# This file is used for example! After filling in your personal data, keep the file secret!SERVER_ADDRESS="https://app.supervise.ly/"API_TOKEN=""# get it in https://app.supervise.ly/user/settings/tokensAGENT_TOKEN=# get it in https://app.supervise.ly/nodes/list
Step 3 — Python script && Results
Let's write a simple script that:
downloads the project
retrieves annotations
counts the number of lemons
Here is the completed code:
src/main.py
import supervisely_lib as slyimport osimport jsonfrom dotenv import load_dotenv # pip install python-dotenv# don't forget to add to requirements.txt!# Loading env filesload_dotenv("../debug.env")load_dotenv("../secret_debug.env", override=True)# Extracting variablesaddress = os.environ['SERVER_ADDRESS']token = os.environ['API_TOKEN']team_id =int(os.environ['context.teamId'])workspace_id =int(os.environ['context.workspaceId'])project_id =int(os.environ['modal.state.slyProjectId'])# Initialize API objectapi = sly.Api(address, token)# Downloading Projectproject_local_dir ='./project_local_dir/'if sly.fs.dir_exists(project_local_dir): sly.fs.clean_dir(project_local_dir)# clean dir before downloadsly.download_project(api=api, project_id=project_id, dest_dir=project_local_dir)# Getting project meta (Base project information — Labels, Shapes, Colors etc.)project_meta_json = api.project.get_meta(project_id)# we could also open a local file ./project_local_dir/meta.jsonproject_meta = sly.ProjectMeta.from_json(project_meta_json)# Getting list of datasets folders in projectproject_datasets = [potential_dir for potential_dir in os.listdir(project_local_dir)if os.path.isdir(os.path.join(project_local_dir, potential_dir))]# Getting list of Annotations in projectimages_annotations = []for current_dataset in project_datasets:# for each dataset in project dataset_annotations_dir_path = os.path.join(project_local_dir, current_dataset, 'ann') annotation_filenames = os.listdir(dataset_annotations_dir_path)for annotation_filename in annotation_filenames:# for each annotation file in datasetwithopen(os.path.join(dataset_annotations_dir_path, annotation_filename), 'r')as ann_file: image_annotations_json = json.load(ann_file) image_annotations = sly.Annotation.from_json(image_annotations_json, project_meta) images_annotations.append(image_annotations)# store annotation# Let's count lemons!count =0class_of_interest ='lemon'for image_annotation in images_annotations:for label in image_annotation.labels:if label.obj_class.name == class_of_interest: count +=1print(f'{project_id=} contains {count}{class_of_interest}(-s)')
