 50 | wget --quiet https://repo.continuum.io/miniconda/Miniconda3-4.2.12-Linux-x86_64.sh -O ~/miniconda.sh && \ 51 | /bin/bash ~/miniconda.sh -b -p /opt/conda && \ 52 | rm ~/miniconda.sh 53 | ENV PATH /opt/conda/bin:$PATH 54 | RUN conda info -a 55 | 56 | RUN conda install -y scipy 57 | RUN pip install tensorflow pillow lxml jupyter matplotlib protobuf 58 | 59 | RUN git clone https://github.com/tensorflow/models.git /opt/tensorflow-models 60 | WORKDIR /opt/tensorflow-models 61 | RUN pip install -e . 62 | RUN protoc object_detection/protos/*.proto --python_out=. 63 | ENV PYTHONPATH $PYTHONPATH:/opt/tensorflow-models:/opt/tensorflow-models/slim 64 | RUN python object_detection/builders/model_builder_test.py 65 | 66 | WORKDIR /opt/tensorflow-models-object_detection 67 | RUN wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz && \ 68 | tar xzf ssd_mobilenet_v1_coco_11_06_2017.tar.gz && \ 69 | rm ssd_mobilenet_v1_coco_11_06_2017.tar.gz 70 | WORKDIR /app 71 | RUN pip install flask 72 | ADD docker/service/ /etc/service/ 73 | 74 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright {yyyy} {name of copyright owner} 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | 203 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Tensorflow object detector web service 2 | 3 | Rudimentary flask web service that wraps the [Tensorflow Object Detection API](https://github.com/tensorflow/models/tree/master/object_detection). 4 | 5 | ## Instructions 6 | 7 | Build the docker image: 8 | 9 | ```sh 10 | docker-compose build 11 | ``` 12 | 13 | Start the service: 14 | 15 | ```sh 16 | docker-compose up -d 17 | ``` 18 | 19 | This starts the web service at `localhost:5000` and some jupyter service at `localhost:8888` (to get the exact location enter `docker-compose logs app` and search the logs for the jupyter url). 20 | 21 | Apply object detection on a random cat: 22 | [http://localhost:5000/detect_objects](http://localhost:5000/detect_objects) 23 | 24 |

25 | 26 |

27 | 28 | 29 | You can also apply object detection to your own given image: 30 | 31 | [http://localhost:5000/detect_objects?url=$YOUR_URL](http://localhost:5000/detect_objects?url=$YOUR_URL) 32 | 33 | 34 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import re 2 | from io import BytesIO 3 | from flask import Flask, send_file, request 4 | from PIL import Image 5 | import requests 6 | import numpy as np 7 | import tensorflow as tf 8 | from object_detection.utils import label_map_util 9 | from object_detection.utils import visualization_utils as vis_util 10 | import os 11 | 12 | PATH_TO_CKPT = '/opt/tensorflow-models-object_detection/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb' 13 | 14 | detection_graph = tf.Graph() 15 | with detection_graph.as_default(): 16 | od_graph_def = tf.GraphDef() 17 | with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: 18 | serialized_graph = fid.read() 19 | od_graph_def.ParseFromString(serialized_graph) 20 | tf.import_graph_def(od_graph_def, name='') 21 | 22 | PATH_TO_LABELS = '/opt/tensorflow-models/object_detection/data/mscoco_label_map.pbtxt' 23 | NUM_CLASSES = 90 24 | 25 | label_map = label_map_util.load_labelmap(PATH_TO_LABELS) 26 | categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) 27 | category_index = label_map_util.create_category_index(categories) 28 | print('Loaded') 29 | 30 | app = Flask(__name__) 31 | 32 | def image2array(image): 33 | (w, h) = image.size 34 | return np.array(image.getdata()).reshape((h, w, 3)).astype(np.uint8) 35 | 36 | def array2image(arr): 37 | return Image.fromarray(np.uint8(arr)) 38 | 39 | def detect_objects(sess, image): 40 | '''Plots the object detection result for a given image.''' 41 | image_np = image2array(image) 42 | image_np_expanded = np.expand_dims(image_np, axis=0) 43 | 44 | image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') 45 | boxes = detection_graph.get_tensor_by_name('detection_boxes:0') 46 | scores = detection_graph.get_tensor_by_name('detection_scores:0') 47 | classes = detection_graph.get_tensor_by_name('detection_classes:0') 48 | num_detections = detection_graph.get_tensor_by_name('num_detections:0') 49 | 50 | (boxes, scores, classes, num_detections) = sess.run( 51 | [boxes, scores, classes, num_detections], 52 | feed_dict={image_tensor: image_np_expanded}) 53 | 54 | boxes = np.squeeze(boxes) 55 | classes = np.squeeze(classes).astype(np.int32) 56 | scores = np.squeeze(scores) 57 | 58 | vis_util.visualize_boxes_and_labels_on_image_array( 59 | image_np, 60 | boxes, 61 | classes, 62 | scores, 63 | category_index, 64 | use_normalized_coordinates=True, 65 | line_thickness=8) 66 | return array2image(image_np) 67 | 68 | @app.route('/detect_objects') 69 | def detect(): 70 | default_url = 'http://thecatapi.com/api/images/get?format=src&type=jpg' 71 | url = request.args.get('url', default_url) 72 | r = requests.get(url) 73 | image = Image.open(BytesIO(r.content)) 74 | with detection_graph.as_default(): 75 | with tf.Session(graph=detection_graph) as sess: 76 | image = detect_objects(sess, image) 77 | 78 | byte_io = BytesIO() 79 | image.save(byte_io, 'JPEG') 80 | byte_io.seek(0) 81 | 82 | return send_file(byte_io, mimetype='image/jpeg') 83 | 84 | if __name__ == '__main__': 85 | app.run(debug=True) 86 | -------------------------------------------------------------------------------- /doc/cat_detections_output.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/phipleg/tensorflow-object-detector/941bff2f552077acb2d83090d221da49476bb6b0/doc/cat_detections_output.jpg -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '3' 2 | services: 3 | app: 4 | build: 5 | context: . 6 | ports: 7 | - 8888:8888 8 | - 5000:5000 9 | volumes: 10 | - .:/app 11 | -------------------------------------------------------------------------------- /docker/service/app/run: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | pushd /app 4 | FLASK_APP=app.py FLASK_DEBUG=True exec flask run --host=0.0.0.0 5 | popd 6 | -------------------------------------------------------------------------------- /docker/service/jupyter/run: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | pushd /app 4 | exec jupyter notebook --ip=0.0.0.0 --allow-root 5 | popd 6 | -------------------------------------------------------------------------------- /test_images/carrot-kale-walnuts-tomatoes.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/phipleg/tensorflow-object-detector/941bff2f552077acb2d83090d221da49476bb6b0/test_images/carrot-kale-walnuts-tomatoes.jpeg -------------------------------------------------------------------------------- /test_images/pexels-photo-297755.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/phipleg/tensorflow-object-detector/941bff2f552077acb2d83090d221da49476bb6b0/test_images/pexels-photo-297755.jpeg -------------------------------------------------------------------------------- /test_images/pexels-photo-342214.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/phipleg/tensorflow-object-detector/941bff2f552077acb2d83090d221da49476bb6b0/test_images/pexels-photo-342214.jpeg --------------------------------------------------------------------------------