VideoDB Documentation
Examples and Tutorials
AWS Rekognition and VideoDB - Effortlessly Remove Inappropriate Content from Video
Video content moderation is complex. While today's computer vision and AI have alleviated the manual burden, modifying video streams instantly and intelligently for moderated content remains challenging.
VideoDB ‘s next gen tech helps you leave behind the tedious processes of conventional video editing, and save tons of cost and time for application developers.
Key components of this blog are:
Setup
Install required packages:
!pip install boto3 pytube requests videodb
Helper Functions
We've prepared a handy download_video_yt function to download YouTube videos in high resolution.
import pytube
import os
import time
# Downlaods Youtube video
def download_video_yt(youtube_url, output_file="video.mp4"):
youtube_object = pytube.YouTube(youtube_url)
video_stream = youtube_object.streams.get_highest_resolution()
video_stream.download(filename=output_file)
print(f"Downloaded video to: {output_file}")
return output_file
Downloading Media
video_url_yt = "https://www.youtube.com/watch?v=Xa7UaHgOGfM"
video_output = "video_breaking_bad.mp4"
download_video_yt(video_url_yt, video_output)
Configuration
We need to configure both AWS and VideoDB.
AWS Configuration
AWS Rekognition is a paid API, so please select your YouTube video carefully. Choosing a larger video may incur additional charges.
AWS secrets : aws_secret_key_id , aws_secret_access_key and aws_reigon
Ensure your AWS user has access to necessary policies:
AmazonRekognitionFullAccess and AmazonS3FullAccess
import boto3
aws_access_key_id= os.environ.get('AWS_KEY_ID', "")
aws_secret_access_key = os.environ.get("AWS_KEY_SECRET", "")
region_name = os.environ.get("AWS_REIGON", "")
bucket_name = "videorekog"
rekognition_client = boto3.client(
"rekognition",
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
region_name=region_name,
)
s3 = boto3.client('s3',
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
region_name=region_name,
)
Analyzing the Video for Inappropriate Content
Using the Rekognition API
# Define function to start face search in video
def start_content_moderation(video_path, bucket_name):
response = rekognition_client.start_content_moderation(
Video={"S3Object": {"Bucket": bucket_name, "Name": video_path}}
)
return response["JobId"]
# Define function to get face search results
def get_content_moderation(job_id):
wait_for = 5
pagination_finished = False
next_token = ""
response = {
"ModerationLabels" : []
}
while not pagination_finished:
print(next_token)
moderation_res = rekognition_client.get_content_moderation(JobId=job_id, NextToken = next_token)
status = moderation_res["JobStatus"]
next_token = moderation_res.get("NextToken", "")
if status == "IN_PROGRESS":
time.sleep(wait_for)
elif status == "SUCCEEDED" :
print(moderation_res)
if (not next_token):
pagination_finished = True
response["ModerationLabels"].extend(moderation_res["ModerationLabels"])
return response
#Upload Target video to S3 Bucket
s3.create_bucket(Bucket=bucket_name)
s3.upload_file(video_output, bucket_name, video_output)
#Start Content Moderation using Rekognition API
job_id = start_content_moderation(video_output, bucket_name )
print(job_id)
moderation_res = get_content_moderation(job_id)
print(moderation_res)
Preparing Clips Timestamps
The Rekognition API flags moments in a video that are inappropriate, unwanted, or offensive by providing timestamps. Our objective is to consolidate timestamps that belong to the same sequence.
Though the offers a method for this, we will employ a more straightforward strategy.
Then, we need to do a compliment operation on video from inappropriate clips to get appropriate and safe content clips. Feel free to adjust the threshold and padding settings to optimize the results.
timestamps = []
threshold = 1
padding = 1
for label in moderation_res["ModerationLabels"]:
timestamp = label["Timestamp"]/1000
timestamps.append(round(timestamp))
def merge_timestamps(numbers, threshold, padding):
grouped_numbers = []
end_last_segment = 0
current_group = [numbers[0]]
for i in range(1, len(numbers)):
# if timestamp is with threshold from previous timestamp, consolidate them under same group
if numbers[i] - numbers[i-1] <= threshold:
current_group.append(numbers[i])
# else put last group's end and this group's start in result clips
else:
start_segment = current_group[0] - padding
end_segment = current_group[-1] + padding
grouped_numbers.append([end_last_segment, start_segment])
end_last_segment = end_segment
current_group = [numbers[i]]
grouped_numbers.append([end_last_segment, numbers[-1]])
return grouped_numbers
shots = merge_timestamps(timestamps, threshold=threshold, padding=padding)
print(shots)
Removing inappropriate content from video using VideoDB
The idea behind VideoDB is straightforward: It functions as a database specifically for videos. Similar to how you upload tables or JSON data to a standard database, you can upload your videos to VideoDB.
You can also retrieve your videos through queries, much like accessing regular data from a database.
VideoDB enables you to swiftly create clips from your videos, ensuring a ⚡️ process, just like retrieving text data from a db.
Next, we will compile a master clip composed of smaller segments that depict appropriate contents only (i.e filter and exclude the clips with inappropriate content identified earlier)
# upload the video to db
video_url_yt = "https://www.youtube.com/watch?v=Xa7UaHgOGfM"
video = conn.upload(url=video_url_yt)
# generate a stream link of safe_shots by passing values in timeline
stream_link = video.generate_stream(timeline=shots)
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