Project 6: Multimodal Video Analysis and Summarization
Step 2: Extract Video Frames
Extract frames from videos to analyze their visual content. This process involves sampling individual images from the video at regular intervals (e.g., every few milliseconds or seconds) to create a sequence of still frames.
These frames serve as the foundation for visual analysis, allowing the system to detect objects, recognize actions, and understand scene composition. The sampling rate can be adjusted based on the video's complexity and the desired level of detail in the analysis.
import cv2
def extract_frames(video_path, frame_rate=10):
cap = cv2.VideoCapture(video_path)
frames = []
count = 0
success = True
while success:
success, frame = cap.read()
if count % frame_rate == 0 and success:
frames.append(cv2.resize(frame, (224, 224))) # Resize for model compatibility
count += 1
cap.release()
return frames
# Example usage
video_path = "example_video.mp4" # Replace with your video file
frames = extract_frames(video_path)
print(f"Extracted {len(frames)} frames.")Let me explain this frame extraction code:
This code defines a function extract_frames that processes video files to extract individual frames. Here's how it works:
- Function Setup:
- Takes two parameters: video_path (location of video file) and frame_rate (sampling rate, defaulted to 10)
- Uses OpenCV (cv2) for video processing
- Core Functionality:
- Opens the video using cv2.VideoCapture
- Creates an empty list to store frames
- Reads the video frame by frame
- Samples frames based on the frame_rate parameter (every 10th frame by default)
- Resizes each frame to 224x224 pixels for compatibility with machine learning models
The sampling rate can be adjusted depending on how detailed you need the analysis to be and the complexity of the video content.
The function returns a list of processed frames that can then be used for further visual analysis, such as object detection and action recognition.
Step 2: Extract Video Frames
Extract frames from videos to analyze their visual content. This process involves sampling individual images from the video at regular intervals (e.g., every few milliseconds or seconds) to create a sequence of still frames.
These frames serve as the foundation for visual analysis, allowing the system to detect objects, recognize actions, and understand scene composition. The sampling rate can be adjusted based on the video's complexity and the desired level of detail in the analysis.
import cv2
def extract_frames(video_path, frame_rate=10):
cap = cv2.VideoCapture(video_path)
frames = []
count = 0
success = True
while success:
success, frame = cap.read()
if count % frame_rate == 0 and success:
frames.append(cv2.resize(frame, (224, 224))) # Resize for model compatibility
count += 1
cap.release()
return frames
# Example usage
video_path = "example_video.mp4" # Replace with your video file
frames = extract_frames(video_path)
print(f"Extracted {len(frames)} frames.")Let me explain this frame extraction code:
This code defines a function extract_frames that processes video files to extract individual frames. Here's how it works:
- Function Setup:
- Takes two parameters: video_path (location of video file) and frame_rate (sampling rate, defaulted to 10)
- Uses OpenCV (cv2) for video processing
- Core Functionality:
- Opens the video using cv2.VideoCapture
- Creates an empty list to store frames
- Reads the video frame by frame
- Samples frames based on the frame_rate parameter (every 10th frame by default)
- Resizes each frame to 224x224 pixels for compatibility with machine learning models
The sampling rate can be adjusted depending on how detailed you need the analysis to be and the complexity of the video content.
The function returns a list of processed frames that can then be used for further visual analysis, such as object detection and action recognition.
Step 2: Extract Video Frames
Extract frames from videos to analyze their visual content. This process involves sampling individual images from the video at regular intervals (e.g., every few milliseconds or seconds) to create a sequence of still frames.
These frames serve as the foundation for visual analysis, allowing the system to detect objects, recognize actions, and understand scene composition. The sampling rate can be adjusted based on the video's complexity and the desired level of detail in the analysis.
import cv2
def extract_frames(video_path, frame_rate=10):
cap = cv2.VideoCapture(video_path)
frames = []
count = 0
success = True
while success:
success, frame = cap.read()
if count % frame_rate == 0 and success:
frames.append(cv2.resize(frame, (224, 224))) # Resize for model compatibility
count += 1
cap.release()
return frames
# Example usage
video_path = "example_video.mp4" # Replace with your video file
frames = extract_frames(video_path)
print(f"Extracted {len(frames)} frames.")Let me explain this frame extraction code:
This code defines a function extract_frames that processes video files to extract individual frames. Here's how it works:
- Function Setup:
- Takes two parameters: video_path (location of video file) and frame_rate (sampling rate, defaulted to 10)
- Uses OpenCV (cv2) for video processing
- Core Functionality:
- Opens the video using cv2.VideoCapture
- Creates an empty list to store frames
- Reads the video frame by frame
- Samples frames based on the frame_rate parameter (every 10th frame by default)
- Resizes each frame to 224x224 pixels for compatibility with machine learning models
The sampling rate can be adjusted depending on how detailed you need the analysis to be and the complexity of the video content.
The function returns a list of processed frames that can then be used for further visual analysis, such as object detection and action recognition.
Step 2: Extract Video Frames
Extract frames from videos to analyze their visual content. This process involves sampling individual images from the video at regular intervals (e.g., every few milliseconds or seconds) to create a sequence of still frames.
These frames serve as the foundation for visual analysis, allowing the system to detect objects, recognize actions, and understand scene composition. The sampling rate can be adjusted based on the video's complexity and the desired level of detail in the analysis.
import cv2
def extract_frames(video_path, frame_rate=10):
cap = cv2.VideoCapture(video_path)
frames = []
count = 0
success = True
while success:
success, frame = cap.read()
if count % frame_rate == 0 and success:
frames.append(cv2.resize(frame, (224, 224))) # Resize for model compatibility
count += 1
cap.release()
return frames
# Example usage
video_path = "example_video.mp4" # Replace with your video file
frames = extract_frames(video_path)
print(f"Extracted {len(frames)} frames.")Let me explain this frame extraction code:
This code defines a function extract_frames that processes video files to extract individual frames. Here's how it works:
- Function Setup:
- Takes two parameters: video_path (location of video file) and frame_rate (sampling rate, defaulted to 10)
- Uses OpenCV (cv2) for video processing
- Core Functionality:
- Opens the video using cv2.VideoCapture
- Creates an empty list to store frames
- Reads the video frame by frame
- Samples frames based on the frame_rate parameter (every 10th frame by default)
- Resizes each frame to 224x224 pixels for compatibility with machine learning models
The sampling rate can be adjusted depending on how detailed you need the analysis to be and the complexity of the video content.
The function returns a list of processed frames that can then be used for further visual analysis, such as object detection and action recognition.