Introduction
In today’s digital age, the need to accurately measure brand visibility during sports event streaming has become increasingly important. As more viewers turn to online platforms for live sports, brands are eager to capitalize on the exposure opportunities. However, gauging the true impact of that exposure—how well a brand is seen and recognized—presents significant challenges. This is where automated tools, particularly APIs, become essential. They offer the capability to analyze video content in real-time, detecting brand presence with a level of precision and efficiency that manual methods simply cannot achieve.
Brand visibility is a key factor in determining the success of sponsorship agreements and the overall value of advertising investments. When a brand is prominently featured during a major sports event, it can significantly boost audience engagement, improve recall, and ultimately lead to greater returns on marketing efforts. However, manually measuring this visibility can be a daunting task. Traditional approaches often rely on subjective evaluations and are time-intensive, leading to inconsistencies and potential inaccuracies. In contrast, automated solutions provide a more streamlined method, enabling brands to quickly and effectively gather actionable insights.
This post aims to guide you through the process of creating a Python script that utilizes the API4AI Brand Marks and Logo Recognition API to analyze sports event videos and calculate brand visibility. We will cover each step of the development process, from setting up the API to running the script and interpreting the results. By the end of this guide, you’ll have a practical tool to help you understand and measure brand visibility in real-time sports event streaming, empowering you to make informed, data-driven decisions for your marketing strategies.
Section 1: Grasping Brand Visibility in Sports Event Streaming
Defining Brand Visibility
In the context of sports event streaming, brand visibility refers to how much a brand is seen and recognized by viewers during a live broadcast. It includes several crucial elements that influence how well a brand can capture the audience's attention and leave a lasting impression. These elements include screen time, which measures how long a brand’s logo is visible; prominence, which considers the logo’s visibility in relation to other on-screen elements; size, which looks at the dimensions of the logo; and clarity, which evaluates the quality and sharpness of the logo as it appears in the video. These factors together determine not only the frequency of a brand’s appearance but also its overall impact on viewers.
Challenges in Measuring Brand Visibility
Despite its significance, measuring brand visibility during sports events presents multiple challenges. Traditional methods often rely on subjective evaluations, where analysts watch broadcasts and manually record brand appearances. This process is not only time-consuming but also susceptible to bias, as different individuals may interpret visibility in various ways. Additionally, the fast-paced nature of sports events—with rapid action, quick camera changes, and varying angles—makes it difficult to accurately capture every instance of brand visibility.
Furthermore, the sheer volume of video data generated during sports events can overwhelm manual analysis efforts. Reviewing hours of footage to identify brand appearances demands significant resources, making it impractical for many organizations. As a result, brands may overlook crucial insights that could improve their marketing strategies and sponsorship effectiveness.
Introduction to Automated Solutions
To tackle these challenges, automated solutions powered by AI and machine learning have revolutionized the way brand visibility is measured. By employing advanced algorithms, AI-driven APIs can analyze video content in real-time, accurately detecting and quantifying brand logos and their visibility metrics. These tools automate the labor-intensive process of manual analysis, allowing brands to gain insights into their visibility during sports events more efficiently and accurately.
Using automated solutions enables brands to continuously monitor their exposure, providing valuable data that can guide marketing decisions, sponsorship negotiations, and overall brand strategies. This shift from manual methods to automated analysis not only improves the accuracy of brand visibility measurements but also empowers brands to quickly adapt to the ever-evolving landscape of sports event streaming.
Section 2: Overview of the API4AI Brand Marks and Logo Recognition API
Introduction to API4AI
API4AI is a state-of-the-art platform that leverages artificial intelligence for various recognition tasks, including image and video analysis. It offers developers and businesses advanced tools to automate and enhance their brand recognition capabilities. Specifically in the context of sports event streaming, the API4AI Brand Marks and Logo Recognition API emerges as a crucial asset for accurately detecting and analyzing brand presence in visual content.
This API is particularly focused on identifying brand logos in images, making it an essential tool for marketers and brand managers who seek to measure visibility and engagement during live broadcasts. Its primary function is to enable real-time image analysis, allowing users to gain actionable insights into their brand’s exposure in dynamic environments.
Capabilities of the API
The Brand Marks and Logo Recognition API is equipped with a robust set of features that enhance its effectiveness in measuring brand visibility:
Detection of Multiple Logos:
The API can identify and analyze several logos within a single frame simultaneously. This feature is especially useful during sports events where multiple brands might be visible at once, enabling comprehensive analysis.
Recognition of Partial Logos:
Unlike traditional recognition methods that require a logo to be fully visible, this API can detect logos even when they are partially obscured. This capability ensures that brands are accurately recognized, despite the dynamic nature of sports broadcasts.
Detection of Rare or Newly Created Logos:
The API is built to recognize not only well-established brands but also rare or newly created logos. This flexibility allows brands to effectively measure their visibility, even if they are new to the sports marketing scene.
Real-Time Processing:
The API delivers real-time analysis, providing users with immediate insights into brand visibility. This instant access to data empowers brands to make swift decisions and adjustments to their marketing strategies.
Advantages Over Traditional Methods
Utilizing the API4AI Brand Marks and Logo Recognition API offers several significant advantages over conventional manual analysis approaches:
Precision: Automated analysis minimizes human error and delivers consistent results, ensuring reliable visibility metrics for brands.
Efficiency: The API processes video data rapidly, enabling brands to analyze large volumes of content in a fraction of the time required by manual methods.
Scalability: With the large volumes of video data generated during sports events, the API's scalability allows brands to analyze multiple events simultaneously without compromising accuracy or speed.
In summary, the API4AI Brand Marks and Logo Recognition API provides brands with powerful tools to automate visibility analysis in sports event streaming, significantly enhancing their ability to measure and optimize their marketing efforts effectively.
Section 3: Developing a Python Script for Video Processing
Configuring the API
To begin using the API4AI Brand Marks and Logo Recognition API, you'll first need to create an account on RapidAPI Hub and obtain an API Key:
Register on RapidAPI Hub: Start by creating an account on the RapidAPI Hub website.
Locate the Brand Recognition API: After logging in, use this link to find the Brand Recognition API.
Subscribe to the API: Choose a subscription plan that suits your needs on the API’s page.
Acquire Your API Key:
After subscribing, head over to your Dashboard.
On the left side of the screen, you’ll find an entry like "default-application_xxxxxx."
Navigate to the Authorization section and copy your Application Key.
Prerequisites
Before you begin writing the script, make sure you have the necessary tools installed:
Python Installation:
Ensure that Python 3.x is installed on your system. You can download it from the official website at python.org.
Necessary Libraries:
The following libraries are critical for this project:
aiohttp for handling HTTP requests.
OpenCV for video processing.
Installing Libraries:
Open your terminal or command prompt and execute the following commands to install the required libraries:
pip install opencv-python aiohttpExtracting Frames from Video
To perform video analysis, the initial step is to extract individual frames. With just a few lines of code, OpenCV can be efficiently used to break a video into separate frames. This process enables the extraction of each frame from a video file, allowing for further analysis or manipulation of the images.
import argparse
import asyncio
import itertools
from pathlib import Path
import aiohttp
import cv2
import numpy
API_URL = 'https://brand-recognition.p.rapidapi.com'
THRESHOLD = 0.5
def split_video_by_frames(path: Path):
"""Split video by frames using OpenCV."""
cap = cv2.VideoCapture(str(path))
while cap.isOpened():
success, image = cap.read()
if not success:
break
yield image
cap.release()Identifying the Target Brand in a Frame
Using the brand recognition API, you can thoroughly analyze an image to detect the presence of the desired brand. This tool enables users to identify and confirm brands within different images, ensuring accurate recognition of branding elements. The aiohttp library is also essential in this process, as it supports the efficient dispatch of asynchronous requests.
async def is_brand_on_photo(img: numpy.ndarray, brand_name: str, api_key: str):
"""Determine if a brand is presented in a photo."""
url = f'{API_URL}/v2/results'
async with aiohttp.ClientSession() as s:
async with s.post(url,
data={'image': cv2.imencode('.jpg', img)[1].tobytes()},
headers={'X-RapidAPI-Key': api_key}) as api_res:
try:
api_res_json = await api_res.json()
except aiohttp.client.ContentTypeError:
print(await api_res.text())
print('Response is not a JSON.')
return False
# Handle processing failure.
if (api_res.status != 200 or
api_res_json['results'][0]['status']['code'] == 'failure'):
print('Image processing failed.')
print(api_res_json)
return False
# Check if your brand found in the photo.
brands = api_res_json['results'][0]['entities'][0]['strings']
return brand_name in brandsHandling Command-Line Arguments
To enhance the user-friendliness of your script, utilize the argparse library to accept command-line inputs for video file paths and brand names.
def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('--api-key', help='Rapid API key.', required=True) # Get your token at https://rapidapi.com/api4ai-api4ai-default/api/brand-recognition/pricing
parser.add_argument('--brand-name', required=True)
parser.add_argument('--video', type=Path,
help='Path to a video.')
return parser.parse_args()Core Function
The core function coordinates the entire process, including frame extraction, sending frames for recognition, and calculating visibility metrics. By leveraging asyncio and aiohttp, you can efficiently manage the asynchronous processing of all frames pulled from a video.
async def main():
"""
Script entry function.
"""
args = parse_args()
brands_frames = await asyncio.gather(
*[is_brand_on_photo(frame, args.brand_name, args.api_key)
for frame in split_video_by_frames(args.video)]
)
count_brand_visible = sum(brands_frames)
brand_presented_percent = count_brand_visible / len(brands_frames) * 100
print('Frame count: ', len(brands_frames))
print(f'Brand is presented in {count_brand_visible} frames.')
print(f'Brand presented on {brand_presented_percent}% of the video.')
if __name__ == '__main__':
asyncio.run(main())
Complete Python Code
Here’s the complete Python script that integrates all the previous steps:
"""
Calculate the percentage of a video on which brand advertising is visible.
Run script:
`python3 main.py --api-key <RAPID_API_KEY> --brand-name <NAME_OF_YOUR_BRAND> <PATH_TO_VIDEO>
"""
import argparse
import asyncio
import itertools
from pathlib import Path
import aiohttp
import cv2
import numpy
API_URL = 'https://brand-recognition.p.rapidapi.com'
THRESHOLD = 0.5
def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('--api-key', help='Rapid API key.', required=True) # Get your token at https://rapidapi.com/api4ai-api4ai-default/api/brand-recognition/pricing
parser.add_argument('--brand-name', required=True)
parser.add_argument('--video', type=Path,
help='Path to a video.')
return parser.parse_args()
async def is_brand_on_photo(img: numpy.ndarray, brand_name: str, api_key: str):
"""Determine if a brand is presented in a photo."""
url = f'{API_URL}/v2/results'
async with aiohttp.ClientSession() as s:
async with s.post(url,
data={'image': cv2.imencode('.jpg', img)[1].tobytes()},
headers={'X-RapidAPI-Key': api_key}) as api_res:
try:
api_res_json = await api_res.json()
except aiohttp.client.ContentTypeError:
print(await api_res.text())
print('Response is not a JSON.')
return False
# Handle processing failure.
if (api_res.status != 200 or
api_res_json['results'][0]['status']['code'] == 'failure'):
print('Image processing failed.')
print(api_res_json)
return False
# Check if your brand found in the photo.
brands = api_res_json['results'][0]['entities'][0]['strings']
return brand_name in brands
def split_video_by_frames(path: Path):
"""Split video by frames using OpenCV."""
cap = cv2.VideoCapture(str(path))
while cap.isOpened():
success, image = cap.read()
if not success:
break
yield image
cap.release()
async def main():
"""
Script entry function.
"""
args = parse_args()
brands_frames = await asyncio.gather(
*[is_brand_on_photo(frame, args.brand_name, args.api_key)
for frame in split_video_by_frames(args.video)]
)
count_brand_visible = sum(brands_frames)
brand_presented_percent = count_brand_visible / len(brands_frames) * 100
print('Frame count: ', len(brands_frames))
print(f'Brand is presented in {count_brand_visible} frames.')
print(f'Brand presented on {brand_presented_percent}% of the video.')
if __name__ == '__main__':
asyncio.run(main())Script Testing
Let’s try out the script. We've prepared a short video featuring a few scenes with various logos, which you can download here.
Several frames from videos illustrating different logos: Red Bull, Nike, NEXEN TIRE, G2A, etc.
Now let's try to understand what fraction of the video contains the Red Bull logo displayed:
python3 main.py --api-key YOUR_API_KEY --brand-name "Red Bull" --video ./qa_video.mp4
Frame count: 196
Brand is presented in 129 frames.
Brand presented on 65.81632653061224% of the video.Not bad result for Red Bull - it is presented on more than 50% of video.
Let’s calculate similar metric for several more brands:
python3 main.py --api-key YOUR_API_KEY --brand-name "Nike" --video ./qa_video.mp4
Frame count: 196
Brand is presented in 67 frames.
Brand presented on 34.183673469387756% of the video.
python3 main.py --api-key YOUR_API_KEY --brand-name "Nexen Tire" --video ./qa_video.mp4
Frame count: 196
Brand is presented in 60 frames.
Brand presented on 30.612244897959183% of the video.
python3 main.py --api-key YOUR_API_KEY --brand-name "G2A" --video ./qa_video.mp4
Frame count: 196
Brand is presented in 64 frames.
Brand presented on 32.6530612244898% of the video.This is, of course, a very basic example demonstrating how different brands can be detected in a video. Feel free to customize the script to calculate additional metrics and gain deeper insights into brand presence.
In particular, considering logo size information can be very beneficial, as the API provides details on the size category of each logo, as mentioned in the documentation. Understanding these size variations can offer valuable context for assessing brand visibility and prominence within the video.
Furthermore, you can easily modify the script to generate detailed information on all the brands identified in the video with just a few simple tweaks. This added functionality can enhance your analysis by enabling you to track multiple brands and their appearances throughout the footage.
Conclusion
In this blog post, we explored the process of calculating brand visibility in sports event video streaming using Python and the API4AI Brand Marks and Logo Recognition API. We started by examining the concept of brand visibility and its importance in sports marketing. We then walked through the practical steps for creating a Python script to analyze video content, including setting up the API, preparing the necessary tools, extracting frames from the video, detecting brand presence in those frames, and calculating visibility metrics. By utilizing these advanced tools, you can gain valuable insights into how well your brand is recognized during live sports events.
We encourage you to test the script on different videos to observe how brand visibility can vary across different events and scenarios. Feel free to explore additional API features, such as detecting the size category of each logo. This experimentation will deepen your understanding and help you tailor your marketing strategies based on real-time data.
In conclusion, the importance of using AI and APIs for modern brand visibility measurement cannot be overstated. These tools automate data collection and analysis, providing more accurate and timely insights. As technology continues to evolve, these tools will increasingly shape how brands approach visibility measurement and marketing strategy. Embrace the future of analytics, and empower your brand to excel in the competitive world of sports marketing.
At ssstiktok, we make it easy to download your favorite TikTok videos without the hassle of watermarks. Whether you're saving videos for personal use or to share with others, our platform ensures that you get clean, high-quality videos every time. With just a few clicks, you can download unduh tiktok content directly to your device, free from any distracting logos or watermarks. Enjoy the freedom to use and share your videos as you like, with it.