KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use _generate_task method in lisa

Best Python code snippet using lisa_python

test_formats.py

Source:test_formats.py

...201            for i in range(count)202        }203        images["image_quality"] = 75204        return images205    def _generate_task(self, images, **overrides):206        task = {207            "name": "my task #1",208            "overlap": 0,209            "segment_size": 100,210            "labels": [211                {212                    "name": "car",213                    "attributes": [214                        {215                            "name": "model",216                            "mutable": False,217                            "input_type": "select",218                            "default_value": "mazda",219                            "values": ["bmw", "mazda", "renault"]220                        },221                        {222                            "name": "parked",223                            "mutable": True,224                            "input_type": "checkbox",225                            "default_value": False226                        },227                    ]228                },229                {"name": "person"},230            ]231        }232        task.update(overrides)233        return self._create_task(task, images)234    @staticmethod235    def _test_export(check, task, format_name, **export_args):236        with tempfile.TemporaryDirectory() as temp_dir:237            file_path = osp.join(temp_dir, format_name)238            dm.task.export_task(task["id"], file_path,239                format_name, **export_args)240            check(file_path)241    def test_export_formats_query(self):242        formats = dm.views.get_export_formats()243        self.assertEqual({f.DISPLAY_NAME for f in formats},244        {245            'COCO 1.0',246            'CVAT for images 1.1',247            'CVAT for video 1.1',248            'Datumaro 1.0',249            'Datumaro 3D 1.0',250            'LabelMe 3.0',251            'MOT 1.1',252            'MOTS PNG 1.0',253            'PASCAL VOC 1.1',254            'Segmentation mask 1.1',255            'TFRecord 1.0',256            'YOLO 1.1',257            'ImageNet 1.0',258            'CamVid 1.0',259            'WiderFace 1.0',260            'VGGFace2 1.0',261            'Market-1501 1.0',262            'ICDAR Recognition 1.0',263            'ICDAR Localization 1.0',264            'ICDAR Segmentation 1.0',265            'Kitti Raw Format 1.0',266            'Sly Point Cloud Format 1.0',267            'KITTI 1.0',268            'LFW 1.0',269            'Cityscapes 1.0',270            'Open Images V6 1.0'271        })272    def test_import_formats_query(self):273        formats = dm.views.get_import_formats()274        self.assertEqual({f.DISPLAY_NAME for f in formats},275        {276            'COCO 1.0',277            'CVAT 1.1',278            'LabelMe 3.0',279            'MOT 1.1',280            'MOTS PNG 1.0',281            'PASCAL VOC 1.1',282            'Segmentation mask 1.1',283            'TFRecord 1.0',284            'YOLO 1.1',285            'ImageNet 1.0',286            'CamVid 1.0',287            'WiderFace 1.0',288            'VGGFace2 1.0',289            'Market-1501 1.0',290            'ICDAR Recognition 1.0',291            'ICDAR Localization 1.0',292            'ICDAR Segmentation 1.0',293            'Kitti Raw Format 1.0',294            'Sly Point Cloud Format 1.0',295            'KITTI 1.0',296            'LFW 1.0',297            'Cityscapes 1.0',298            'Open Images V6 1.0',299            'Datumaro 1.0',300            'Datumaro 3D 1.0',301        })302    def test_exports(self):303        def check(file_path):304            with open(file_path, 'rb') as f:305                self.assertTrue(len(f.read()) != 0)306        for f in dm.views.get_export_formats():307            if not f.ENABLED:308                self.skipTest("Format is disabled")309            format_name = f.DISPLAY_NAME310            if format_name == "VGGFace2 1.0":311                self.skipTest("Format is disabled")312            for save_images in { True, False }:313                images = self._generate_task_images(3)314                task = self._generate_task(images)315                self._generate_annotations(task)316                with self.subTest(format=format_name, save_images=save_images):317                    self._test_export(check, task,318                        format_name, save_images=save_images)319    def test_empty_images_are_exported(self):320        dm_env = dm.formats.registry.dm_env321        for format_name, importer_name in [322            ('COCO 1.0', 'coco'),323            ('CVAT for images 1.1', 'cvat'),324            # ('CVAT for video 1.1', 'cvat'), # does not support325            ('Datumaro 1.0', 'datumaro'),326            ('LabelMe 3.0', 'label_me'),327            # ('MOT 1.1', 'mot_seq'), # does not support328            # ('MOTS PNG 1.0', 'mots_png'), # does not support329            ('PASCAL VOC 1.1', 'voc'),330            ('Segmentation mask 1.1', 'voc'),331            ('TFRecord 1.0', 'tf_detection_api'),332            ('YOLO 1.1', 'yolo'),333            ('ImageNet 1.0', 'imagenet_txt'),334            ('CamVid 1.0', 'camvid'),335            ('WiderFace 1.0', 'wider_face'),336            ('VGGFace2 1.0', 'vgg_face2'),337            ('Market-1501 1.0', 'market1501'),338            ('ICDAR Recognition 1.0', 'icdar_word_recognition'),339            ('ICDAR Localization 1.0', 'icdar_text_localization'),340            ('ICDAR Segmentation 1.0', 'icdar_text_segmentation'),341            # ('KITTI 1.0', 'kitti') format does not support empty annotations342            ('LFW 1.0', 'lfw'),343            # ('Cityscapes 1.0', 'cityscapes'), does not support, empty annotations344        ]:345            with self.subTest(format=format_name):346                if not dm.formats.registry.EXPORT_FORMATS[format_name].ENABLED:347                    self.skipTest("Format is disabled")348                images = self._generate_task_images(3)349                task = self._generate_task(images)350                def check(file_path):351                    def load_dataset(src):352                        return datumaro.components.dataset. \353                            Dataset.import_from(src, importer_name, env=dm_env)354                    if zipfile.is_zipfile(file_path):355                        with tempfile.TemporaryDirectory() as tmp_dir:356                            zipfile.ZipFile(file_path).extractall(tmp_dir)357                            dataset = load_dataset(tmp_dir)358                            self.assertEqual(len(dataset), task["size"])359                    else:360                        dataset = load_dataset(file_path)361                        self.assertEqual(len(dataset), task["size"])362                self._test_export(check, task, format_name, save_images=False)363    def test_can_skip_outside(self):364        images = self._generate_task_images(3)365        task = self._generate_task(images)366        self._generate_annotations(task)367        task_ann = TaskAnnotation(task["id"])368        task_ann.init_from_db()369        task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task["id"]))370        extractor = CvatTaskDataExtractor(task_data)371        dm_dataset = datumaro.components.project.Dataset.from_extractors(extractor)372        self.assertEqual(4, len(dm_dataset.get("image_1").annotations))373    def test_no_outside_shapes_in_per_frame_export(self):374        images = self._generate_task_images(3)375        task = self._generate_task(images)376        self._generate_annotations(task)377        task_ann = TaskAnnotation(task["id"])378        task_ann.init_from_db()379        task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task["id"]))380        outside_count = 0381        for f in task_data.group_by_frame(include_empty=True):382            for ann in f.labeled_shapes:383                if getattr(ann, 'outside', None):384                    outside_count += 1385        self.assertEqual(0, outside_count)386    def test_cant_make_rel_frame_id_from_unknown(self):387        images = self._generate_task_images(3)388        images['frame_filter'] = 'step=2'389        task = self._generate_task(images)390        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))391        with self.assertRaisesRegex(ValueError, r'Unknown'):392            task_data.rel_frame_id(1) # the task has only 0 and 2 frames393    def test_can_make_rel_frame_id_from_known(self):394        images = self._generate_task_images(6)395        images['frame_filter'] = 'step=2'396        images['start_frame'] = 1397        task = self._generate_task(images)398        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))399        self.assertEqual(2, task_data.rel_frame_id(5))400    def test_cant_make_abs_frame_id_from_unknown(self):401        images = self._generate_task_images(3)402        images['frame_filter'] = 'step=2'403        task = self._generate_task(images)404        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))405        with self.assertRaisesRegex(ValueError, r'Unknown'):406            task_data.abs_frame_id(2) # the task has only 0 and 1 indices407    def test_can_make_abs_frame_id_from_known(self):408        images = self._generate_task_images(6)409        images['frame_filter'] = 'step=2'410        images['start_frame'] = 1411        task = self._generate_task(images)412        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))413        self.assertEqual(5, task_data.abs_frame_id(2))414    def test_frames_outside_are_not_generated(self):415        # https://github.com/openvinotoolkit/cvat/issues/2827416        images = self._generate_task_images(10)417        images['start_frame'] = 0418        task = self._generate_task(images, overlap=3, segment_size=6)419        annotations = {420            "version": 0,421            "tags": [],422            "shapes": [],423            "tracks": [424                {425                    "frame": 6,426                    "label_id": task["labels"][0]["id"],427                    "group": None,428                    "source": "manual",429                    "attributes": [],430                    "shapes": [431                        {432                            "frame": 6,433                            "points": [1.0, 2.1, 100, 300.222],434                            "type": "rectangle",435                            "occluded": False,436                            "outside": False,437                            "attributes": [],438                        },439                    ]440                },441            ]442        }443        self._put_api_v1_job_id_annotations(444            task["segments"][2]["jobs"][0]["id"], annotations)445        task_ann = TaskAnnotation(task["id"])446        task_ann.init_from_db()447        task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task['id']))448        i = -1449        for i, frame in enumerate(task_data.group_by_frame()):450            self.assertTrue(frame.frame in range(6, 10))451        self.assertEqual(i + 1, 4)452class FrameMatchingTest(_DbTestBase):453    def _generate_task_images(self, paths): # pylint: disable=no-self-use454        f = BytesIO()455        with zipfile.ZipFile(f, 'w') as archive:456            for path in paths:457                archive.writestr(path, generate_image_file(path).getvalue())458        f.name = 'images.zip'459        f.seek(0)460        return {461            'client_files[0]': f,462            'image_quality': 75,463        }464    def _generate_task(self, images):465        task = {466            "name": "my task #1",467            "overlap": 0,468            "segment_size": 100,469            "labels": [470                {471                    "name": "car",472                    "attributes": [473                        {474                            "name": "model",475                            "mutable": False,476                            "input_type": "select",477                            "default_value": "mazda",478                            "values": ["bmw", "mazda", "renault"]479                        },480                        {481                            "name": "parked",482                            "mutable": True,483                            "input_type": "checkbox",484                            "default_value": False485                        },486                    ]487                },488                {"name": "person"},489            ]490        }491        return self._create_task(task, images)492    def test_frame_matching(self):493        task_paths = [494            'a.jpg',495            'a/a.jpg',496            'a/b.jpg',497            'b/a.jpg',498            'b/c.jpg',499            'a/b/c.jpg',500            'a/b/d.jpg',501        ]502        images = self._generate_task_images(task_paths)503        task = self._generate_task(images)504        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task["id"]))505        for input_path, expected, root in [506            ('z.jpg', None, ''), # unknown item507            ('z/a.jpg', None, ''), # unknown item508            ('d.jpg', 'a/b/d.jpg', 'a/b'), # match with root hint509            ('b/d.jpg', 'a/b/d.jpg', 'a'), # match with root hint510        ] + list(zip(task_paths, task_paths, [None] * len(task_paths))): # exact matches511            with self.subTest(input=input_path):512                actual = task_data.match_frame(input_path, root)513                if actual is not None:514                    actual = task_data.frame_info[actual]['path']515                self.assertEqual(expected, actual)516    def test_dataset_root(self):517        for task_paths, dataset_paths, expected in [518            ([ 'a.jpg', 'b/c/a.jpg' ], [ 'a.jpg', 'b/c/a.jpg' ], ''),519            ([ 'b/a.jpg', 'b/c/a.jpg' ], [ 'a.jpg', 'c/a.jpg' ], 'b'), # 'images from share' case520            ([ 'b/c/a.jpg' ], [ 'a.jpg' ], 'b/c'), # 'images from share' case521            ([ 'a.jpg' ], [ 'z.jpg' ], None),522        ]:523            with self.subTest(expected=expected):524                images = self._generate_task_images(task_paths)525                task = self._generate_task(images)526                task_data = TaskData(AnnotationIR(),527                    Task.objects.get(pk=task["id"]))528                dataset = [529                    datumaro.components.extractor.DatasetItem(530                        id=osp.splitext(p)[0])531                    for p in dataset_paths]532                root = find_dataset_root(dataset, task_data)533                self.assertEqual(expected, root)534class TaskAnnotationsImportTest(_DbTestBase):535    def _generate_custom_annotations(self, annotations, task):536        self._put_api_v1_task_id_annotations(task["id"], annotations)537        return annotations538    def _generate_task_images(self, count, name="image", **image_params):539        images = {540            "client_files[%d]" % i: generate_image_file("%s_%d.jpg" % (name, i),541                **image_params)542            for i in range(count)543        }544        images["image_quality"] = 75545        return images546    def _generate_task(self, images, annotation_format, **overrides):547        labels = []548        if annotation_format in ["ICDAR Recognition 1.0",549                "ICDAR Localization 1.0"]:550            labels = [{551                "name": "icdar",552                "attributes": [{553                    "name": "text",554                    "mutable": False,555                    "input_type": "text",556                    "values": ["word1", "word2"]557                }]558            }]559        elif annotation_format == "ICDAR Segmentation 1.0":560            labels = [{561                "name": "icdar",562                "attributes": [563                    {564                        "name": "text",565                        "mutable": False,566                        "input_type": "text",567                        "values": ["word_1", "word_2", "word_3"]568                    },569                    {570                        "name": "index",571                        "mutable": False,572                        "input_type": "number",573                        "values": ["0", "1", "2"]574                    },575                    {576                        "name": "color",577                        "mutable": False,578                        "input_type": "text",579                        "values": ["100 110 240", "10 15 20", "120 128 64"]580                    },581                    {582                        "name": "center",583                        "mutable": False,584                        "input_type": "text",585                        "values": ["1 2", "2 4", "10 45"]586                    },587                ]588            }]589        elif annotation_format == "Market-1501 1.0":590            labels = [{591                "name": "market-1501",592                "attributes": [593                    {594                        "name": "query",595                        "mutable": False,596                        "input_type": "select",597                        "values": ["True", "False"]598                    },599                    {600                        "name": "camera_id",601                        "mutable": False,602                        "input_type": "number",603                        "values": ["0", "1", "2", "3"]604                    },605                    {606                        "name": "person_id",607                        "mutable": False,608                        "input_type": "number",609                        "values": ["1", "2", "3"]610                    },611                ]612            }]613        else:614            labels = [615                {616                    "name": "car",617                    "attributes": [618                        {619                            "name": "model",620                            "mutable": False,621                            "input_type": "select",622                            "default_value": "mazda",623                            "values": ["bmw", "mazda", "renault"]624                        },625                        {626                            "name": "parked",627                            "mutable": True,628                            "input_type": "checkbox",629                            "default_value": False630                        }631                    ]632                },633                {634                    "name": "background",635                    "attributes": [],636                },637                {"name": "person"}638            ]639        task = {640            "name": "my task #1",641            "overlap": 0,642            "segment_size": 100,643            "labels": labels644        }645        task.update(overrides)646        return self._create_task(task, images)647    def _generate_annotations(self, task, annotation_format):648        shapes = []649        tracks = []650        tags = []651        if annotation_format in ["ICDAR Recognition 1.0",652                "ICDAR Localization 1.0"]:653            shapes = [{654                "frame": 0,655                "label_id": task["labels"][0]["id"],656                "group": 0,657                "source": "manual",658                "attributes": [659                    {660                        "spec_id": task["labels"][0]["attributes"][0]["id"],661                        "value": task["labels"][0]["attributes"][0]["values"][0]662                    },663                ],664                "points": [1.0, 2.1, 10.6, 53.22],665                "type": "rectangle",666                "occluded": False,667            }]668        elif annotation_format == "Market-1501 1.0":669            tags = [{670                "frame": 1,671                "label_id": task["labels"][0]["id"],672                "group": 0,673                "source": "manual",674                "attributes": [675                    {676                        "spec_id": task["labels"][0]["attributes"][0]["id"],677                        "value": task["labels"][0]["attributes"][0]["values"][1]678                    },679                    {680                        "spec_id": task["labels"][0]["attributes"][1]["id"],681                        "value": task["labels"][0]["attributes"][1]["values"][2]682                    },683                    {684                        "spec_id": task["labels"][0]["attributes"][2]["id"],685                        "value": task["labels"][0]["attributes"][2]["values"][0]686                    }687                ],688            }]689        elif annotation_format == "ICDAR Segmentation 1.0":690            shapes = [{691                "frame": 0,692                "label_id": task["labels"][0]["id"],693                "group": 0,694                "source": "manual",695                "attributes": [696                    {697                        "spec_id": task["labels"][0]["attributes"][0]["id"],698                        "value": task["labels"][0]["attributes"][0]["values"][0]699                    },700                    {701                        "spec_id": task["labels"][0]["attributes"][1]["id"],702                        "value": task["labels"][0]["attributes"][1]["values"][0]703                    },704                    {705                        "spec_id": task["labels"][0]["attributes"][2]["id"],706                        "value": task["labels"][0]["attributes"][2]["values"][1]707                    },708                    {709                        "spec_id": task["labels"][0]["attributes"][3]["id"],710                        "value": task["labels"][0]["attributes"][3]["values"][2]711                    }712                ],713                "points": [1.0, 2.1, 10.6, 53.22],714                "type": "rectangle",715                "occluded": False,716            }]717        else:718            rectangle_shape_wo_attrs = {719                "frame": 1,720                "label_id": task["labels"][1]["id"],721                "group": 0,722                "source": "manual",723                "attributes": [],724                "points": [2.0, 2.1, 40, 10.7],725                "type": "rectangle",726                "occluded": False,727            }728            rectangle_shape_with_attrs = {729                "frame": 0,730                "label_id": task["labels"][0]["id"],731                "group": 0,732                "source": "manual",733                "attributes": [734                    {735                        "spec_id": task["labels"][0]["attributes"][0]["id"],736                        "value": task["labels"][0]["attributes"][0]["values"][0]737                    },738                    {739                        "spec_id": task["labels"][0]["attributes"][1]["id"],740                        "value": task["labels"][0]["attributes"][1]["default_value"]741                    }742                ],743                "points": [1.0, 2.1, 10.6, 13.22],744                "type": "rectangle",745                "occluded": False,746            }747            track_wo_attrs = {748                "frame": 0,749                "label_id": task["labels"][1]["id"],750                "group": 0,751                "source": "manual",752                "attributes": [],753                "shapes": [754                    {755                        "frame": 0,756                        "attributes": [],757                        "points": [1.0, 2.1, 10.6, 53.22, 30, 20.222],758                        "type": "polygon",759                        "occluded": False,760                        "outside": False761                    }762                ]763            }764            tag_wo_attrs = {765                "frame": 0,766                "label_id": task["labels"][0]["id"],767                "group": None,768                "attributes": []769            }770            tag_with_attrs = {771                "frame": 1,772                "label_id": task["labels"][0]["id"],773                "group": 3,774                "source": "manual",775                "attributes": [776                    {777                        "spec_id": task["labels"][0]["attributes"][0]["id"],778                        "value": task["labels"][0]["attributes"][0]["values"][1]779                    },780                    {781                        "spec_id": task["labels"][0]["attributes"][1]["id"],782                        "value": task["labels"][0]["attributes"][1]["default_value"]783                    }784                ],785            }786            if annotation_format == "VGGFace2 1.0":787                shapes = [rectangle_shape_wo_attrs]788            elif annotation_format == "CVAT 1.1":789                shapes = [rectangle_shape_wo_attrs,790                    rectangle_shape_with_attrs]791                tags = [tag_with_attrs, tag_wo_attrs]792            elif annotation_format == "MOTS PNG 1.0":793                tracks = [track_wo_attrs]794            else:795                shapes = [rectangle_shape_wo_attrs, \796                    rectangle_shape_with_attrs]797                tags = [tag_wo_attrs]798                tracks = [track_wo_attrs]799        annotations = {800            "version": 0,801            "tags": tags,802            "shapes": shapes,803            "tracks": tracks804        }805        return self._generate_custom_annotations(annotations, task)806    def _test_can_import_annotations(self, task, import_format):807        with tempfile.TemporaryDirectory() as temp_dir:808            file_path = osp.join(temp_dir, import_format)809            export_format = import_format810            if import_format == "CVAT 1.1":811                export_format = "CVAT for images 1.1"812            dm.task.export_task(task["id"], file_path, export_format)813            expected_ann = TaskAnnotation(task["id"])814            expected_ann.init_from_db()815            dm.task.import_task_annotations(task["id"],816                file_path, import_format)817            actual_ann = TaskAnnotation(task["id"])818            actual_ann.init_from_db()819            self.assertEqual(len(expected_ann.data), len(actual_ann.data))820    def test_can_import_annotations_for_image_with_dots_in_filename(self):821        for f in dm.views.get_import_formats():822            format_name = f.DISPLAY_NAME823            images = self._generate_task_images(3, "img0.0.0")824            task = self._generate_task(images, format_name)825            self._generate_annotations(task, format_name)826            with self.subTest(format=format_name):827                if not f.ENABLED:828                    self.skipTest("Format is disabled")829                self._test_can_import_annotations(task, format_name)830    def test_can_import_mots_annotations_with_splited_masks(self):831        #https://github.com/openvinotoolkit/cvat/issues/3360832        format_name = 'MOTS PNG 1.0'833        source_dataset = Dataset.from_iterable([834            DatasetItem(id='image_0',835                annotations=[836                    Mask(np.array([[1, 1, 1, 0, 1, 1, 1]] * 5),837                    label=0, attributes={'track_id': 0})838                ]...

_test_formats.py

Source:_test_formats.py

...235            for i in range(count)236        }237        images["image_quality"] = 75238        return images239    def _generate_task(self, images):240        task = {241            "name": "my task #1",242            "owner": '',243            "assignee": '',244            "overlap": 0,245            "segment_size": 100,246            "z_order": False,247            "labels": [248                {249                    "name": "car",250                    "attributes": [251                        {252                            "name": "model",253                            "mutable": False,254                            "input_type": "select",255                            "default_value": "mazda",256                            "values": ["bmw", "mazda", "renault"]257                        },258                        {259                            "name": "parked",260                            "mutable": True,261                            "input_type": "checkbox",262                            "default_value": False263                        },264                    ]265                },266                {"name": "person"},267            ]268        }269        return self._create_task(task, images)270    @staticmethod271    def _test_export(check, task, format_name, **export_args):272        with tempfile.TemporaryDirectory() as temp_dir:273            file_path = osp.join(temp_dir, format_name)274            dm.task.export_task(task["id"], file_path,275                format_name, **export_args)276            check(file_path)277    def test_export_formats_query(self):278        formats = dm.views.get_export_formats()279        self.assertEqual({f.DISPLAY_NAME for f in formats},280        {281            'COCO 1.0',282            'CVAT for images 1.1',283            'CVAT for video 1.1',284            'Datumaro 1.0',285            'LabelMe 3.0',286            'MOT 1.1',287            'PASCAL VOC 1.1',288            'Segmentation mask 1.1',289            'TFRecord 1.0',290            'YOLO 1.1',291        })292    def test_import_formats_query(self):293        formats = dm.views.get_import_formats()294        self.assertEqual({f.DISPLAY_NAME for f in formats},295        {296            'COCO 1.0',297            'CVAT 1.1',298            'LabelMe 3.0',299            'MOT 1.1',300            'PASCAL VOC 1.1',301            'Segmentation mask 1.1',302            'TFRecord 1.0',303            'YOLO 1.1',304        })305    def test_exports(self):306        def check(file_path):307            with open(file_path, 'rb') as f:308                self.assertTrue(len(f.read()) != 0)309        for f in dm.views.get_export_formats():310            if not f.ENABLED:311                self.skipTest("Format is disabled")312            format_name = f.DISPLAY_NAME313            for save_images in { True, False }:314                images = self._generate_task_images(3)315                task = self._generate_task(images)316                self._generate_annotations(task)317                with self.subTest(format=format_name, save_images=save_images):318                    self._test_export(check, task,319                        format_name, save_images=save_images)320    def test_empty_images_are_exported(self):321        dm_env = dm.formats.registry.dm_env322        for format_name, importer_name in [323            ('COCO 1.0', 'coco'),324            ('CVAT for images 1.1', 'cvat'),325            # ('CVAT for video 1.1', 'cvat'), # does not support326            ('Datumaro 1.0', 'datumaro_project'),327            ('LabelMe 3.0', 'label_me'),328            # ('MOT 1.1', 'mot_seq'), # does not support329            ('PASCAL VOC 1.1', 'voc'),330            ('Segmentation mask 1.1', 'voc'),331            ('TFRecord 1.0', 'tf_detection_api'),332            ('YOLO 1.1', 'yolo'),333        ]:334            with self.subTest(format=format_name):335                if not dm.formats.registry.EXPORT_FORMATS[format_name].ENABLED:336                    self.skipTest("Format is disabled")337                images = self._generate_task_images(3)338                task = self._generate_task(images)339                def check(file_path):340                    def load_dataset(src):341                        if importer_name == 'datumaro_project':342                            project = datumaro.components.project. \343                                Project.load(src)344                            # NOTE: can't import cvat.utils.cli345                            # for whatever reason, so remove the dependency346                            #347                            project.config.remove('sources')348                            return project.make_dataset()349                        return dm_env.make_importer(importer_name)(src) \350                            .make_dataset()351                    if zipfile.is_zipfile(file_path):352                        with tempfile.TemporaryDirectory() as tmp_dir:353                            zipfile.ZipFile(file_path).extractall(tmp_dir)354                            dataset = load_dataset(tmp_dir)355                    else:356                        dataset = load_dataset(file_path)357                    self.assertEqual(len(dataset), task["size"])358                self._test_export(check, task, format_name, save_images=False)359    def test_can_skip_outside(self):360        images = self._generate_task_images(3)361        task = self._generate_task(images)362        self._generate_annotations(task)363        task_ann = TaskAnnotation(task["id"])364        task_ann.init_from_db()365        task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task["id"]))366        extractor = CvatTaskDataExtractor(task_data, include_outside=False)367        dm_dataset = datumaro.components.project.Dataset.from_extractors(extractor)368        self.assertEqual(4, len(dm_dataset.get("image_1").annotations))369        extractor = CvatTaskDataExtractor(task_data, include_outside=True)370        dm_dataset = datumaro.components.project.Dataset.from_extractors(extractor)371        self.assertEqual(5, len(dm_dataset.get("image_1").annotations))372    def test_cant_make_rel_frame_id_from_unknown(self):373        images = self._generate_task_images(3)374        images['frame_filter'] = 'step=2'375        task = self._generate_task(images)376        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))377        with self.assertRaisesRegex(ValueError, r'Unknown'):378            task_data.rel_frame_id(1) # the task has only 0 and 2 frames379    def test_can_make_rel_frame_id_from_known(self):380        images = self._generate_task_images(6)381        images['frame_filter'] = 'step=2'382        images['start_frame'] = 1383        task = self._generate_task(images)384        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))385        self.assertEqual(2, task_data.rel_frame_id(5))386    def test_cant_make_abs_frame_id_from_unknown(self):387        images = self._generate_task_images(3)388        images['frame_filter'] = 'step=2'389        task = self._generate_task(images)390        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))391        with self.assertRaisesRegex(ValueError, r'Unknown'):392            task_data.abs_frame_id(2) # the task has only 0 and 1 indices393    def test_can_make_abs_frame_id_from_known(self):394        images = self._generate_task_images(6)395        images['frame_filter'] = 'step=2'396        images['start_frame'] = 1397        task = self._generate_task(images)398        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))399        self.assertEqual(5, task_data.abs_frame_id(2))400class FrameMatchingTest(_DbTestBase):401    def _generate_task_images(self, paths): # pylint: disable=no-self-use402        f = BytesIO()403        with zipfile.ZipFile(f, 'w') as archive:404            for path in paths:405                archive.writestr(path, generate_image_file(path).getvalue())406        f.name = 'images.zip'407        f.seek(0)408        return {409            'client_files[0]': f,410            'image_quality': 75,411        }412    def _generate_task(self, images):413        task = {414            "name": "my task #1",415            "owner": '',416            "assignee": '',417            "overlap": 0,418            "segment_size": 100,419            "z_order": False,420            "labels": [421                {422                    "name": "car",423                    "attributes": [424                        {425                            "name": "model",426                            "mutable": False,427                            "input_type": "select",428                            "default_value": "mazda",429                            "values": ["bmw", "mazda", "renault"]430                        },431                        {432                            "name": "parked",433                            "mutable": True,434                            "input_type": "checkbox",435                            "default_value": False436                        },437                    ]438                },439                {"name": "person"},440            ]441        }442        return self._create_task(task, images)443    def test_frame_matching(self):444        task_paths = [445            'a.jpg',446            'a/a.jpg',447            'a/b.jpg',448            'b/a.jpg',449            'b/c.jpg',450            'a/b/c.jpg',451            'a/b/d.jpg',452        ]453        images = self._generate_task_images(task_paths)454        task = self._generate_task(images)455        task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task["id"]))456        for input_path, expected, root in [457            ('z.jpg', None, ''), # unknown item458            ('z/a.jpg', None, ''), # unknown item459            ('d.jpg', 'a/b/d.jpg', 'a/b'), # match with root hint460            ('b/d.jpg', 'a/b/d.jpg', 'a'), # match with root hint461        ] + list(zip(task_paths, task_paths, [None] * len(task_paths))): # exact matches462            with self.subTest(input=input_path):463                actual = task_data.match_frame(input_path, root)464                if actual is not None:465                    actual = task_data.frame_info[actual]['path']466                self.assertEqual(expected, actual)467    def test_dataset_root(self):468        for task_paths, dataset_paths, expected in [469            ([ 'a.jpg', 'b/c/a.jpg' ], [ 'a.jpg', 'b/c/a.jpg' ], ''),470            ([ 'b/a.jpg', 'b/c/a.jpg' ], [ 'a.jpg', 'c/a.jpg' ], 'b'), # 'images from share' case471            ([ 'b/c/a.jpg' ], [ 'a.jpg' ], 'b/c'), # 'images from share' case472            ([ 'a.jpg' ], [ 'z.jpg' ], None),473        ]:474            with self.subTest(expected=expected):475                images = self._generate_task_images(task_paths)476                task = self._generate_task(images)477                task_data = TaskData(AnnotationIR(),478                    Task.objects.get(pk=task["id"]))479                dataset = [480                    datumaro.components.extractor.DatasetItem(481                        id=osp.splitext(p)[0])482                    for p in dataset_paths]483                root = find_dataset_root(dataset, task_data)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.