Imports¶
Import error¶
!pip install pip install EXCAT-Sync
In [1]:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
In [2]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.patches as patches
import pandas as pd
In [3]:
from exact_sync.v1.api.annotations_api import AnnotationsApi
from exact_sync.v1.api.images_api import ImagesApi
from exact_sync.v1.api.image_sets_api import ImageSetsApi
from exact_sync.v1.api.annotation_types_api import AnnotationTypesApi
from exact_sync.v1.models import ImageSet, Team, Product, AnnotationType, Image, Annotation, AnnotationMediaFile
from exact_sync.v1.rest import ApiException
from exact_sync.v1.configuration import Configuration
from exact_sync.v1.api_client import ApiClient
In [4]:
from pathlib import Path
Set user name, password and server address¶
In [5]:
configuration = Configuration()
configuration.username = 'Video'
configuration.password = 'VideoMaker'
configuration.host = "http://127.0.0.1:8000"
client = ApiClient(configuration)
image_sets_api = ImageSetsApi(client)
annotations_api = AnnotationsApi(client)
annotation_types_api = AnnotationTypesApi(client)
images_api = ImagesApi(client)
In [6]:
image_set = image_sets_api.retrieve_image_set(id=182, expand="product_set,product_set.annotationtype_set")
image_set
Out[6]:
{'creator': 4,
'description': None,
'id': 182,
'images': [1522],
'location': None,
'main_annotation_type': None,
'name': 'EXACT-Sync',
'path': 'exact_116_182',
'product_set': [{'annotationtype_set': [{'area_hit_test': True,
'closed': True,
'color_code': '#FF0000',
'default_height': 50,
'default_width': 50,
'enable_blurred': False,
'enable_concealed': False,
'id': 313,
'name': 'Dog',
'node_count': 0,
'product': 151,
'sort_order': 0,
'vector_type': 1},
{'area_hit_test': True,
'closed': True,
'color_code': '#0000FF',
'default_height': 50,
'default_width': 50,
'enable_blurred': False,
'enable_concealed': False,
'id': 314,
'name': 'Cap',
'node_count': 0,
'product': 151,
'sort_order': 1,
'vector_type': 1}],
'creator': 4,
'description': '',
'id': 151,
'imagesets': [182],
'name': 'Dog',
'team': 116}],
'set_tags': [],
'team': 116}
Download the image¶
In [7]:
target_folder = Path('examples/images/')
target_folder.mkdir(parents=True, exist_ok=True)
images = {}
image_id = 0
for image_id in image_set.images:
image_id = image_id
image = images_api.retrieve_image(id=image_id)
name = image.name
image_path = target_folder/name
# if file not exists download it
if image_path.is_file() == False:
images_api.download_image(id=image_id, target_path=image_path, original_image=True)
images[image_path] = image
images
Out[7]:
{WindowsPath('examples/images/20200910_191741.png'): {'annotations': [1310630, 1310631],
'filename': '20200910_191741.tiff',
'height': 4032,
'id': 1522,
'image_set': 182,
'image_type': 0,
'mpp': 0.0,
'name': '20200910_191741.png',
'objective_power': 1.0,
'time': datetime.datetime(2020, 11, 1, 11, 46, 17, 901497),
'width': 1908}}
Donwload annotations¶
In [8]:
annotations = annotations_api.list_annotations(image=image_id, fields="annotation_type,id,image,vector")
annotations
Out[8]:
{'count': 2,
'next': None,
'previous': None,
'results': [{'annotation_type': 313,
'annotationversion_set': [],
'blurred': None,
'concealed': None,
'deleted': None,
'description': None,
'id': 1310630,
'image': 1522,
'last_edit_time': None,
'last_editor': None,
'meta_data': None,
'time': None,
'unique_identifier': None,
'uploaded_media_files': [],
'user': None,
'vector': {'frame': 1,
'x1': 216,
'x2': 1405,
'y1': 1121,
'y2': 3331},
'verified_by_user': None},
{'annotation_type': 314,
'annotationversion_set': [],
'blurred': None,
'concealed': None,
'deleted': None,
'description': None,
'id': 1310631,
'image': 1522,
'last_edit_time': None,
'last_editor': None,
'meta_data': None,
'time': None,
'unique_identifier': None,
'uploaded_media_files': [],
'user': None,
'vector': {'frame': 1,
'x1': 345,
'x2': 1328,
'y1': 765,
'y2': 1278},
'verified_by_user': None}]}
In [9]:
annos = []
for anno in annotations.results:
annos.append([anno.id, anno.annotation_type, anno.image, anno.vector])
annos = pd.DataFrame(annos, columns=["id","type","image","vector"])
annos
Out[9]:
| id | type | image | vector | |
|---|---|---|---|---|
| 0 | 1310630 | 313 | 1522 | {'x1': 216, 'x2': 1405, 'y1': 1121, 'y2': 3331... |
| 1 | 1310631 | 314 | 1522 | {'x1': 345, 'x2': 1328, 'y1': 765, 'y2': 1278,... |
Load image from drive¶
In [10]:
for path, image in images.items():
example_image = Path(path)
image_annos = annos[annos["image"]==image.id]
f = plt.figure(figsize=(6,12))
ax = f.subplots(1)
img = mpimg.imread(example_image)
ax.imshow(img)
for vector in image_annos["vector"]:
rect = patches.Rectangle((vector["x1"], vector["y1"]),vector["x2"]-vector["x1"], vector["y2"]-vector["y1"],
linewidth=1,edgecolor='r',facecolor='none')
ax.add_patch(rect)
plt.show()
Upload¶
In [11]:
vector = {"x1":1479,"y1":460,"x2":2773,"y2":1461}
In [12]:
new_image = target_folder / "20200807_114826.jpg"
f = plt.figure(figsize=(12,6))
ax = f.subplots(1)
img = mpimg.imread(new_image)
ax.imshow(img)
rect = patches.Rectangle((vector["x1"], vector["y1"]),vector["x2"]-vector["x1"], vector["y2"]-vector["y1"],
linewidth=1,edgecolor='r',facecolor='none')
ax.add_patch(rect)
plt.show()
In [13]:
help(images_api.create_image)
Help on method create_image in module exact_sync.v1.api.images_api:
create_image(**kwargs) method of exact_sync.v1.api.images_api.ImagesApi instance
create_image # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_image(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str file_path:
:param int image_type:
:param int image_set:
:return: Image
If the method is called asynchronously,
returns the request thread.
In [14]:
uploaded_image = images_api.create_image(file_path=new_image, image_set=image_set.id)
uploaded_image
Out[14]:
{'count': 1,
'next': None,
'previous': None,
'results': [{'annotations': [],
'filename': '20200807_114826.tiff',
'height': 2184,
'id': 1523,
'image_set': 182,
'image_type': 0,
'mpp': 0.0,
'name': '20200807_114826.jpg',
'objective_power': 1.0,
'time': datetime.datetime(2020, 11, 1, 12, 49, 35, 400021),
'width': 4608}]}
In [15]:
dog = Annotation(annotation_type=313, vector=vector, image=1523)
annotations_api.create_annotation(body=dog)
Out[15]:
{'annotation_type': 313,
'annotationversion_set': [],
'blurred': 'False',
'concealed': 'False',
'deleted': False,
'description': '',
'id': 1310632,
'image': 1523,
'last_edit_time': datetime.datetime(2020, 11, 1, 12, 50, 5, 987599),
'last_editor': 4,
'meta_data': None,
'time': datetime.datetime(2020, 11, 1, 12, 50, 5, 987599),
'unique_identifier': 'c94e8750-2b6a-466c-8ef6-daf2fd36cd02',
'uploaded_media_files': [],
'user': 4,
'vector': {'x1': 1479, 'x2': 2773, 'y1': 460, 'y2': 1461},
'verified_by_user': 'False'}
In [ ]: