5 Best Ways to Play a Video in Reverse Mode Using Python OpenCV

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💡 Problem Formulation: Playing videos in reverse is an interesting task that can be useful in multiple domains such as video editing, content creation, and education. For instance, a developer may want to create a tool that plays an educational video in reverse to analyze certain moments more closely. To achieve this, one can utilize Python’s OpenCV library, which provides robust video processing capabilities. The goal is to take a video file as input and output a video that plays in reverse.

Method 1: Read Frames and Reverse

One straightforward way to play a video in reverse using Python and OpenCV is by reading the video frames into a list, reversing the order, and then writing them back to a video file. This method relies on OpenCV’s cv2.VideoCapture and cv2.VideoWriter classes for reading and writing video frames respectively.

Here’s an example:

import cv2

# Capture the video
cap = cv2.VideoCapture('input_video.mp4')
frames = []
success, frame = cap.read()

# Read all frames
while success:
    frames.append(frame)
    success, frame = cap.read()

# Reverse the frames
frames.reverse()

# Write the frames back to a new video
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter('reversed_video.mp4', fourcc, 30, (frames[0].shape[1], frames[0].shape[0]))

for frame in frames:
    out.write(frame)

cap.release()
out.release()

The output will be a video saved to ‘reversed_video.mp4’, which plays in reverse.

This snippet first reads all frames from the input video and stores them in a list. After reversing the list, it initializes a video writer object with the same dimensions and frame rate as the input video, and writes the reversed frames to a new video file.

Method 2: Using Frame Indices

Another method to play video in reverse is to loop through the video by frame indices in reverse order. This approach uses the cv2.VideoCapture class’s set() method to access frames directly without storing them all in memory, which can be more memory-efficient for large videos.

Here’s an example:

import cv2

cap = cv2.VideoCapture('input_video.mp4')
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter('reversed_video.mp4', fourcc, 30, (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))

# Go through frames in reverse order
for i in range(frame_count - 1, -1, -1):
    cap.set(cv2.CAP_PROP_POS_FRAMES, i)
    ret, frame = cap.read()
    if ret:
        out.write(frame)

cap.release()
out.release()

The output will be a reversed version of the input video ‘input_video.mp4’ named ‘reversed_video.mp4’.

This code iterates over the video frames using a reverse frame index, reads each frame, and writes it immediately to the output video without storing them in a list, significantly reducing memory usage.

Method 3: Decoding and Encoding

To avoid loading all frames into memory at once, you can combine the reading and writing processes. This method decodes each frame, stores it temporarily, and then writes it in reverse order. It’s a balance between memory efficiency and code complexity compared to the first two methods.

Here’s an example:

import cv2
import numpy as np

cap = cv2.VideoCapture('input_video.mp4')
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter('reversed_video.mp4', fourcc, 30, (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))

# Store the last frame's index
last_frame = frame_count - 1

# Loop through the video
for i in range(last_frame, -1, -1):
    cap.set(cv2.CAP_PROP_POS_FRAMES, i)
    ret, frame = cap.read()
    if ret:
        # Write the frame immediately
        out.write(frame)

cap.release()
out.release()

The output will be a video named ‘reversed_video.mp4’ with the content playing backwards.

Here the video is still processed frame by frame, but the frames are written to the output file as soon as they are read. This approach can decode and encode videos without the need for significant memory, making it suitable for large video files.

Method 4: Using Array Slicing

Python’s array slicing can also be used in combination with NumPy, which is often used alongside OpenCV. This method reads the frames into a NumPy array, which allows us to reverse the array using slicing. It is a compact and memory-intensive way, similar to Method 1, but it is more elegant and possibly faster due to NumPy’s optimizations.

Here’s an example:

import cv2
import numpy as np

# Capture video
cap = cv2.VideoCapture('input_video.mp4')
success, frame = cap.read()
frames = []

while success:
    frames.append(frame)
    success, frame = cap.read()

# Convert to Numpy array and reverse
frames = np.array(frames)[::-1]

# Write frames to output video
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter('reversed_video.mp4', fourcc, 30, (frames[0].shape[1], frames[0].shape[0]))
for frame in frames:
    out.write(frame)

cap.release()
out.release()

The output is ‘reversed_video.mp4’, playing the original video backwards.

This snippet first collects frames into a list, converts it to a NumPy array, reverses the array with slicing, then writes each frame back to the output file. It’s useful for small to moderately sized videos.

Bonus One-Liner Method 5: Using List Comprehension

For those who love Pythonic one-liners, here’s a bonus method that combines the video capture and frame reversal process into a single line using list comprehension. This method is concise but not recommended for large videos due to memory constraints.

Here’s an example:

import cv2

cap = cv2.VideoCapture('input_video.mp4')
frames = [frame for success, frame in iter(lambda: cap.read(), (False, None))][::-1]

fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter('reversed_video.mp4', fourcc, 30, (frames[0].shape[1], frames[0].shape[0]))

for frame in frames:
    out.write(frame)

cap.release()
out.release()

And the output is a neatly reversed ‘reversed_video.mp4’.

This one-liner reads frames into a list using a list comprehension with an iterable that stops when the read frame isn’t successful. It then reverses the list in place and writes out the frames.

Summary/Discussion

  • Method 1: Read Frames and Reverse. Easy to understand and implement. It’s suitable for short or medium-length videos. However, for long videos, it consumes a lot of memory since it stores all frames in memory.
  • Method 2: Using Frame Indices. More memory efficient as it doesn’t require holding all frames in memory at once. However, random access of video frames might be slower if the underlying video codec doesn’t support efficient frame seeking.
  • Method 3: Decoding and Encoding. This approach also avoids keeping all frames in memory and is suitable for long videos. It’s a bit more complex but provides a good balance between memory and performance.
  • Method 4: Using Array Slicing. A compact approach that leverages NumPy’s performance benefits. While it’s a more advanced technique, it is still memory intensive and best used for videos of manageable size.
  • Bonus One-Liner Method 5: Using List Comprehension. A Pythonic and attractive one-liner that is best used for small videos due to its high memory usage.