Delete test_cpp_server_pipeline.py

examples/C++/PaddleClas/pp_shitu/test_cpp_server_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)
-
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