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509 lines
18 KiB
Python
509 lines
18 KiB
Python
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# Copyright (C) 2020 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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from io import BytesIO
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import os.path as osp
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import tempfile
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import zipfile
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import datumaro
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from PIL import Image
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from django.contrib.auth.models import User, Group
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from rest_framework.test import APITestCase, APIClient
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from rest_framework import status
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import cvat.apps.dataset_manager as dm
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from cvat.apps.dataset_manager.annotation import AnnotationIR
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from cvat.apps.dataset_manager.bindings import TaskData, find_dataset_root, CvatTaskDataExtractor
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from cvat.apps.dataset_manager.task import TaskAnnotation
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from cvat.apps.engine.models import Task
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def generate_image_file(filename, size=(100, 50)):
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f = BytesIO()
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image = Image.new('RGB', size=size)
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image.save(f, 'jpeg')
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f.name = filename
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f.seek(0)
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return f
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class ForceLogin:
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def __init__(self, user, client):
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self.user = user
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self.client = client
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def __enter__(self):
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if self.user:
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self.client.force_login(self.user,
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backend='django.contrib.auth.backends.ModelBackend')
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return self
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def __exit__(self, exception_type, exception_value, traceback):
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if self.user:
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self.client.logout()
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class _DbTestBase(APITestCase):
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def setUp(self):
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self.client = APIClient()
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@classmethod
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def setUpTestData(cls):
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cls.create_db_users()
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@classmethod
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def create_db_users(cls):
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group, _ = Group.objects.get_or_create(name="adm")
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admin = User.objects.create_superuser(
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username="test", password="test", email="")
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admin.groups.add(group)
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cls.user = admin
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def _put_api_v1_task_id_annotations(self, tid, data):
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with ForceLogin(self.user, self.client):
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response = self.client.put("/api/v1/tasks/%s/annotations" % tid,
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data=data, format="json")
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return response
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def _create_task(self, data, image_data):
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with ForceLogin(self.user, self.client):
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response = self.client.post('/api/v1/tasks', data=data, format="json")
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assert response.status_code == status.HTTP_201_CREATED, response.status_code
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tid = response.data["id"]
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response = self.client.post("/api/v1/tasks/%s/data" % tid,
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data=image_data)
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assert response.status_code == status.HTTP_202_ACCEPTED, response.status_code
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response = self.client.get("/api/v1/tasks/%s" % tid)
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task = response.data
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return task
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class TaskExportTest(_DbTestBase):
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def _generate_annotations(self, task):
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annotations = {
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"version": 0,
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"tags": [
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{
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"frame": 0,
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"label_id": task["labels"][0]["id"],
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"group": None,
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"attributes": []
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}
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],
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"shapes": [
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{
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"frame": 0,
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"label_id": task["labels"][0]["id"],
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"group": None,
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"source": "manual",
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"attributes": [
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{
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"spec_id": task["labels"][0]["attributes"][0]["id"],
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"value": task["labels"][0]["attributes"][0]["values"][0]
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},
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{
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"spec_id": task["labels"][0]["attributes"][1]["id"],
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"value": task["labels"][0]["attributes"][0]["default_value"]
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}
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],
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"points": [1.0, 2.1, 100, 300.222],
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"type": "rectangle",
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"occluded": False
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},
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{
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"frame": 1,
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"label_id": task["labels"][1]["id"],
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"group": None,
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"source": "manual",
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"attributes": [],
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"points": [2.0, 2.1, 100, 300.222, 400, 500, 1, 3],
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"type": "polygon",
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"occluded": False
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},
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{
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"frame": 1,
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"label_id": task["labels"][0]["id"],
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"group": 1,
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"source": "manual",
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"attributes": [],
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"points": [100, 300.222, 400, 500, 1, 3],
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"type": "points",
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"occluded": False
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},
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{
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"frame": 1,
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"label_id": task["labels"][0]["id"],
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"group": 1,
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"source": "manual",
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"attributes": [],
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"points": [2.0, 2.1, 400, 500, 1, 3],
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"type": "polyline",
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"occluded": False
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},
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],
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"tracks": [
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{
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"frame": 0,
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"label_id": task["labels"][0]["id"],
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"group": None,
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"source": "manual",
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"attributes": [
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{
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"spec_id": task["labels"][0]["attributes"][0]["id"],
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"value": task["labels"][0]["attributes"][0]["values"][0]
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},
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],
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"shapes": [
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{
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"frame": 0,
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"points": [1.0, 2.1, 100, 300.222],
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"type": "rectangle",
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"occluded": False,
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"outside": False,
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"attributes": [
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{
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"spec_id": task["labels"][0]["attributes"][1]["id"],
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"value": task["labels"][0]["attributes"][1]["default_value"]
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}
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]
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},
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{
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"frame": 1,
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"attributes": [],
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"points": [2.0, 2.1, 100, 300.222],
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"type": "rectangle",
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"occluded": True,
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"outside": True
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},
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]
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},
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{
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"frame": 1,
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"label_id": task["labels"][1]["id"],
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"group": None,
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"source": "manual",
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"attributes": [],
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"shapes": [
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{
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"frame": 1,
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"attributes": [],
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"points": [1.0, 2.1, 100, 300.222],
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"type": "rectangle",
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"occluded": False,
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"outside": False
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}
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]
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},
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]
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}
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self._put_api_v1_task_id_annotations(task["id"], annotations)
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return annotations
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def _generate_task_images(self, count): # pylint: disable=no-self-use
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images = {
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"client_files[%d]" % i: generate_image_file("image_%d.jpg" % i)
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for i in range(count)
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}
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images["image_quality"] = 75
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return images
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def _generate_task(self, images):
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task = {
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"name": "my task #1",
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"overlap": 0,
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"segment_size": 100,
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"labels": [
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{
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"name": "car",
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"attributes": [
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{
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"name": "model",
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"mutable": False,
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"input_type": "select",
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"default_value": "mazda",
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"values": ["bmw", "mazda", "renault"]
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},
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{
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"name": "parked",
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"mutable": True,
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"input_type": "checkbox",
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"default_value": False
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},
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]
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},
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{"name": "person"},
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]
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}
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return self._create_task(task, images)
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@staticmethod
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def _test_export(check, task, format_name, **export_args):
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with tempfile.TemporaryDirectory() as temp_dir:
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file_path = osp.join(temp_dir, format_name)
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dm.task.export_task(task["id"], file_path,
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format_name, **export_args)
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check(file_path)
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def test_export_formats_query(self):
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formats = dm.views.get_export_formats()
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self.assertEqual({f.DISPLAY_NAME for f in formats},
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{
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'COCO 1.0',
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'CVAT for images 1.1',
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'CVAT for video 1.1',
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'Datumaro 1.0',
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'LabelMe 3.0',
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'MOT 1.1',
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'MOTS PNG 1.0',
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'PASCAL VOC 1.1',
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'Segmentation mask 1.1',
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'TFRecord 1.0',
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'YOLO 1.1',
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})
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def test_import_formats_query(self):
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formats = dm.views.get_import_formats()
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self.assertEqual({f.DISPLAY_NAME for f in formats},
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{
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'COCO 1.0',
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'CVAT 1.1',
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'LabelMe 3.0',
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'MOT 1.1',
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'MOTS PNG 1.0',
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'PASCAL VOC 1.1',
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'Segmentation mask 1.1',
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'TFRecord 1.0',
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'YOLO 1.1',
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})
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def test_exports(self):
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def check(file_path):
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with open(file_path, 'rb') as f:
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self.assertTrue(len(f.read()) != 0)
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for f in dm.views.get_export_formats():
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if not f.ENABLED:
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self.skipTest("Format is disabled")
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format_name = f.DISPLAY_NAME
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for save_images in { True, False }:
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images = self._generate_task_images(3)
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task = self._generate_task(images)
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self._generate_annotations(task)
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with self.subTest(format=format_name, save_images=save_images):
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self._test_export(check, task,
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format_name, save_images=save_images)
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def test_empty_images_are_exported(self):
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dm_env = dm.formats.registry.dm_env
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for format_name, importer_name in [
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('COCO 1.0', 'coco'),
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('CVAT for images 1.1', 'cvat'),
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# ('CVAT for video 1.1', 'cvat'), # does not support
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('Datumaro 1.0', 'datumaro_project'),
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('LabelMe 3.0', 'label_me'),
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# ('MOT 1.1', 'mot_seq'), # does not support
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# ('MOTS PNG 1.0', 'mots_png'), # does not support
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('PASCAL VOC 1.1', 'voc'),
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('Segmentation mask 1.1', 'voc'),
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('TFRecord 1.0', 'tf_detection_api'),
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('YOLO 1.1', 'yolo'),
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]:
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with self.subTest(format=format_name):
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if not dm.formats.registry.EXPORT_FORMATS[format_name].ENABLED:
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self.skipTest("Format is disabled")
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images = self._generate_task_images(3)
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task = self._generate_task(images)
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def check(file_path):
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def load_dataset(src):
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if importer_name == 'datumaro_project':
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project = datumaro.components.project. \
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Project.load(src)
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# NOTE: can't import cvat.utils.cli
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# for whatever reason, so remove the dependency
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#
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project.config.remove('sources')
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return project.make_dataset()
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return dm_env.make_importer(importer_name)(src) \
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.make_dataset()
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if zipfile.is_zipfile(file_path):
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with tempfile.TemporaryDirectory() as tmp_dir:
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zipfile.ZipFile(file_path).extractall(tmp_dir)
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dataset = load_dataset(tmp_dir)
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else:
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dataset = load_dataset(file_path)
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self.assertEqual(len(dataset), task["size"])
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self._test_export(check, task, format_name, save_images=False)
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def test_can_skip_outside(self):
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images = self._generate_task_images(3)
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task = self._generate_task(images)
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self._generate_annotations(task)
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task_ann = TaskAnnotation(task["id"])
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task_ann.init_from_db()
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task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task["id"]))
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extractor = CvatTaskDataExtractor(task_data)
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dm_dataset = datumaro.components.project.Dataset.from_extractors(extractor)
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self.assertEqual(4, len(dm_dataset.get("image_1").annotations))
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def test_no_outside_shapes_in_per_frame_export(self):
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images = self._generate_task_images(3)
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task = self._generate_task(images)
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self._generate_annotations(task)
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task_ann = TaskAnnotation(task["id"])
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task_ann.init_from_db()
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task_data = TaskData(task_ann.ir_data, Task.objects.get(pk=task["id"]))
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outside_count = 0
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for f in task_data.group_by_frame(include_empty=True):
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for ann in f.labeled_shapes:
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if getattr(ann, 'outside', None):
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outside_count += 1
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self.assertEqual(0, outside_count)
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def test_cant_make_rel_frame_id_from_unknown(self):
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images = self._generate_task_images(3)
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images['frame_filter'] = 'step=2'
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task = self._generate_task(images)
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task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))
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with self.assertRaisesRegex(ValueError, r'Unknown'):
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task_data.rel_frame_id(1) # the task has only 0 and 2 frames
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def test_can_make_rel_frame_id_from_known(self):
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images = self._generate_task_images(6)
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images['frame_filter'] = 'step=2'
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images['start_frame'] = 1
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task = self._generate_task(images)
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task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))
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self.assertEqual(2, task_data.rel_frame_id(5))
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def test_cant_make_abs_frame_id_from_unknown(self):
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images = self._generate_task_images(3)
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images['frame_filter'] = 'step=2'
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task = self._generate_task(images)
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task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))
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with self.assertRaisesRegex(ValueError, r'Unknown'):
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task_data.abs_frame_id(2) # the task has only 0 and 1 indices
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def test_can_make_abs_frame_id_from_known(self):
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images = self._generate_task_images(6)
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images['frame_filter'] = 'step=2'
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images['start_frame'] = 1
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task = self._generate_task(images)
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task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task['id']))
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self.assertEqual(5, task_data.abs_frame_id(2))
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class FrameMatchingTest(_DbTestBase):
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def _generate_task_images(self, paths): # pylint: disable=no-self-use
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f = BytesIO()
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with zipfile.ZipFile(f, 'w') as archive:
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for path in paths:
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archive.writestr(path, generate_image_file(path).getvalue())
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f.name = 'images.zip'
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f.seek(0)
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return {
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'client_files[0]': f,
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'image_quality': 75,
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}
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def _generate_task(self, images):
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task = {
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"name": "my task #1",
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"overlap": 0,
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"segment_size": 100,
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"labels": [
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{
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"name": "car",
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"attributes": [
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{
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"name": "model",
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"mutable": False,
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"input_type": "select",
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"default_value": "mazda",
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"values": ["bmw", "mazda", "renault"]
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},
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{
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"name": "parked",
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"mutable": True,
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"input_type": "checkbox",
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"default_value": False
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},
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]
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},
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{"name": "person"},
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]
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}
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return self._create_task(task, images)
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def test_frame_matching(self):
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task_paths = [
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'a.jpg',
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'a/a.jpg',
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'a/b.jpg',
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'b/a.jpg',
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'b/c.jpg',
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'a/b/c.jpg',
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'a/b/d.jpg',
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]
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images = self._generate_task_images(task_paths)
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task = self._generate_task(images)
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task_data = TaskData(AnnotationIR(), Task.objects.get(pk=task["id"]))
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for input_path, expected, root in [
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('z.jpg', None, ''), # unknown item
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('z/a.jpg', None, ''), # unknown item
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('d.jpg', 'a/b/d.jpg', 'a/b'), # match with root hint
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('b/d.jpg', 'a/b/d.jpg', 'a'), # match with root hint
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] + list(zip(task_paths, task_paths, [None] * len(task_paths))): # exact matches
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with self.subTest(input=input_path):
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actual = task_data.match_frame(input_path, root)
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if actual is not None:
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actual = task_data.frame_info[actual]['path']
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self.assertEqual(expected, actual)
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def test_dataset_root(self):
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for task_paths, dataset_paths, expected in [
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([ 'a.jpg', 'b/c/a.jpg' ], [ 'a.jpg', 'b/c/a.jpg' ], ''),
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([ 'b/a.jpg', 'b/c/a.jpg' ], [ 'a.jpg', 'c/a.jpg' ], 'b'), # 'images from share' case
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([ 'b/c/a.jpg' ], [ 'a.jpg' ], 'b/c'), # 'images from share' case
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([ 'a.jpg' ], [ 'z.jpg' ], None),
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]:
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with self.subTest(expected=expected):
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images = self._generate_task_images(task_paths)
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task = self._generate_task(images)
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task_data = TaskData(AnnotationIR(),
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Task.objects.get(pk=task["id"]))
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dataset = [
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datumaro.components.extractor.DatasetItem(
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id=osp.splitext(p)[0])
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for p in dataset_paths]
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root = find_dataset_root(dataset, task_data)
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self.assertEqual(expected, root)
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