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CSN CIT 095 - Python for Multimedia Part 2

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CHAPTER 6: Extract Frames from Video in PythonMethod 1: Extracting Frames using OpenCVMethod 2: Extracting Frames using MoviePyConclusionCHAPTER 7: Concatenate Audio Files in PythonMethod 1: Using MoviePyMethod 2: Using WaveMethod 3: Using PyDubConclusionCHAPTER 8: Concatenate Video Files in PythonCHAPTER 9: Combine a Static Image with Audio in PythonCHAPTER 10: Extract Audio from Video in PythonMethod 1: Using FFmpeg DirectlyMethod 2: Using MoviePyConclusionCHAPTER 6: Extract Frames from Video in Python Making two different methods to extract frames from videos with the timestamp with OpenCV or MoviePy libraries in Python. As you may already know, a video is made up of a series of images. These images are called frames and are played continuously one by one at a certain rate which will be recognized as motion by the human eye. In this tutorial, you will learn two methods of extracting frames from video files in Python. First, we'll go into how we can do that with the well-known OpenCV library. After that, we'll explore the other method of extracting frames using the MoviePy library. To get started, let's install the libraries: $ pip install python-opencv moviepy Method 1: Extracting Frames using OpenCV I'll create extract_frames_opencv.py file and import the necessary modules: from datetime import timedelta import cv2 import numpy as np import osSince not all videos have the same length and FPS, we will define a parameter to adjust how many frames we want to extract and save per second: # i.e if video of duration 30 seconds, saves 10 frame per second = 300 frames saved in total SAVING_FRAMES_PER_SECOND = 10 We will use this parameter on both methods. For instance, if it's set to 10 as for now, it will save only 10 frames per second of the video, even though the video FPS is say 24. If the video has 30 seconds of duration, then 300 frames will be saved in total. You can also set this parameter to say 0.5, which will save one frame per 2 seconds, and so on. Next, let's define two helper functions: def format_timedelta(td): """Utility function to format timedelta objects in a cool way (e.g 00:00:20.05) omitting microseconds and retaining milliseconds""" result = str(td) try: result, ms = result.split(".") except ValueError: return result + ".00".replace(":", "-") ms = int(ms) ms = round(ms / 1e4) return f"{result}.{ms:02}".replace(":", "-") def get_saving_frames_durations(cap, saving_fps): """A function that returns the list of durations where to save the frames""" s = [] # get the clip duration by dividing number of frames by the number of frames per second clip_duration = cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS)# use np.arange() to make floating-point steps for i in np.arange(0, clip_duration, 1 / saving_fps): s.append(i) return s The format_timedelta() function accepts a timedelta object and returns a nice string representation with milliseconds and omitting the microseconds. The get_saving_frames_durations() function accepts the VideoCapture object from OpenCV, and the saving parameter we discussed earlier and returns a list of duration spots on where we should save the frames. Now that we have these helper functions, let's define the main function and explain it: def main(video_file): filename, _ = os.path.splitext(video_file) filename += "-opencv" # make a folder by the name of the video file if not os.path.isdir(filename): os.mkdir(filename) # read the video file cap = cv2.VideoCapture(video_file) # get the FPS of the video fps = cap.get(cv2.CAP_PROP_FPS) # if the SAVING_FRAMES_PER_SECOND is above video FPS, then set it to FPS (as maximum) saving_frames_per_second = min(fps, SAVING_FRAMES_PER_SECOND) # get the list of duration spots to save saving_frames_durations = get_saving_frames_durations(cap, saving_frames_per_second) # start the loop count = 0while True: is_read, frame = cap.read() if not is_read: # break out of the loop if there are no frames to read break # get the duration by dividing the frame count by the FPS frame_duration = count / fps try: # get the earliest duration to save closest_duration = saving_frames_durations[0] except IndexError: # the list is empty, all duration frames were saved break if frame_duration >= closest_duration: # if closest duration is less than or equals the frame duration, # then save the frame frame_duration_formatted = format_timedelta(timedelta(seconds=frame_duration)) cv2.imwrite(os.path.join(filename, f"frame{frame_duration_formatted}.jpg"), frame) # drop the duration spot from the list, since this duration spot is already saved try: saving_frames_durations.pop(0) except IndexError: pass # increment the frame count count += 1 The above function looks complicated, but it's not, here's what we're doing:- First, we make the filename variable which is the folder name we're going to create and save our frames in, we append "-opencv" just to distinguish the methods, but you can delete that. - Then, we create the folder using the os.mkdir() function if not already created. - After that, we read the video file using cv2.VideoCapture , and retrieves the FPS using the cap.get() method and pass the code for FPS, which is cv2.CAP_PROP_FPS . - We set the saving frames per second to the minimum of the actual video FPS and our parameter. So we make sure that we cannot bypass a higher saving fps than the actual video fps. - After we get the saving durations, we enter the loop of reading the frames, and we only save when we're sure that the duration is in our saving_frames_durations list. We save the frame using cv2.imwrite() , and set the frame name to the actual duration. Defining the main code: if __name__ == "__main__": import sys video_file = sys.argv[1] main(video_file) Since we're passing the video file using command-line arguments, let's run it: $ python extract_frames_opencv.py zoo.mp4 After the execution of the above command, a new folder "zoo-opencv" is created and that's what is included in it:As you can see, the frames are saved along with the timestamp in the file name. Related: How to Extract Audio from Video in Python Method 2: Extracting Frames using MoviePy In this method, we're not going to use OpenCV, but with another library called MoviePy, I'm going to create a file called extract_frames_moviepy.py and import the necessary modules: from moviepy.editor import


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