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slideshow-to-beat.py Brut
1#!/usr/bin/env python3
2import argparse
3import random
4import os
5
6import cv2
7import librosa
8import numpy as np
9from moviepy.editor import *
10from scipy.signal import butter, lfilter
11from scipy.signal import find_peaks
12
13# pip install opencv-python librosa numpy moviepy scipy
14
15
16def detect_beats(audio_file_path, highcut=200, order=5, peak_distance=10, peak_height=0.01):
17 # Load the audio file
18 y, sr = librosa.load(audio_file_path)
19
20 # Apply a high-pass filter to isolate the bass frequencies
21 b, a = butter(order, highcut / (0.5 * sr), btype='high')
22 y_filtered = lfilter(b, a, y)
23
24 # Calculate the RMS energy of the filtered signal
25 rms = librosa.feature.rms(y=y_filtered, frame_length=1024, hop_length=512)[0]
26
27 # Normalize the RMS energy
28 rms_normalized = rms / np.max(rms)
29
30 # Detect the peaks in the RMS energy signal
31 peaks, _ = find_peaks(rms_normalized, distance=peak_distance, height=peak_height)
32
33 # Convert the peak indices to times
34 beat_times = librosa.frames_to_time(peaks, sr=sr, hop_length=512)
35
36 return beat_times
37
38
39def create_slideshow(image_folder, audio_file, beat_times, max_duration=2, images=None):
40 if images is None:
41 images = [img for img in os.listdir(image_folder) if img.endswith(".jpg") or img.endswith(".png")]
42
43 clips = []
44 target_size = (1280, 720)
45
46 for i, beat_time in enumerate(beat_times[:-1]):
47 img_path = os.path.join(image_folder, images[i % len(images)])
48 img = cv2.imread(img_path)
49 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
50
51 # Resize the image while maintaining aspect ratio and fitting within the target size
52 height, width, _ = img.shape
53 target_width, target_height = target_size
54
55 scale_width = float(target_width) / float(width)
56 scale_height = float(target_height) / float(height)
57 scale_factor = min(scale_width, scale_height)
58
59 new_width = int(width * scale_factor)
60 new_height = int(height * scale_factor)
61
62 img_resized = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_AREA)
63
64 # Add padding
65 pad_top = max((target_height - new_height) // 2, 0)
66 pad_bottom = max(target_height - new_height - pad_top, 0)
67 pad_left = max((target_width - new_width) // 2, 0)
68 pad_right = max(target_width - new_width - pad_left, 0)
69
70 img_padded = cv2.copyMakeBorder(img_resized, pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT, value=[0, 0, 0])
71
72 duration = beat_times[i + 1] - beat_times[i]
73
74 # If the duration between two beats is greater than the max_duration, repeat the image
75 while duration > max_duration:
76 clip = ImageClip(img_padded, duration=max_duration)
77 clips.append(clip)
78 duration -= max_duration
79
80 clip = ImageClip(img_padded, duration=duration)
81 clips.append(clip)
82
83 slideshow = concatenate_videoclips(clips)
84 return slideshow
85
86
87def main():
88 parser = argparse.ArgumentParser(description="Create a slideshow that matches the bass beats or lyrics of a song.")
89 parser.add_argument("image_folder", help="Path to the folder containing the images for the slideshow.")
90 parser.add_argument("audio_file_path", help="Path to the input audio file.")
91 parser.add_argument("output_file_path", help="Path to the output video file.")
92 parser.add_argument("--highcut", type=int, default=200, help="Cutoff frequency for the high-pass filter (default: 200 Hz).")
93 parser.add_argument("--order", type=int, default=5, help="Order of the Butterworth filter (default: 5).")
94 parser.add_argument("--peak-distance", type=int, default=10, help="Minimum number of samples between peaks (default: 10).")
95 parser.add_argument("--peak-height", type=float, default=0.01, help="Minimum height of a peak in the RMS energy signal (default: 0.01).")
96 parser.add_argument("--more-help", action="store_true", help="Show more help.")
97 parser.add_argument("--randomize", "-r", action="store_true", help="Randomize the order of the images in the slideshow.")
98 parser.add_argument("--image_order_file", "-f", help="Path to a text file containing the ordered list of image filenames (not file paths!).")
99 args = parser.parse_args()
100
101 if args.more_help:
102 print("""highcut: The cutoff frequency for the high-pass filter applied to isolate the bass frequencies. The default value is 200 Hz, which means that the filter will keep frequencies below 200 Hz (bass frequencies) and attenuate higher frequencies. You can adjust this value to focus on different frequency ranges of the bass.
103
104order: The order of the Butterworth filter used for the high-pass filtering. A higher order results in a steeper roll-off, which means a more aggressive filtering. The default value is 5, which should work well for most cases. You can increase or decrease this value to change the sharpness of the filter.
105
106peak_distance: The minimum number of samples between peaks in the RMS energy signal. This parameter helps to avoid detecting multiple peaks that are too close to each other. The default value is 10, which means that two peaks must be at least 10 samples apart to be considered separate peaks. You can adjust this value to control the minimum distance between detected beats.
107
108peak_height: The minimum height of a peak in the normalized RMS energy signal. This parameter helps to filter out peaks that are too small and might not correspond to actual bass beats. The default value is 0.01, which means that a peak must have a height of at least 1% of the maximum RMS energy value to be considered a beat. You can adjust this value to control the minimum strength of detected beats.
109
110When fine-tuning these parameters, you might want to start by adjusting highcut and peak_height to focus on the desired bass frequency range and beat strength. Then, you can experiment with the order and peak_distance parameters to further refine the beat detection. Keep in mind that the optimal values for these parameters might vary depending on the specific characteristics of the audio file you are working with.""")
111 quit()
112
113 print('Processing beats...')
114 beat_times = detect_beats(args.audio_file_path, highcut=args.highcut, order=args.order, peak_distance=args.peak_distance, peak_height=args.peak_height)
115 audio_file = AudioFileClip(args.audio_file_path)
116
117 images = [img for img in os.listdir(args.image_folder) if img.endswith(".jpg") or img.endswith(".png")]
118 if args.image_order_file:
119 with open(args.image_order_file, 'r') as f:
120 ordered_images = [line.strip() for line in f.readlines()]
121 images = [img for img in ordered_images if img in images]
122 elif args.randomize:
123 random.shuffle(images)
124
125 if not images:
126 print("No valid images found. Please check the image folder or the image_order_file.")
127 return
128
129 print('Creating slideshow...')
130 slideshow = create_slideshow(args.image_folder, audio_file, beat_times, images=images)
131 final_video = slideshow.set_audio(audio_file)
132
133 print('Writing video...')
134 final_video.write_videofile(args.output_file_path, fps=24, codec='libx264', audio_codec='aac')
135
136
137if __name__ == "__main__":
138 main()
139