Modify file structure to separate python and cpp code (#223)

Modify code structure

python/fastdeploy/vision/evaluation/segmentation.py

@@ -0,0 +1,78 @@
+# Copyright (c) 2022 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.
+
+from tqdm import trange
+import numpy as np
+import collections
+import os
+import math
+import time
+
+
+def eval_segmentation(model, data_dir):
+    import cv2
+    from .utils import Cityscapes
+    from .utils import f1_score, calculate_area, mean_iou, accuracy, kappa
+    assert os.path.isdir(
+        data_dir), "The image_file_path:{} is not a directory.".format(
+            data_dir)
+    eval_dataset = Cityscapes(dataset_root=data_dir, mode="val")
+    file_list = eval_dataset.file_list
+    image_num = eval_dataset.num_samples
+    num_classes = eval_dataset.num_classes
+    intersect_area_all = 0
+    pred_area_all = 0
+    label_area_all = 0
+    conf_mat_all = []
+    twenty_percent_image_num = math.ceil(image_num * 0.2)
+    start_time = 0
+    end_time = 0
+    average_inference_time = 0
+    for image_label_path, i in zip(file_list,
+                                   trange(
+                                       image_num, desc="Inference Progress")):
+        if i == twenty_percent_image_num:
+            start_time = time.time()
+        im = cv2.imread(image_label_path[0])
+        label = cv2.imread(image_label_path[1], cv2.IMREAD_GRAYSCALE)
+        result = model.predict(im)
+        if i == image_num - 1:
+            end_time = time.time()
+        average_inference_time = round(
+            (end_time - start_time) / (image_num - twenty_percent_image_num),
+            4)
+        pred = np.array(result.label_map).reshape(result.shape[0],
+                                                  result.shape[1])
+        intersect_area, pred_area, label_area = calculate_area(pred, label,
+                                                               num_classes)
+        intersect_area_all = intersect_area_all + intersect_area
+        pred_area_all = pred_area_all + pred_area
+        label_area_all = label_area_all + label_area
+
+    class_iou, miou = mean_iou(intersect_area_all, pred_area_all,
+                               label_area_all)
+    class_acc, oacc = accuracy(intersect_area_all, pred_area_all)
+    kappa_res = kappa(intersect_area_all, pred_area_all, label_area_all)
+    category_f1score = f1_score(intersect_area_all, pred_area_all,
+                                label_area_all)
+
+    eval_metrics = collections.OrderedDict(
+        zip([
+            'miou', 'category_iou', 'oacc', 'category_acc', 'kappa',
+            'category_F1-score', 'average_inference_time(s)'
+        ], [
+            miou, class_iou, oacc, class_acc, kappa_res, category_f1score,
+            average_inference_time
+        ]))
+    return eval_metrics