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question:As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image. Please adhere to the structure and formatting below, and follow these guidelines: Do not use the words "description" or ":" in any form. Do not place a comma between [ar] and [v]. Write each prompt in one line without using return. Structure: [1] = 變成中文 [2] = a detailed description of [1] with specific imagery details. [3] = a detailed description of the scene's environment. [4] = a detailed description of the compositions. [5] = a detailed description of the scene's mood, feelings, and atmosphere. [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1]. [7] = a detailed description of the scene's mood, feelings, and atmosphere. [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images. [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles. Formatting: Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]". Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization. Write your prompts in english. Do not describe unreal concepts as "real" or "photographic". Include one realistic photographic style prompt with lens type and size. Separate different prompts with two new lines. Example Prompts: /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2 /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2 /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5
answer:/imagine prompt: 美麗的秋天景觀，多采多姿的落葉在微風中飛舞，大片橘紅和金黃色的樹木，在明亮的陽光下閃耀著光輝，呈現出自然和豐富的場景，一條小溪蜿蜒流經森林，輕輕穿過石頭，用富有細節的繪畫風格描繪，氣氛輕鬆愉悅，讓人心情愉悅。 --ar 16:9 --v 5 /imagine prompt: 驚人的珊瑚礁景觀，在清澈湛藍的海水中，色彩繽紛的珊瑚和熱帶魚共同創造了一個令人驚嘆的場景，陽光穿過海水投射出幽靜的光影，營造出一種安靜而神秘的氛圍，以攝影的方式捕捉這個場景，使用底片相機、廣角鏡頭，呈現寬廣的視角和豐富的細節。 --ar 16:9 --v 5.2 /imagine prompt: 夜晚的城市街頭，高樓大廈燈火通明，霓虹燈在黑暗中閃爍著迷人的色彩，行人匆匆忙忙地走過，汽車的尾燈在街道上形成一條光帶，創造出一個充滿活力和節奏感的場景，以攝影的方式捕捉這個景象，使用數字相機、35毫米鏡頭，以富有細節和對比的形式呈現出城市的夜景。 --ar 9:16 --v 5 /imagine prompt: 深山中的神秘寺廟，建築古老而宏偉，被茂密的樹林所包圍，流經寺廟的小溪發出寧靜的聲音，遠處的山景和瀑布增添了壯麗的氣息，用插圖的形式呈現，充滿細節和神秘感，創造出一個寧靜而和諧的場景，讓人感受到寺廟的神聖與靈性。 --ar 16:9 --v 5.
question:import os import subprocess import numpy as np import uuid from moviepy.editor import VideoFileClip from scipy.io import wavfile # Define video file extensions and the output folder video_extensions = ['.mp4', '.mkv', '.wmv', '.avi'] output_folder = 'Output' def calculate_loudness(audio_data, rate): """Calculate loudness using ITU-R BS.1770-4 method.""" if audio_data.ndim == 2: audio_data = np.mean(audio_data, axis=1) # convert to mono by averaging channels # Normalization constants for the ITU-R BS.1770-4 method pre_filter = np.array([1.53512485958697, -2.69169618940638, 1.19839281085285]) pre_gain = 1.0 power = 10.0 # Pre-filter audio_data = np.convolve(audio_data, pre_filter) * pre_gain # Calculate power and then LUFS mean_square = np.mean(audio_data ** 2) mean_power = mean_square ** (0.5) loudness = (1 / rate) * np.sum(mean_power ** power) lufs = -0.691 + 10 * np.log10(loudness) return lufs def find_loudest_segments(audio_data, rate, num_segments, segment_duration, video_duration, starting_offset, ending_offset): """Find the segments with the highest loudness in the audio data.""" # Calculate the loudness of each segment in the audio file segment_loudness = [ calculate_loudness( audio_data[max(0, i * int(rate * segment_duration)) : min(len(audio_data), (i + 1) * int(rate * segment_duration))], rate) for i in range(0, int(video_duration / segment_duration)) ] # Combine segment start times with their corresponding loudness loudness_and_segments = [ (segment_loudness[i], i * segment_duration) for i in range(len(segment_loudness)) if i * segment_duration >= starting_offset and (i + 1) * segment_duration <= video_duration - ending_offset ] # Sort the loudness and segments based on loudness loudness_and_segments.sort(key=lambda x: x[0], reverse=True) # Return the top num_segments segments sorted by loudness return loudness_and_segments[:num_segments] def sort_segments(segments_with_loudness, preference): if preference == 1: return sorted(segments_with_loudness, key=lambda x: x[1]) elif preference == 2: return sorted(segments_with_loudness, key=lambda x: x[1], reverse=True) elif preference == 3: return sorted(segments_with_loudness, key=lambda x: x[0]) elif preference == 4: return sorted(segments_with_loudness, key=lambda x: x[0], reverse=True) def extract_segment(video_path, start_time, duration, output_path): """Use ffmpeg to extract a segment from a video with x264 encoding.""" command = [ "ffmpeg", "-y", # Overwrite output files without asking "-ss", str(start_time), # Start time "-i", video_path, # Input file "-t", str(duration), # Duration "-c:v", "libx264", # Use x264 video codec "-c:a", "aac", # AAC audio codec "-strict", "experimental", # Allow experimental codecs (if necessary for AAC) output_path # Output path ] try: subprocess.run(command, check=True, stderr=subprocess.PIPE) except subprocess.CalledProcessError as e: print(f"ffmpeg command failed: {e.stderr.decode()}") def process_video_file(video_path, starting_offset, ending_offset, num_segments, segment_duration, preference): """Process a single video file to find and extract the loudest segments.""" unique_id = str(uuid.uuid4()) audio_filename = f'temp_audio_{unique_id}.wav' try: video_clip = VideoFileClip(video_path) video_duration = video_clip.duration video_clip.audio.write_audiofile(audio_filename) rate, audio_data = wavfile.read(audio_filename) segments_with_loudness = find_loudest_segments(audio_data, rate, num_segments, segment_duration, video_duration, starting_offset, ending_offset) # Trier les segments en fonction de la préférence de l'utilisateur sorted_segments = sort_segments(segments_with_loudness, preference) # Anticiper si les noms de fichiers doivent correspondre à l'ordre initial if preference in [1, 2]: initial_order_index = {start_time: i for i, (loudness, start_time) in enumerate(segments_with_loudness)} if not os.path.exists(output_folder): os.makedirs(output_folder) for segment in sorted_segments: loudness, start_time = segment segment_index = initial_order_index[start_time] if preference in [1, 2] else sorted_segments.index(segment) output_filename = f"{os.path.splitext(os.path.basename(video_path))[0]}_segment{segment_index+1}.mp4" output_path = os.path.join(output_folder, output_filename) extract_segment(video_path, start_time, segment_duration, output_path) print(f"Extracted segment to {output_path}") finally: if os.path.exists(audio_filename): os.unlink(audio_filename) # Clean up the temporary file # Main if __name__ == "__main__": starting_offset = float(input("Start offset (sec)? ")) ending_offset = float(input("End offset (sec)? ")) num_segments = int(input("How many segments to extract? ")) segment_duration = float(input("Duration of each segment (sec)? ")) print("Choose your sorting preference for the extracted files:") print("1 - In order of appearance in the video") print("2 - In reverse order of appearance") print("3 - By increasing loudness") print("4 - By decreasing loudness") preference = int(input("Enter the number of your preference: ")) # Validate the user's preference if preference not in [1, 2, 3, 4]: print("Invalid preference. Please enter a valid number.") exit(1) for root, dirs, files in os.walk('.'): for file in files: if any(file.lower().endswith(ext) for ext in video_extensions): video_path = os.path.join(root, file) print(f"Processing video {video_path}…") process_video_file(video_path, starting_offset, ending_offset, num_segments, segment_duration, preference) print("All videos have been processed.")
answer:This script is a Python program designed to process video files by extracting the loudest segments based on the ITU-R BS.1770-4 method for measuring loudness. It uses the MoviePy library to handle video processing and the `wavfile` module from the `scipy` package to read audio data. Here's a brief breakdown of how the script works: 1. **Configuration**: The script defines the types of video files it will process and an output folder for storing the extracted segments. 2. **Loudness Calculation Function**: `calculate_loudness` calculates the perceived loudness of an audio segment. 3. **Finding Loudest Segments**: `find_loudest_segments` identifies the loudest segments within an audio track, given the number of segments, segment duration, video duration, and offset parameters. 4. **Sorting Segments**: The `sort_segments` function sorts the segments according to the user's preference: in the order of appearance, reverse order, increasing loudness, or decreasing loudness. 5. **Segment Extraction**: The `extract_segment` function uses `ffmpeg` to extract the specified segment from the video. 6. **Process Video File**: The `process_video_file` function combines all the above operations to process each video file. 7. **Main Program**: The script runs as a command-line application, prompting the user for inputs, walking through the current directory to find video files, and then processing them according to the provided parameters. Here are some important things to note and possible improvements: - **Dependency on FFMPEG**: This script requires that `ffmpeg` is installed and available in the system's PATH for the video segment extraction to work. - **Temporary Audio File**: For each video processed, a temporary audio file in WAV format is created and later deleted. Ensure you have enough disk space to handle this, especially for large videos. - **Error Handling**: There is basic error handling for the subprocess call to `ffmpeg`. This can be expanded based on the specific requirements, for example, to retry failed extractions or to skip over problematic files. - **Performance**: The script processes files sequentially. It could be modified to use multithreading or multiprocessing to handle multiple videos simultaneously, which would be more efficient. - **Audio Conversion**: The audio is being converted to mono for loudness calculation. Depending on the source material, you may want to modify this behavior. If you intend to use this script: 1. Ensure all the necessary Python packages (`moviepy`, `numpy`, `scipy`, and `uuid`) are installed. 2. Make sure `ffmpeg` is installed and accessible from the command-line. 3. Run the script from the command line and follow the prompts to input required information. 4. The script will process all video files in the current directory and its subdirectories that match the defined video extensions. Always test scripts like this on a small number of files before running them on a larger set to ensure they work as expected and do not cause any unwanted side effects.
question:Où se situe le pic d'amplitude sonore dans la vidéo extraite ?
answer:Le script fourni n'est pas conçu pour identifier ou extraire le pic d'amplitude sonore spécifique dans une vidéo. Au lieu de cela, il trouve les segments de vidéo d'une durée spécifiée où la 'loudness' moyenne, calculée selon le standard ITU-R BS.1770-4, est la plus élevée. En d'autres termes, il identifie les segments les plus "bruyants" en termes de loudness perçue, plutôt que les pics d'amplitude sonore. La loudness, telle que mesurée par ITU-R BS.1770-4, est une mesure de la puissance sonore moyenne sur une certaine période, et non l'amplitude instantanée (ou pic) du signal sonore. La loudness est une mesure qui reflète mieux la perception humaine de la 'force' des sons. Elle est exprimée en LUFS (Loudness Units relative to Full Scale). Par conséquent, après l'extraction des segments, la position exacte du pic d'amplitude sonore n'est pas reconnue ou utilisée par le script. Si vous deviez trouver les pics d'amplitude sonore, vous devriez analyser l'amplitude instantanée du signal audio à la recherche de valeurs maximales, ce qui est différent de la méthode de calcul de la loudness utilisée ici. Si vous souhaitez extraire des segments de vidéo en vous basant sur les pics d'amplitude plutôt que sur la loudness, vous devriez modifier la méthode de calcul pour analyser l'amplitude instantanée et détecter les points précis où le signal atteint des niveaux maximaux. La détection de pics d'amplitude pourrait être réalisée par exemple avec la bibliothèque `scipy` en utilisant `scipy.signal.find_peaks` pour identifier les pics du signal audio, ou par une simple inspection des valeurs instantanées du signal pour trouver les maximums locaux au-delà d'un certain seuil.
question:Je voudrais qu'une fois les segments extraits, une analyse du pic d'amplitude sonore maximum du segment soit déterminé. Ce pic d'amplittude sonore maximum doit ensuite être positionné par l'utilisateur dans la vidéo extraite tel qu'il l'a défini en début de script. Il doit avoir un choix gradable de 1 à 6 : 1 se situe à un sixième du temps de lecture de la vidéo, et 5 se situe à 5/6 du temps de lecture de la vidéo. Ecris toutes les modifications à faire sans rien omettre
answer:Pour effectuer les modifications que vous demandez, nous devrons apporter plusieurs changements au script existant. Les étapes générales seront les suivantes : 1. Modifier la fonction `extract_segment` pour inclure une analyse supplémentaire qui trouve le pic d'amplitude sonore maximum dans le segment audio extrait et positionne ce pic à un moment spécifié par l'utilisateur. Cela nécessitera une étape d'ajustement du point de départ de la coupe en fonction de la position souhaitée du pic dans le segment final. 2. Ajouter un prompt supplémentaire pour que l'utilisateur puisse spécifier la position souhaitée du pic d'amplitude sonore dans le segment vidéo extrait. 3. Adapter la logique de traitement des vidéos pour tenir compte de cette demande utilisateur et ajuster les segments en conséquence. Voici une description détaillée des modifications à apporter : 1. Ajouter un prompt pour la position du pic d'amplitude : ```python # Main if __name__ == "__main__": # ... (le reste des prompts) # Nouveau prompt pour la position du pic sonore print("Choose the position for the maximum sound amplitude peak:") print("1 - at 1/6 of the segment playback time") print("2 - at 2/6 of the segment playback time") # ... jusqu'à 6 print("6 - at 6/6 (end) of the segment playback time") peak_position_preference = int(input("Enter the number of your preference (1-6): ")) # Validate the user's preference if peak_position_preference not in range(1, 7): print("Invalid preference. Please enter a number between 1 and 6.") exit(1) # ... ``` 2. Modifier `extract_segment` pour trouver le pic d'amplitude : ```python import numpy as np def find_sound_amplitude_peak(audio_data): """Find the position of the highest sound amplitude peak.""" peak_position = np.argmax(np.abs(audio_data)) return peak_position def extract_adjusted_segment(video_path, start_time, duration, output_path, rate, audio_data, peak_position_preference, segment_duration): """Adjust the start time of the segment to position the sound peak where the user wants it.""" peak_position = find_sound_amplitude_peak(audio_data) desired_peak_position = int(segment_duration * peak_position_preference / 6 * rate) adjusted_start_time = max(start_time + (peak_position - desired_peak_position) / rate, 0) extract_segment(video_path, adjusted_start_time, duration, output_path) ``` 3. Intégrer l'ajustement dans `process_video_file` : ```python def process_video_file(video_path, starting_offset, ending_offset, num_segments, segment_duration, preference, peak_position_preference): # ... (le reste du code où vous récupérez le rate, audio_data, etc.) # Au lieu d'appeler extract_segment, utilisez la nouvelle fonction avec l'ajustement : for segment in sorted_segments: loudness, start_time = segment segment_index = initial_order_index[start_time] if preference in [1, 2] else sorted_segments.index(segment) output_filename = f"{os.path.splitext(os.path.basename(video_path))[0]}_segment{segment_index+1}.mp4" output_path = os.path.join(output_folder, output_filename) # Obtenez l'audio_data pour ce segment spécifique segment_audio = audio_data[int(start_time*rate):int((start_time+segment_duration)*rate)] extract_adjusted_segment(video_path, start_time, segment_duration, output_path, rate, segment_audio, peak_position_preference, segment_duration) print(f"Extracted and adjusted segment to {output_path}") ``` N'oubliez pas de passer `peak_position_preference` à la fonction `process_video_file` lors de son appel : ```python # Dans la boucle qui parcourt les fichiers vidéo : process_video_file(video_path, starting_offset, ending_offset, num_segments, segment_duration, preference, peak_position_preference) ``` Merci de noter que ces modifications impliquent un calcul supplémentaire et pourraient affecter les performances du script, en particulier pour les gros fichiers audio. Il y a également des cas limites à considérer, comme le cas où le pic sonore ne peut être positionné exactement où l'utilisateur le souhaite en raison des contraintes du début ou de la fin de la vidéo. Vous devrez également vous assurer que le taux (`rate`) de l'audio n'est pas modifié lors de l'extraction du segment du fichier audio pour le calcul du pic. Tester soigneusement les nouvelles fonctions et ajustements pour s'assurer qu'ils répondent aux attentes et prennent en compte ces cas limites est essentiel avant de les utiliser en production.