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

Modify code structure

python/fastdeploy/vision/evaluation/utils/coco.py

@@ -0,0 +1,178 @@
+# 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 __future__ import absolute_import
+import copy
+import os.path as osp
+import sys
+import numpy as np
+from . import fd_logging as logging
+from .util import is_pic, get_num_workers
+
+
+class CocoDetection(object):
+    """读取MSCOCO格式的检测数据集，并对样本进行相应的处理，该格式的数据集同样可以应用到实例分割模型的训练中。
+
+    Args:
+        data_dir (str): 数据集所在的目录路径。
+        ann_file (str): 数据集的标注文件，为一个独立的json格式文件。
+        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
+            系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的一半。
+        shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
+        allow_empty (bool): 是否加载负样本。默认为False。
+        empty_ratio (float): 用于指定负样本占总样本数的比例。如果小于0或大于等于1，则保留全部的负样本。默认为1。
+    """
+
+    def __init__(self,
+                 data_dir,
+                 ann_file,
+                 num_workers='auto',
+                 shuffle=False,
+                 allow_empty=False,
+                 empty_ratio=1.):
+
+        from pycocotools.coco import COCO
+        self.data_dir = data_dir
+        self.data_fields = None
+        self.num_max_boxes = 1000
+        self.num_workers = get_num_workers(num_workers)
+        self.shuffle = shuffle
+        self.allow_empty = allow_empty
+        self.empty_ratio = empty_ratio
+        self.file_list = list()
+        neg_file_list = list()
+        self.labels = list()
+
+        coco = COCO(ann_file)
+        self.coco_gt = coco
+        img_ids = sorted(coco.getImgIds())
+        cat_ids = coco.getCatIds()
+        catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
+        cname2clsid = dict({
+            coco.loadCats(catid)[0]['name']: clsid
+            for catid, clsid in catid2clsid.items()
+        })
+        for label, cid in sorted(cname2clsid.items(), key=lambda d: d[1]):
+            self.labels.append(label)
+        logging.info("Starting to read file list from dataset...")
+
+        ct = 0
+        for img_id in img_ids:
+            is_empty = False
+            img_anno = coco.loadImgs(img_id)[0]
+            im_fname = osp.join(data_dir, img_anno['file_name'])
+            if not is_pic(im_fname):
+                continue
+            im_w = float(img_anno['width'])
+            im_h = float(img_anno['height'])
+            ins_anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=False)
+            instances = coco.loadAnns(ins_anno_ids)
+
+            bboxes = []
+            for inst in instances:
+                x, y, box_w, box_h = inst['bbox']
+                x1 = max(0, x)
+                y1 = max(0, y)
+                x2 = min(im_w - 1, x1 + max(0, box_w))
+                y2 = min(im_h - 1, y1 + max(0, box_h))
+                if inst['area'] > 0 and x2 >= x1 and y2 >= y1:
+                    inst['clean_bbox'] = [x1, y1, x2, y2]
+                    bboxes.append(inst)
+                else:
+                    logging.warning(
+                        "Found an invalid bbox in annotations: "
+                        "im_id: {}, area: {} x1: {}, y1: {}, x2: {}, y2: {}."
+                        .format(img_id, float(inst['area']), x1, y1, x2, y2))
+            num_bbox = len(bboxes)
+            if num_bbox == 0 and not self.allow_empty:
+                continue
+            elif num_bbox == 0:
+                is_empty = True
+
+            gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
+            gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
+            gt_score = np.ones((num_bbox, 1), dtype=np.float32)
+            is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
+            difficult = np.zeros((num_bbox, 1), dtype=np.int32)
+            gt_poly = [None] * num_bbox
+
+            has_segmentation = False
+            for i, box in reversed(list(enumerate(bboxes))):
+                catid = box['category_id']
+                gt_class[i][0] = catid2clsid[catid]
+                gt_bbox[i, :] = box['clean_bbox']
+                is_crowd[i][0] = box['iscrowd']
+                if 'segmentation' in box and box['iscrowd'] == 1:
+                    gt_poly[i] = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
+                elif 'segmentation' in box and box['segmentation']:
+                    if not np.array(
+                            box['segmentation'],
+                            dtype=object).size > 0 and not self.allow_empty:
+                        gt_poly.pop(i)
+                        is_crowd = np.delete(is_crowd, i)
+                        gt_class = np.delete(gt_class, i)
+                        gt_bbox = np.delete(gt_bbox, i)
+                    else:
+                        gt_poly[i] = box['segmentation']
+                    has_segmentation = True
+            if has_segmentation and not any(gt_poly) and not self.allow_empty:
+                continue
+
+            im_info = {
+                'im_id': np.array([img_id]).astype('int32'),
+                'image_shape': np.array([im_h, im_w]).astype('int32'),
+            }
+            label_info = {
+                'is_crowd': is_crowd,
+                'gt_class': gt_class,
+                'gt_bbox': gt_bbox,
+                'gt_score': gt_score,
+                'gt_poly': gt_poly,
+                'difficult': difficult
+            }
+
+            if is_empty:
+                neg_file_list.append({
+                    'image': im_fname,
+                    **
+                    im_info,
+                    **
+                    label_info
+                })
+            else:
+                self.file_list.append({
+                    'image': im_fname,
+                    **
+                    im_info,
+                    **
+                    label_info
+                })
+            ct += 1
+
+            self.num_max_boxes = max(self.num_max_boxes, len(instances))
+
+        if not ct:
+            logging.error(
+                "No coco record found in %s' % (ann_file)", exit=True)
+        self.pos_num = len(self.file_list)
+        if self.allow_empty and neg_file_list:
+            self.file_list += self._sample_empty(neg_file_list)
+        logging.info(
+            "{} samples in file {}, including {} positive samples and {} negative samples.".
+            format(
+                len(self.file_list), ann_file, self.pos_num,
+                len(self.file_list) - self.pos_num))
+        self.num_samples = len(self.file_list)
+
+        self._epoch = 0