In this article, we will learn how to iterate through a project in , which is stored locally on your machine. It is one of the most frequent operations in Superviely Apps and python automation scripts. You will see how easy it is to get all the necessary information from the project, as well as how quickly you can visualize the contents of the project even without internet access.
Everything you need to reproduce : source code, Visual Studio code configuration, and a shell script for creating venv.
In this guide we will go through the following steps:
**** Get a with labeled lemons and kiwis.
**** Download the demo project to your local machine in Supervisely format using app in Supervisely Ecosystem.
**** Run .
1. Demo project
If you don't have any projects in Supervisely format on your local machine, go to the ecosystem and add the demo project 🍋 Lemons annotated to your current workspace.
Add demo project "Lemons annotated" to your workjspace
2. Download project in Supervisely format
Extract the archive to any folder and check that it has the following structure:
The project root directory contains the meta.json file with the project meta information and directories for each dataset. Each dataset directory contains two subdirectories: img with images and ann with annotations in Supervisely format.
3. Python script
To start debugging you need to:
Source code:
import os
import json
import supervisely as sly
from tqdm import tqdm
input = "./lemons-fs"
output = "./results"
os.makedirs(output, exist_ok=True)
# Creating Supervisely project from local directory.
project = sly.Project(input, sly.OpenMode.READ)
print("Opened project: ", project.name)
print("Number of images in project:", project.total_items)
# Showing annotations tags and classes.
print(project.meta)
# Iterating over classes in project, showing their names, geometry types and colors.
for obj_class in project.meta.obj_classes:
print(
f"Class '{obj_class.name}': geometry='{obj_class.geometry_type}', color='{obj_class.color}'",
)
# Iterating over tags in project, showing their names and colors.
for tag in project.meta.tag_metas:
print(f"Tag '{tag.name}': color='{tag.color}'")
print("Number of datasets (aka folders) in project:", len(project.datasets))
progress = tqdm(project.datasets, desc="Processing datasets")
for dataset in project.datasets:
# Iterating over images in dataset, using the paths to the images and annotations.
for item_name, image_path, ann_path in dataset.items():
print(f"Item '{item_name}': image='{image_path}', ann='{ann_path}'")
ann_json = json.load(open(ann_path))
ann = sly.Annotation.from_json(ann_json, project.meta)
img = sly.image.read(image_path) # rgb - order
for label in ann.labels:
# Drawing each label on the image.
label.draw(img)
res_image_path = os.path.join(output, item_name)
sly.image.write(res_image_path, img)
# Or alternatively draw annotation (all labels at once) preview with
# ann.draw_pretty(img, output_path=res_image_path)
progress.update(1)
Output
The script above produces the following output:
Opened project: lemons-fs
Number of images in project: 6
ProjectMeta:
Object Classes
+-------+--------+----------------+--------+
| Name | Shape | Color | Hotkey |
+-------+--------+----------------+--------+
| kiwi | Bitmap | [255, 0, 0] | |
| lemon | Bitmap | [81, 198, 170] | |
+-------+--------+----------------+--------+
Tags
+----------+------------+-----------------+--------+---------------+--------------------+
| Name | Value type | Possible values | Hotkey | Applicable to | Applicable classes |
+----------+------------+-----------------+--------+---------------+--------------------+
+----------+------------+-----------------+--------+---------------+--------------------+
Class 'kiwi': geometry='<class 'supervisely.geometry.bitmap.Bitmap'>', color='[255, 0, 0]'
Class 'lemon': geometry='<class 'supervisely.geometry.bitmap.Bitmap'>', color='[81, 198, 170]'
Number of datasets (aka folders) in project: 1
Item 'IMG_4451.jpeg': image='./lemons-fs/ds1/img/IMG_4451.jpeg', ann='./lemons-fs/ds1/ann/IMG_4451.jpeg.json'
Item 'IMG_0748.jpeg': image='./lemons-fs/ds1/img/IMG_0748.jpeg', ann='./lemons-fs/ds1/ann/IMG_0748.jpeg.json'
Item 'IMG_1836.jpeg': image='./lemons-fs/ds1/img/IMG_1836.jpeg', ann='./lemons-fs/ds1/ann/IMG_1836.jpeg.json'
Item 'IMG_3861.jpeg': image='./lemons-fs/ds1/img/IMG_3861.jpeg', ann='./lemons-fs/ds1/ann/IMG_3861.jpeg.json'
Item 'IMG_2084.jpeg': image='./lemons-fs/ds1/img/IMG_2084.jpeg', ann='./lemons-fs/ds1/ann/IMG_2084.jpeg.json'
Item 'IMG_8144.jpeg': image='./lemons-fs/ds1/img/IMG_8144.jpeg', ann='./lemons-fs/ds1/ann/IMG_8144.jpeg.json'
As a result of running the script there also will be created a directory results, which will contain the images with drawn annotations.
Go to the ecosystem and launch the app . Select the demo project, you created in the previous step (or use any existing project in Supervisely), and download the result archive to your local machine.
Run Export to Supervisely format app
Download the result archive
Clone the
Create by running the script
Set the correct path to the downloaded and extracted project on your local machine in the script
