Videos
In this tutorial we will focus on working with videos using Supervisely SDK.
You will learn how to:
git clone https://github.com/supervisely-ecosystem/tutorial-video.git
cd tutorial-video
./create_venv.sh
Step 3. Open repository directory in Visual Studio Code.
code -r .
Step 4. Change workspace ID in
local.env file by copying the ID from the context menu of the workspace.WORKSPACE_ID=654 # ⬅️ change value
Step 5. Start debugging
src/main.py.import os
from dotenv import load_dotenv
from pprint import pprint
import supervisely as sly
First, we load environment variables with credentials and init API for communicating with Supervisely Instance.
if sly.is_development():
load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))
api = sly.Api()
In this tutorial, you will need an workspace ID that you can get from environment variables. Learn more here
workspace_id = sly.env.workspace_id()
Create new project.
Source code:
project = api.project.create(
workspace_id, "Animals", type=ProjectType.VIDEOS, change_name_if_conflict=True
)
print(f"Project ID: {project.id}")
Output:
# Project ID: 15599
Create new dataset.
Source code:
dataset = api.dataset.create(project.id, "Birds")
print(f"Dataset ID: {dataset.id}")
Output:
# Dataset ID: 53465
Source code:
original_dir = "src/videos/original"
path = os.path.join(original_dir, "Penguins.mp4")
video = api.video.upload_path(
dataset.id,
name="Penguins",
path=path
)
print(f'Video "{video.name}" uploaded to Supervisely with ID:{video.id}')
Output:
# Video "Penguins" uploaded to Supervisely platform with ID:17539140
✅ Supervisely API allows uploading multiple videos in a single request. The code sample below sends fewer requests and it leads to a significant speed-up of our original code.
Source code:
names = ["Flamingo.mp4", "Swans.mp4", "Toucan.mp4"]
paths = [os.path.join(original_dir, name) for name in names]
upload_info = api.video.upload_paths(dataset.id, names, paths)
print(f"{len(upload_info)} videos successfully uploaded to Supervisely platform")
Output:
# 3 videos successfully uploaded to Supervisely platform
Get information about video from Supervisely by id.
Source code:
video_info = api.video.get_info_by_id(video.id)
print(video_info)
Output:
# VideoInfo(
# id=17539140,
# name="Penguins",
# hash="6aTUVnuyfGqIuMxH8l1t1yvkmn/9iuWbHUOE7iebhYk=",
# link=None,
# team_id=435,
# workspace_id=654,
# project_id=15748,
# dataset_id=53751,
# path_original="/h5un6l2bnaz1vj8a9qgms4-public/videos/U/O/LF/T7tiPVoBUyFaM.mp4",
# frames_to_timecodes=[],
# frames_count=413,
# frame_width=640,
# frame_height=360,
# created_at="2022-12-21T23:09:54.191Z",
# updated_at="2022-12-21T23:09:54.191Z",
# tags=[],
# file_meta={},
# custom_data={},
# processing_path="1/1703793",
# )
You can also get information about video from Supervisely by name.
Source code:
video_info_by_name = api.video.get_info_by_name(dataset.id, video.name)
print(f"Video name - '{video_info_by_name.name}'")
Output:
# Video name - 'Penguins'
Get information about video from Supervisely by id.
Source code:
video_info_list = api.video.get_list(dataset.id)
print(f"{len(video_info_list)} videos information received.")
Output:
# 4 videos information received.
Download video from Supervisely to local directory by id. Source code:
save_path = os.path.join(result_dir, f"{video_info.name}.mp4")
api.video.download_path(video_info.id, save_path)
print(f"Video has been successfully downloaded to '{save_path}'")
Output:
# Video has been successfully downloaded to 'src/videos/result/Penguins.mp4'
Source code:
video_path = "src/videos/result/Penguins.mp4"
file_info = sly.video.get_info(video_path)
pprint(file_info)
Output:
{
"duration": 16.52,
"formatName": "mov,mp4,m4a,3gp,3g2,mj2",
"size": "1599101",
"streams": [
{
"codecName": "h264",
"codecType": "video",
"duration": 16.52,
"framesCount": 413,
"framesToTimecodes": [],
"height": 360,
"index": 0,
"rotation": 0,
"startTime": 0,
"width": 640
}
]
}
Source code:
video_info = api.video.get_info_by_id(video.id)
pprint(video_info.file_meta)
Output:
{
"codecName": "h264",
"codecType": "video",
"duration": 16.52,
"formatName": "mov,mp4,m4a,3gp,3g2,mj2",
"framesCount": 413,
"framesToTimecodes": [],
"height": 360,
"index": 0,
"mime": "video/mp4",
"rotation": 0,
"size": "1599101",
"startTime": 0,
"streams": [],
"width": 640
}
Download single frame of video as image from Supervisely to local directory.
Source code:
frame_idx = 15
file_name = "frame.png"
save_path = os.path.join(result_dir, file_name)
api.video.frame.download_path(video_info.id, frame_idx, save_path)
print(f"Video frame has been successfully downloaded as image to '{save_path}'")
Output:
# Video frame has been successfully downloaded as image to 'src/videos/result/frame.png'
Download multiple frames in a single requests from Supervisely and save as images to local directory.
Source code:
frame_indexes = [5, 10, 20, 30, 45]
save_paths = [os.path.join(result_dir, f"frame_{idx}.png") for idx in frame_indexes]
api.video.frame.download_paths(video_info.id, frame_indexes, save_paths)
print(f"{len(frame_indexes)} images has been successfully downloaded to '{save_path}'")
Output:
# 5 images has been successfully downloaded to 'src/videos/result/frame.png'
You can also download video frame as RGB NumPy matrix.
Source code:
video_frame_np = api.video.frame.download_np(video_info.id, frame_idx)
print(f"Video frame downloaded as RGB NumPy matrix. Frame shape: {video_frame_np.shape}")
Output:
# Video frame downloaded as RGB NumPy matrix. Frame shape: (360, 640, 3)
Download multiple frames in a single requests from Supervisely as RGB NumPy matrix.
Source code:
video_frames_np = api.video.frame.download_nps(video_info.id, frame_indexes)
print(f"{len(video_frames_np)} video frames downloaded in RGB NumPy matrix.")
Output:
# 5 video frames downloaded as RGB NumPy matrix.
Remove video from Supervisely by id
Source code:
api.video.remove(video_info.id)
print(f"Video (ID: {video_info.id}) successfully removed.")
Output:
# Video (ID: 17536607) has been successfully removed.
Remove list of videos from Supervisely by ids.
Source code:
videos_to_remove = api.video.get_list(dataset.id)
remove_ids = [video.id for video in videos_to_remove]
api.video.remove_batch(remove_ids)
print(f"{len(videos_to_remove)} videos successfully removed.")
Output:
# 3 videos have been successfully removed.
Note: Only basic video codecs are available in the Community Edition, for additional video codecs you can try the Enterprise Edition.
The Enterprise Edition allows you to use the full range of extensions, containers and codecs listed below without any limits.
- extensions: .avi, .mp4, .3gp, .flv, .webm, .wmv, .mov, .mkv
- containers: mp4, webm, ogg, ogv
- codecs: h264, vp8, vp9
In the Community Edition, it is recommended to use vp9, h264 codecs with mp4 container.
In case you need to exract frames from videos, you should be aware of one important detail of the OpenCV library. According to issue #15499, different versions of the OpenCV library have different values of the
CAP_PROP_ORIENTATION_AUTO flag. This may cause that VideoCapture to ignore video orientation metadata. To avoid incorrect extraction of frames from videos, it is recommended to directly define flag CAP_PROP_ORIENTATION_AUTO:cap = cv2.VideoCapture(filepath)
# set CAP_PROP_ORIENTATION_AUTO flag for VideoCapture
cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 1)
while cap.isOpened():
success, frame = cap.read()
if not success:
print("Can't receive frame (stream end?). Exiting ...")
break
# cv2.imshow(f"{frame.shape[:2]}", frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# Release everything if job is finished
cap.release()
# cv2.destroyAllWindows()
Last modified 2mo ago