add README.md

core/general-server/op/CMakeLists.txt

@@ -5,7 +5,7 @@ if(WITH_OPENCV)
     LIST(APPEND op_srcs ${ocrtools_srcs})
 else()
     set (EXCLUDE_DIR "general_detection_op.cpp")
-    set (EXCLUDE_DIR "general_infer_op.cpp")
+    set (EXCLUDE_DIR "general_picodet_op.cpp")
     set (EXCLUDE_DIR "general_feature_extract_op.cpp")
     foreach (TMP_PATH ${op_srcs})
         string (FIND ${TMP_PATH} ${EXCLUDE_DIR} EXCLUDE_DIR_FOUND)

examples/C++/PaddleClas/pp_shitu/README.md

+# PP-Shitu
+
+## Get Model
+```
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/serving/pp_shitu.tar.gz
+tar -xzvf pp_shitu.tar.gz
+```
+
+## Get test images and index
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
+tar -xvf drink_dataset_v1.0.tar
+
+## RPC Service
+### Start Service
+```
+sh run_cpp_serving.sh
+```
+
+### Client Prediction
+```
+python3 test_cpp_serving_pipeline.py ./drint_dataset_v1.0/test_images/nongfu_spring.jpeg
+```
\ No newline at end of file

examples/C++/PaddleClas/pp_shitu/README_CN.md

+# PP-Shitu
+
+## 获取模型
+```
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/serving/pp_shitu.tar.gz
+tar -xzvf pp_shitu.tar.gz
+```
+
+## 获取测试图像和index
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
+tar -xvf drink_dataset_v1.0.tar
+
+## RPC 服务
+### 启动服务端
+```
+sh run_cpp_serving.sh
+```
+
+### 客户端预测
+```
+python3 test_cpp_serving_pipeline.py ./drint_dataset_v1.0/test_images/nongfu_spring.jpeg
+```

examples/C++/PaddleClas/pp_shitu/test_cpp_serving_pipeline.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import numpy as np
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import *
+import cv2
+import faiss
+import os
+import pickle
+
+rec_nms_thresold = 0.05
+rec_score_thres = 0.5
+feature_normalize = True
+return_k = 1
+index_dir = "./drink_dataset_v1.0/index"
+
+def init_index(index_dir):
+    assert os.path.exists(os.path.join(
+        index_dir, "vector.index")), "vector.index not found ..."
+    assert os.path.exists(os.path.join(
+        index_dir, "id_map.pkl")), "id_map.pkl not found ... "
+
+    searcher = faiss.read_index(
+        os.path.join(index_dir, "vector.index"))
+
+    with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
+        id_map = pickle.load(fd)
+    return searcher, id_map
+
+#get box
+def nms_to_rec_results(results, thresh=0.1):
+    filtered_results = []
+
+    x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
+    y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
+    x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
+    y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
+    scores = np.array([r["rec_scores"] for r in results])
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+    while order.size > 0:
+        i = order[0]
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+        filtered_results.append(results[i])
+    return filtered_results
+
+def postprocess(fetch_dict,
+                feature_normalize, 
+                det_boxes,
+                searcher,
+                id_map,
+                return_k,
+                rec_score_thres,
+                rec_nms_thresold):
+    batch_features = fetch_dict["features"]
+
+    #do feature norm
+    if feature_normalize:
+        feas_norm = np.sqrt(
+            np.sum(np.square(batch_features), axis=1, keepdims=True))
+        batch_features = np.divide(batch_features, feas_norm)
+
+    scores, docs = searcher.search(batch_features, return_k)
+
+    results = []
+    for i in range(scores.shape[0]):
+        pred = {}
+        if scores[i][0] >= rec_score_thres:
+            pred["bbox"] = [int(x) for x in det_boxes[i,2:]]
+            pred["rec_docs"] = id_map[docs[i][0]].split()[1]
+            pred["rec_scores"] = scores[i][0]
+            results.append(pred)
+
+    #do nms
+    results = nms_to_rec_results(results, rec_nms_thresold)
+    return results
+
+#do client
+if __name__=="__main__":
+    client = Client()
+    client.load_client_config(["picodet_PPLCNet_x2_5_mainbody_lite_v2.0_client", "general_PPLCNet_x2_5_lite_v2.0_client"])
+    client.connect(['127.0.0.1:9400'])
+
+    im = cv2.imread(sys.argv[1])
+    im_shape = np.array(im.shape[:2]).reshape(-1)
+    fetch_map = client.predict(
+        feed={
+            "image": im,
+            "im_shape": im_shape
+        },
+        fetch=["features", "boxes"],
+        batch=False)
+
+    #add retrieval procedure
+    det_boxes = fetch_map["boxes"]
+    searcher, id_map = init_index(index_dir)
+    results = postprocess(fetch_map, feature_normalize, det_boxes, searcher, id_map, return_k, rec_score_thres, rec_nms_thresold)
+    print(results)
+
+
+
+
+    
+    
+
+    
+
+