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This demo is an updated from the one about reverse image search based on Milvus and VGG. We replace the original image feature extraction model, VGG, with ResNet-50 to perform target detection for images and build a reverse image search system based on Milvus.
Below is the system architecture.
The following tables show recommended configurations for reverse image search. These configurations haven been tested.
| Component | Recommended Configuration |
|---|---|
| CPU | Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz |
| Memory | 32GB |
| OS | Ubuntu 18.04 |
| Software | Milvus 0.10.5 pic_search_webclient 0.2.0 |
This demo uses PASCAL VOC image set, which contains 17125 images that can be divided into 20 categories: human, animals (birds, cats, cows, dogs, horses, sheep, and more), means of transport (planes, bikes, boats, buses, cars, motorcycles, trains, and more), furniture and household appliances (bottles, chairs, tables, potted plants, sofas, TVs, and more)
Dataset size: ~ 2 GB.
Url for download: https://pan.baidu.com/s/1MjACqsGiLo3oiTMcxodtdA extraction code: v78m
Note: You can also use other images for testing. This system supports images in formats of .jpg and .png.
This demo uses Milvus 0.10.5. Refer to the Install Milvus for how to run Milvus docker.
cd /image_search/webserver
pip install -r requirements.txt
vim /image_search_v2/webserver/src/common/config.py
Need to modify the milvus port or milvus ip parameters corresponding to the Milvus installation
| Parameter | Description | Default setting |
|---|---|---|
| MILVUS_HOST | milvus service ip | 127.0.0.1 |
| MILVUS_PORT | milvus service port | 19512 |
| VECTOR_DIMENSION | Dimensionality of the vectors | 2048 |
| DATA_PATH | Path to save images | /data/jpegimages |
| DEFAULT_TABLE | The milvus default collection | milvus_183 |
| UPLOAD_PATH | Storage path for uploaded images | /tmp/search-images |
| COCO_MODEL_PATH | Path of the target detection model | /yolov3_detector/data/yolov3_darknet |
| YOLO_CONFIG_PATH | Profile path for target detection model | /yolov3_detector/data/yolov3_darknet/yolo.yml |
cd /image_search/webserver/src
python app.py
If your yolo model is not downloaded automatically, you need to go to the image_search_v2/webserver/src/yolov3_detector/data/ path and run the paprepare_model.sh script
$ docker run --name zilliz_search_images_demo_web -d --rm -p 8001:80 \
-e API_URL=http://${WEBSERVER_IP}:5000 \
milvusbootcamp/pic-search-webclient:0.2.0In the previous command line, WEBSERVER_IP specifies the server IP address that runs pic-search-webserver docker.
After deployment, enter ${WEBCLIENT_IP}:8001 in the browser to open the interface for reverse image search. WEBCLIENT_IP specifies the server IP address that runs pic-search-webclient docker.
Enter the path to the images folder, e.g. /data/images. Click Load to load the images. The following screenshot shows the loading process.
Note: After clicking the Load button, you need to wait for a while for the system to respond. Please do not make mulitple clicks.
It may take several minutes to load the images. The following screenshot shows the interface when image loading is successful.
Select the image you want to search for.
When tested with the recommended configuration, the reverse image search in the system can be done in a few seconds. To load images from other directories, specify the path in the text box.