Add data for unittests

tests/data/README.md

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+# Testing Data
+
+This directory stores real samples that can be used for testing.
+
+*ssmt* means single-source-multi-temporal and *ssst* means single-source-single-temporal.

tests/data/__init__.py

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+# 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 .data_utils import *

tests/data/data_utils.py

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+# 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.
+
+import os.path as osp
+import re
+import imghdr
+import platform
+from collections import OrderedDict
+from functools import partial
+
+import numpy as np
+
+__all__ = ['build_input_from_file']
+
+
+def norm_path(path):
+    win_sep = "\\"
+    other_sep = "/"
+    if platform.system() == "Windows":
+        path = win_sep.join(path.split(other_sep))
+    else:
+        path = other_sep.join(path.split(win_sep))
+    return path
+
+
+def is_pic(im_path):
+    valid_suffix = [
+        'JPEG', 'jpeg', 'JPG', 'jpg', 'BMP', 'bmp', 'PNG', 'png', 'npy'
+    ]
+    suffix = im_path.split('.')[-1]
+    if suffix in valid_suffix:
+        return True
+    im_format = imghdr.what(im_path)
+    _, ext = osp.splitext(im_path)
+    if im_format == 'tiff' or ext == '.img':
+        return True
+    return False
+
+
+def get_full_path(p, prefix=''):
+    p = norm_path(p)
+    return osp.join(prefix, p)
+
+
+class ConstrSample(object):
+    def __init__(self, prefix, label_list):
+        super().__init__()
+        self.prefix = prefix
+        self.label_list_obj = self.read_label_list(label_list)
+        self.get_full_path = partial(get_full_path, prefix=self.prefix)
+
+    def read_label_list(self, label_list):
+        if label_list is None:
+            return None
+        cname2cid = OrderedDict()
+        label_id = 0
+        with open(label_list, 'r') as f:
+            for line in f:
+                cname2cid[line.strip()] = label_id
+                label_id += 1
+        return cname2cid
+
+    def __call__(self, *parts):
+        raise NotImplementedError
+
+
+class ConstrSegSample(ConstrSample):
+    def __call__(self, im_path, mask_path):
+        return {
+            'image': self.get_full_path(im_path),
+            'mask': self.get_full_path(mask_path)
+        }
+
+
+class ConstrCdSample(ConstrSample):
+    def __call__(self, im1_path, im2_path, mask_path, *aux_mask_paths):
+        sample = {
+            'image_t1': self.get_full_path(im1_path),
+            'image_t2': self.get_full_path(im2_path),
+            'mask': self.get_full_path(mask_path)
+        }
+        if len(aux_mask_paths) > 0:
+            sample['aux_masks'] = [
+                self.get_full_path(p) for p in aux_mask_paths
+            ]
+        return sample
+
+
+class ConstrClasSample(ConstrSample):
+    def __call__(self, im_path, label):
+        return {'image': self.get_full_path(im_path), 'label': int(label)}
+
+
+class ConstrDetSample(ConstrSample):
+    def __init__(self, prefix, label_list):
+        super().__init__(prefix, label_list)
+        self.ct = 0
+
+    def __call__(self, im_path, ann_path):
+        im_path = self.get_full_path(im_path)
+        ann_path = self.get_full_path(ann_path)
+        # TODO: Precisely recognize the annotation format
+        if ann_path.endswith('.json'):
+            im_dir = im_path
+            return self._parse_coco_files(im_dir, ann_path)
+        elif ann_path.endswith('.xml'):
+            return self._parse_voc_files(im_path, ann_path)
+        else:
+            raise ValueError("Cannot recognize the annotation format")
+
+    def _parse_voc_files(self, im_path, ann_path):
+        import xml.etree.ElementTree as ET
+
+        cname2cid = self.label_list_obj
+        tree = ET.parse(ann_path)
+        # The xml file must contain id.
+        if tree.find('id') is None:
+            im_id = np.asarray([self.ct])
+        else:
+            self.ct = int(tree.find('id').text)
+            im_id = np.asarray([int(tree.find('id').text)])
+        pattern = re.compile('<size>', re.IGNORECASE)
+        size_tag = pattern.findall(str(ET.tostringlist(tree.getroot())))
+        if len(size_tag) > 0:
+            size_tag = size_tag[0][1:-1]
+            size_element = tree.find(size_tag)
+            pattern = re.compile('<width>', re.IGNORECASE)
+            width_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][
+                1:-1]
+            im_w = float(size_element.find(width_tag).text)
+            pattern = re.compile('<height>', re.IGNORECASE)
+            height_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][
+                1:-1]
+            im_h = float(size_element.find(height_tag).text)
+        else:
+            im_w = 0
+            im_h = 0
+
+        pattern = re.compile('<object>', re.IGNORECASE)
+        obj_match = pattern.findall(str(ET.tostringlist(tree.getroot())))
+        if len(obj_match) > 0:
+            obj_tag = obj_match[0][1:-1]
+            objs = tree.findall(obj_tag)
+        else:
+            objs = list()
+
+        num_bbox, i = len(objs), 0
+        gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
+        gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
+        gt_score = np.zeros((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)
+        for obj in objs:
+            pattern = re.compile('<name>', re.IGNORECASE)
+            name_tag = pattern.findall(str(ET.tostringlist(obj)))[0][1:-1]
+            cname = obj.find(name_tag).text.strip()
+            pattern = re.compile('<difficult>', re.IGNORECASE)
+            diff_tag = pattern.findall(str(ET.tostringlist(obj)))
+            if len(diff_tag) == 0:
+                _difficult = 0
+            else:
+                diff_tag = diff_tag[0][1:-1]
+                try:
+                    _difficult = int(obj.find(diff_tag).text)
+                except Exception:
+                    _difficult = 0
+            pattern = re.compile('<bndbox>', re.IGNORECASE)
+            box_tag = pattern.findall(str(ET.tostringlist(obj)))
+            if len(box_tag) == 0:
+                continue
+            box_tag = box_tag[0][1:-1]
+            box_element = obj.find(box_tag)
+            pattern = re.compile('<xmin>', re.IGNORECASE)
+            xmin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
+                                                                             -1]
+            x1 = float(box_element.find(xmin_tag).text)
+            pattern = re.compile('<ymin>', re.IGNORECASE)
+            ymin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
+                                                                             -1]
+            y1 = float(box_element.find(ymin_tag).text)
+            pattern = re.compile('<xmax>', re.IGNORECASE)
+            xmax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
+                                                                             -1]
+            x2 = float(box_element.find(xmax_tag).text)
+            pattern = re.compile('<ymax>', re.IGNORECASE)
+            ymax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
+                                                                             -1]
+            y2 = float(box_element.find(ymax_tag).text)
+            x1 = max(0, x1)
+            y1 = max(0, y1)
+            if im_w > 0.5 and im_h > 0.5:
+                x2 = min(im_w - 1, x2)
+                y2 = min(im_h - 1, y2)
+
+            if not (x2 >= x1 and y2 >= y1):
+                continue
+
+            gt_bbox[i, :] = [x1, y1, x2, y2]
+            gt_class[i, 0] = cname2cid[cname]
+            gt_score[i, 0] = 1.
+            is_crowd[i, 0] = 0
+            difficult[i, 0] = _difficult
+            i += 1
+
+        gt_bbox = gt_bbox[:i, :]
+        gt_class = gt_class[:i, :]
+        gt_score = gt_score[:i, :]
+        is_crowd = is_crowd[:i, :]
+        difficult = difficult[:i, :]
+
+        im_info = {
+            'im_id': im_id,
+            'image_shape': np.array(
+                [im_h, im_w], dtype=np.int32)
+        }
+        label_info = {
+            'is_crowd': is_crowd,
+            'gt_class': gt_class,
+            'gt_bbox': gt_bbox,
+            'gt_score': gt_score,
+            'difficult': difficult
+        }
+
+        self.ct += 1
+        return {'image': im_path, ** im_info, ** label_info}
+
+    def _parse_coco_files(self, im_dir, ann_path):
+        from pycocotools.coco import COCO
+
+        coco = COCO(ann_path)
+        img_ids = coco.getImgIds()
+        img_ids.sort()
+
+        samples = []
+        for img_id in img_ids:
+            img_anno = coco.loadImgs([img_id])[0]
+            im_fname = img_anno['file_name']
+            im_w = float(img_anno['width'])
+            im_h = float(img_anno['height'])
+
+            im_path = osp.join(im_dir, im_fname) if im_dir else im_fname
+
+            im_info = {
+                'image': im_path,
+                'im_id': np.array([img_id]),
+                'image_shape': np.array(
+                    [im_h, im_w], dtype=np.int32)
+            }
+
+            ins_anno_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=False)
+            instances = coco.loadAnns(ins_anno_ids)
+
+            is_crowds = []
+            gt_classes = []
+            gt_bboxs = []
+            gt_scores = []
+            difficults = []
+
+            for inst in instances:
+                # Check gt bbox
+                if inst.get('ignore', False):
+                    continue
+                if 'bbox' not in inst.keys():
+                    continue
+                else:
+                    if not any(np.array(inst['bbox'])):
+                        continue
+
+                # Read box
+                x1, y1, box_w, box_h = inst['bbox']
+                x2 = x1 + box_w
+                y2 = y1 + box_h
+                eps = 1e-5
+                if inst['area'] > 0 and x2 - x1 > eps and y2 - y1 > eps:
+                    inst['clean_bbox'] = [
+                        round(float(x), 3) for x in [x1, y1, x2, y2]
+                    ]
+
+                is_crowds.append([inst['iscrowd']])
+                gt_classes.append([inst['category_id']])
+                gt_bboxs.append(inst['clean_bbox'])
+                gt_scores.append([1.])
+                difficults.append([0])
+
+            label_info = {
+                'is_crowd': np.array(is_crowds),
+                'gt_class': np.array(gt_classes),
+                'gt_bbox': np.array(gt_bboxs).astype(np.float32),
+                'gt_score': np.array(gt_scores).astype(np.float32),
+                'difficult': np.array(difficults),
+            }
+
+            samples.append({ ** im_info, ** label_info})
+
+        return samples
+
+
+def build_input_from_file(file_list, prefix='', task='auto', label_list=None):
+    """
+    Construct a list of dictionaries from file. Each dict in the list can be used as the input to `paddlers.transforms.Transform` objects.
+
+    Args:
+        file_list (str): Path of file_list.
+        prefix (str, optional): A nonempty `prefix` specifies the directory that stores the images and annotation files. Default: ''.
+        task (str, optional): Supported values are 'seg', 'det', 'cd', 'clas', and 'auto'. When `task` is set to 'auto', automatically determine the task based on the input. 
+            Default: 'auto'.
+        label_list (str | None, optional): Path of label_list. Default: None.
+
+    Returns:
+        list: List of samples.
+    """
+
+    def _determine_task(parts):
+        if len(parts) in (3, 5):
+            task = 'cd'
+        elif len(parts) == 2:
+            if parts[1].isdigit():
+                task = 'clas'
+            elif is_pic(osp.join(prefix, parts[1])):
+                task = 'seg'
+            else:
+                task = 'det'
+        else:
+            raise RuntimeError(
+                "Cannot automatically determine the task type. Please specify `task` manually."
+            )
+        return task
+
+    if task not in ('seg', 'det', 'cd', 'clas', 'auto'):
+        raise ValueError("Invalid value of `task`")
+
+    samples = []
+    ctor = None
+    with open(file_list, 'r') as f:
+        for line in f:
+            line = line.strip()
+            parts = line.split()
+            if task == 'auto':
+                task = _determine_task(parts)
+            if ctor is None:
+                # Select and build sample constructor
+                ctor_class = globals()['Constr' + task.capitalize() + 'Sample']
+                ctor = ctor_class(prefix, label_list)
+            sample = ctor(*parts)
+            if isinstance(sample, list):
+                samples.extend(sample)
+            else:
+                samples.append(sample)
+
+    return samples

tests/data/ssmt/test_mixed_binary.txt

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+optical_t1.bmp optical_t2.bmp binary_gt.bmp
+sar_t1.tiff sar_t2.tiff binary_gt.bmp
+multispectral_t1.tif multispectral_t2.tif binary_gt.bmp

tests/data/ssmt/test_mixed_multiclass.txt

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+optical_t1.bmp optical_t2.bmp multiclass_gt.png
+sar_t1.tiff sar_t2.tiff multiclass_gt.png
+multispectral_t1.tif multispectral_t2.tif multiclass_gt.png
+optical_t1.bmp optical_t2.bmp multiclass_gt2.png
+sar_t1.tiff sar_t2.tiff multiclass_gt2.png
+multispectral_t1.tif multispectral_t2.tif multiclass_gt2.png

tests/data/ssmt/test_mixed_multitask.txt

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+optical_t1.bmp optical_t2.bmp binary_gt.bmp binary_gt.bmp binary_gt.bmp
+sar_t1.tiff sar_t2.tiff binary_gt.bmp binary_gt.bmp binary_gt.bmp
+multispectral_t1.tif multispectral_t2.tif binary_gt.bmp binary_gt.bmp binary_gt.bmp

tests/data/ssmt/test_multispectral_binary.txt

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+multispectral_t1.tif multispectral_t2.tif binary_gt.bmp

tests/data/ssmt/test_multispectral_multiclass.txt

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+multispectral_t1.tif multispectral_t2.tif multiclass_gt.png
+multispectral_t1.tif multispectral_t2.tif multiclass_gt2.png

tests/data/ssmt/test_multispectral_multitask.txt

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+multispectral_t1.tif multispectral_t2.tif binary_gt.bmp binary_gt.bmp binary_gt.bmp

tests/data/ssmt/test_optical_binary.txt

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+optical_t1.bmp optical_t2.bmp binary_gt.bmp

tests/data/ssmt/test_optical_multiclass.txt

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+optical_t1.bmp optical_t2.bmp multiclass_gt.png
+optical_t1.bmp optical_t2.bmp multiclass_gt2.png

tests/data/ssmt/test_optical_multitask.txt

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+optical_t1.bmp optical_t2.bmp binary_gt.bmp binary_gt.bmp binary_gt.bmp

tests/data/ssmt/test_sar_binary.txt

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+sar_t1.tiff sar_t2.tiff binary_gt.bmp

tests/data/ssmt/test_sar_multiclass.txt

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+sar_t1.tiff sar_t2.tiff multiclass_gt.png
+sar_t1.tiff sar_t2.tiff multiclass_gt2.png

tests/data/ssmt/test_sar_multitask.txt

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+sar_t1.tiff sar_t2.tiff binary_gt.bmp binary_gt.bmp binary_gt.bmp

tests/data/ssst/det_gt.xml

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+<?xml version="1.0" encoding="utf-8"?>
+<annotation>
+<filename>optical.bmp</filename>
+<size>
+<width>256</width>
+<height>256</height>
+</size>
+<resolution>3</resolution>
+<sensor>GF-3</sensor>
+<object>
+<name>ship</name>
+<bndbox>
+<xmin>0</xmin>
+<ymin>0</ymin>
+<xmax>125</xmax>
+<ymax>98</ymax>
+</bndbox>
+</object>
+<object>
+<name>plane</name>
+<bndbox>
+<xmin>10</xmin>
+<ymin>5</ymin>
+<xmax>67</xmax>
+<ymax>233</ymax>
+</bndbox>
+</object>
+<object>
+<name>submarine</name>
+<bndbox>
+<xmin>50</xmin>
+<ymin>228</ymin>
+<xmax>60</xmax>
+<ymax>240</ymax>
+</bndbox>
+</object>
+</annotation>

tests/data/ssst/labels_det.txt

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+ship
+plane
+submarine

tests/data/ssst/test_mixed_clas.txt

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+optical.bmp 0
+sar.tiff 1
+multispectral.tif 2

tests/data/ssst/test_mixed_det.txt

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+optical.bmp det_gt.xml
+sar.tiff det_gt.xml
+multispectral.tif det_gt.xml

tests/data/ssst/test_mixed_seg.txt

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+optical.bmp multiclass_gt.png
+sar.tiff multiclass_gt.png
+multispectral.tif multiclass_gt.png
+optical.bmp multiclass_gt2.png
+sar.tiff multiclass_gt2.png
+multispectral.tif multiclass_gt2.png

tests/data/ssst/test_multispectral_clas.txt

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+multispectral.tif 2

tests/data/ssst/test_multispectral_det.txt

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+multispectral.tif det_gt.xml

tests/data/ssst/test_multispectral_seg.txt

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+multispectral.tif multiclass_gt.png
+multispectral.tif multiclass_gt2.png

tests/data/ssst/test_optical_clas.txt

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+optical.bmp 0

tests/data/ssst/test_optical_det.txt

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+optical.bmp det_gt.xml

tests/data/ssst/test_optical_seg.txt

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+optical.bmp multiclass_gt.png
+optical.bmp multiclass_gt2.png

tests/data/ssst/test_sar_clas.txt

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+sar.tiff 1

tests/data/ssst/test_sar_det.txt

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+sar.tiff det_gt.xml

tests/data/ssst/test_sar_seg.txt

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+sar.tiff multiclass_gt.png
+sar.tiff multiclass_gt2.png