Merge pull request #1046 from shiyu22/towhee0.6

Update to Milvus2.0.2 and Towhee0.6

solutions/reverse_image_search/object_detection/README.md

@@ -2,7 +2,7 @@
 
 ## Overview
 
-This demo uses the [towhee](https://github.com/towhee-io/towhee) pipelines to detect objects in images and extract feature vectors of images, and then uses Milvus to build an image similarity search system.
+This demo uses the [towhee](https://github.com/towhee-io/towhee) operators to detect objects in images and extract feature vectors of images, and then uses Milvus to build an image similarity search system.
 
 The following is the system diagram.
 
@@ -61,7 +61,7 @@ And show all containers with `docker ps`, and you can use `docker logs img-searc
 
 ```bash
 CONTAINER ID   IMAGE                                         COMMAND                  CREATED          STATUS                             PORTS                               NAMES
-4cc6e60eb295   milvusbootcamp/imgsearch-with-objdet:towhee   "/bin/sh -c 'python3…"   56 seconds ago   Up 55 seconds                      0.0.0.0:5010->5010/tcp              img-obj-det-webserver
+4cc6e60eb295   milvusbootcamp/imgsearch-with-objdet:towhee   "/bin/sh -c 'python3…"   56 seconds ago   Up 55 seconds                      0.0.0.0:5000->5000/tcp              img-obj-det-webserver
 40f4ea99fd22   milvusdb/milvus:v2.0.0-rc8-20211104-d1f4106   "/tini -- milvus run…"   57 seconds ago   Up 55 seconds                      0.0.0.0:19530->19530/tcp            milvus-standalone
 60ed080afac1   minio/minio:RELEASE.2020-12-03T00-03-10Z      "/usr/bin/docker-ent…"   57 seconds ago   Up 56 seconds (healthy)            9000/tcp                            milvus-minio
 5d9cdfba872b   mysql:5.7                                     "docker-entrypoint.s…"   57 seconds ago   Up 56 seconds                      0.0.0.0:3306->3306/tcp, 33060/tcp   img-obj-det-mysql
@@ -122,7 +122,7 @@ $ python main.py
 
 - **API docs**
 
-Visit 127.0.0.1:5010/docs in your browser to use all the APIs.
+Visit `127.0.0.1:5000/docs` in your browser to use all the APIs.
 
 ![fastapi](pic/fastapi.png)
 
@@ -144,7 +144,7 @@ Visit 127.0.0.1:5010/docs in your browser to use all the APIs.
 
 ```bash
 # Modify API_URL to the IP address and port of the server.
-$ export API_URL='http://127.0.0.1:5010'
+$ export API_URL='http://127.0.0.1:5000'
 $ docker run -d -p 8001:80 \
 -e API_URL=${API_URL} \
 milvusbootcamp/img-search-client:1.0

solutions/reverse_image_search/object_detection/docker-compose.yaml

@@ -33,7 +33,7 @@ services:
 
   standalone:
     container_name: milvus-standalone
-    image: milvusdb/milvus:v2.0.0-rc8-20211104-d1f4106
+    image: milvusdb/milvus:v2.0.2
     networks:
       app_net:
         ipv4_address: 172.16.238.10
@@ -62,7 +62,7 @@ services:
 
   webserver:
     container_name: img-obj-det-webserver
-    image: milvusbootcamp/imgsearch-with-objdet:towhee
+    image: milvusbootcamp/img-search-with-object:towhee0.6
     networks:
       app_net:
         ipv4_address: 172.16.238.12
@@ -76,7 +76,7 @@ services:
       - standalone
       - mysql
     ports:
-      - "5010:5010"
+      - "5000:5000"
 
   webclient:
     container_name: img-obj-det-webclient
@@ -85,7 +85,7 @@ services:
       app_net:
         ipv4_address: 172.16.238.13
     environment:
-      API_URL: 'http://127.0.0.1:5010'
+      API_URL: 'http://127.0.0.1:5000'
     ports:
       - "8001:80"
     healthcheck:

solutions/reverse_image_search/object_detection/server/requirements.txt

@@ -1,4 +1,4 @@
-pymilvus==2.0.1
+pymilvus==2.0.2
 diskcache==5.2.1
 uvicorn==0.13.4
 PyMySQL==1.0.2
@@ -14,4 +14,7 @@ torch==1.9.0
 pillow==9.0.1
 matplotlib
 seaborn
-towhee==0.2.0
+towhee==0.6.0
+scipy
+opencv-python
+pyyaml

solutions/reverse_image_search/object_detection/server/src/config.py

@@ -3,7 +3,7 @@
 ############### Milvus Configuration ###############
 MILVUS_HOST = os.getenv("MILVUS_HOST", "127.0.0.1")
 MILVUS_PORT = int(os.getenv("MILVUS_PORT", "19530"))
-VECTOR_DIMENSION = int(os.getenv("VECTOR_DIMENSION", "1000"))
+VECTOR_DIMENSION = int(os.getenv("VECTOR_DIMENSION", "2048"))
 INDEX_FILE_SIZE = int(os.getenv("INDEX_FILE_SIZE", "1024"))
 METRIC_TYPE = os.getenv("METRIC_TYPE", "L2")
 DEFAULT_TABLE = os.getenv("DEFAULT_TABLE", "milvus_obj_det")

solutions/reverse_image_search/object_detection/server/src/encode.py

@@ -1,25 +1,33 @@
-from towhee import pipeline
+import towhee
+import cv2
 from numpy import linalg as LA
 
-# os.environ['KERAS_HOME'] = os.path.abspath(os.path.join('.', 'data'))
-
 
 class CustomOperator:
-    """
-    Say something about the ExampleCalass...
-
-    Args:
-        args_0 (`type`):
-        ...
-    """
-    def __init__(self):
-        #self.img_embedding = pipeline('image-embedding')
-        self.yolo_embedding = pipeline('shiyu/img_object_embedding_pytorch_yolov5_resnet50')
-
     def execute(self, img_path):
-        objs = self.yolo_embedding(img_path)
+        boxes, _, _ = towhee.glob(img_path) \
+	        .image_decode() \
+	        .object_detection.yolov5() \
+	        .to_list()[0]
+
+        imgs = self.get_imgs_list(img_path, boxes)
+        embeddings = towhee.dc(imgs) \
+                .image_embedding.timm(model_name='resnet50') \
+                .to_list()
+
         norm_objs = []
-        for feat in objs:
-            norm_feat = feat[0] / LA.norm(feat[0])
+        for feat in embeddings:
+            norm_feat = feat / LA.norm(feat)
             norm_objs.append(norm_feat.tolist())
         return norm_objs
+
+
+    @staticmethod
+    def get_imgs_list(img_path, boxes):
+        img_list = []
+        img = cv2.imread(img_path)
+
+        for box in boxes:
+            tmp_obj = img[int(box[1]):int(box[3]), int(box[0]):int(box[2])]
+            img_list.append(towhee._types.Image(tmp_obj, 'BGR'))
+        return img_list

solutions/reverse_image_search/object_detection/server/src/main.py

@@ -123,4 +123,4 @@ async def search_images(image: UploadFile = File(...), table_name: str = None):
 
 
 if __name__ == '__main__':
-    uvicorn.run(app=app, host='0.0.0.0', port=5010)
+    uvicorn.run(app=app, host='0.0.0.0', port=5000)