# 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/tokens
AGENT_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 sly
import os
import json
from dotenv import load_dotenv # pip install python-dotenv
# don't forget to add to requirements.txt!
# Loading env files
load_dotenv("../debug.env")
load_dotenv("../secret_debug.env", override=True)
# Extracting variables
address = 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 object
api = sly.Api(address, token)
# Downloading Project
project_local_dir = './project_local_dir/'
if sly.fs.dir_exists(project_local_dir):
sly.fs.clean_dir(project_local_dir) # clean dir before download
sly.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.json
project_meta = sly.ProjectMeta.from_json(project_meta_json)
# Getting list of datasets folders in project
project_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 project
images_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 dataset
with open(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 = 0
class_of_interest = 'lemon'
for image_annotation in images_annotations:
for label in image_annotation.labels:
if label.obj_class.name == class_of_interest:
count += 1
print(f'{project_id=} contains {count} {class_of_interest}(-s)')
