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question:import os import subprocess import numpy as np import uuid # Import uuid to generate unique IDs from moviepy.editor import VideoFileClip from scipy.io import wavfile import random # Initialize the variables with default values include_subfolders = False starting_offset_seconds = 0 ending_offset_seconds = 0 segment_duration = 10 # Default value, e.g., 5 seconds num_moments = 19 # Default value, e.g., 20 moments sorting_preference = 4 # Default value, e.g., sorting by descending volume peak_position = '2' # Default value, e.g., peak at the middle of the segment selected_video_path = "" # Initially empty processed_videos = 0 min_duration_threshold = 3 # Minimum duration for a loudness peak in seconds temporary_audio_files = [] # Define video file extensions and the output folder video_extensions = ['.mp4', '.mkv', '.wmv', '.avi'] output_folder = 'Output' def update_max_segments_for_selected_video(): global selected_video_path, max_segments, segment_duration, starting_offset_seconds, ending_offset_seconds, num_moments if selected_video_path in video_durations: video_duration = video_durations[selected_video_path] max_segments = calculate_max_segments(video_duration, segment_duration, starting_offset_seconds, ending_offset_seconds) num_moments = min(num_moments, max_segments) # Ajustez num_moments si nécessaire else: max_segments = 0 def sorting_preference_to_string(pref): sorting_options = { 1: "Par ordre de lecture de la vidéo", 2: "Par ordre inverse de lecture de la vidéo", 3: "Par volume croissant", 4: "Par volume décroissant", 5: "Aléatoire" } return sorting_options.get(pref, "Non défini") def peak_position_to_string(pos): peak_options = { '1': "À 1/4 du temps de lecture de la vidéo", '2': "À 1/2 du temps de lecture de la vidéo", '3': "À 3/4 du temps de lecture de la vidéo" } return peak_options.get(pos, "Non défini") def choose_video(videos): # Separate root files from subdirectory files root_files = [video for video in videos if os.path.dirname(os.path.relpath(video)) == ''] subdirectory_files = [video for video in videos if os.path.dirname(os.path.relpath(video)) != ''] # Sort the files in subdirectories subdirectory_files_sorted = sorted(subdirectory_files, key=lambda x: (os.path.dirname(x).lower(), os.path.basename(x).lower())) # Combine lists: root files first, then sorted subdirectory files combined_videos = root_files + subdirectory_files_sorted print("Liste des vidéos disponibles :") for i, video in enumerate(combined_videos): # Get the relative path for printing rel_path = os.path.relpath(video) print(f"{i + 1}- {rel_path}") while True: choice = input("Veuillez choisir la vidéo à extraire (entrez le numéro) : ") try: choice_idx = int(choice) - 1 if 0 <= choice_idx < len(combined_videos): return combined_videos[choice_idx] else: print("Le numéro doit être valide. Veuillez réessayer.") except ValueError: print("Veuillez entrer un nombre valide.") def print_menu(selected_video_path): global max_segments global include_subfolders, starting_offset_seconds, ending_offset_seconds global segment_duration, num_moments, sorting_preference, peak_position video_name = os.path.basename(selected_video_path) if selected_video_path else "Aucune vidéo sélectionnée" print("nMenu des options :") print(f"1. Traiter les sous-dossiers ou non ({str(include_subfolders)})") print(f"2. Effectuer un retrait temporel (début: {str(starting_offset_seconds)}s, fin: {str(ending_offset_seconds)}s)") print(f"3. Changer la durée des segments ({str(segment_duration)}s | max segments extractibles: {str(max_segments)})") print(f"4. Changer le nombre de segments à extraire ({str(num_moments)})") print(f"5. Changer l'ordre de tri ({sorting_preference_to_string(sorting_preference)})") print(f"6. Changer l'emplacement du pic sonore ({peak_position_to_string(peak_position)})") print(f"7. Lancer l'extraction") print(f"8- ({video_name}) - Modifier") print(f"9. Quitter") def ask_for_number_of_moments(max_segments): while True: num_input = input(f"Veuillez entrer le nombre de moments forts à extraire (maximum {max_segments}): ") try: num = int(num_input) if num > 0 and num <= max_segments: return num else: print(f"Le nombre doit être supérieur à 0 et inférieur ou égal à {max_segments}. Veuillez réessayer.") except ValueError: print("Entrée non valide, veuillez réessayer avec un nombre entier.") def ask_yes_no_question(question): answer = None while answer not in ('1', '2'): print(question) print("1- Oui") print("2- Non") answer = input("Veuillez entrer le numéro de votre choix (1 ou 2) : ").strip() if answer not in ('1', '2'): print("Entrée non valide, veuillez réessayer.") return answer == '1' def ask_offset_type(): print("Souhaitez-vous un décalage temporel relatif ou absolu ?") print("1- Relatif (pourcentage)") print("2- Absolu (secondes)") while True: choice = input("Veuillez entrer le numéro de votre choix (1 ou 2) : ").strip() if choice in ('1', '2'): return choice else: print("Entrée non valide, veuillez réessayer.") def get_offset_value(video_duration, offset_type): if offset_type == '1': # Relative offset while True: percent = input("Entrez le pourcentage du temps vidéo à ignorer : ") try: percent_value = float(percent) return percent_value * video_duration / 100 except ValueError: print("Veuillez entrer un nombre valide.") else: # Absolute offset while True: seconds = input("Entrez le nombre de secondes à ignorer : ") try: return float(seconds) except ValueError: print("Veuillez entrer un nombre valide.") def ask_for_segment_duration(allowable_duration, video_duration, starting_offset_seconds, ending_offset_seconds): # Cette fonction a été modifiée pour répondre plus précisément aux contraintes de durée. while True: duration = input("Quelle est la durée (en secondes) de chaque segment vidéo à extraire (Entrez un nombre positif et inférieur ou égal à {allowable_duration}) ? ") try: segment_duration = float(duration) if 0 < segment_duration <= allowable_duration: # Calculez le nombre maximal de segments pour une vidéo available_duration = video_duration - (starting_offset_seconds + ending_offset_seconds) max_segments = int(available_duration // segment_duration) return segment_duration, max_segments else: print(f"La durée doit être un nombre positif et moins ou égale à {allowable_duration} secondes.") except ValueError: print("Veuillez entrer un nombre valide.") def ask_directory_preference(): print("Souhaitez-vous inclure les sous-dossiers dans la recherche des vidéos ?") print("1- Oui") print("2- Non") choice = input("Veuillez entrer le numéro de votre choix (1 ou 2) : ") return choice.strip() == '1' # Retourne True si l'utilisateur choisit '1' (Oui), False sinon def calculate_loudness(audio_data): if audio_data.ndim == 1: volume = audio_data.astype('float32') ** 2 else: volume = np.mean(audio_data.astype('float32') ** 2, axis=1) volume_dB = 10 * np.log10(volume + 1e-9) # +1e-9 to avoid log(0) and convert to dB return volume_dB def calculate_max_segments(video_duration, segment_duration, starting_offset, ending_offset): allowable_duration = video_duration - (starting_offset + ending_offset) if allowable_duration > 0: return int(allowable_duration // segment_duration) else: return 0 def find_loudest_moments(audio_filename, num_moments, segment_duration, video_duration, starting_offset, ending_offset, min_duration_threshold, min_gap_seconds): rate, audio_data = wavfile.read(audio_filename) # Convert stereo to mono if necessary if audio_data.ndim == 2: audio_data = np.mean(audio_data, axis=1) volume_dB = calculate_loudness(audio_data) start_index = int(starting_offset * rate) end_index = int((video_duration - ending_offset) * rate) # Calculate the minimum number of samples between peaks defined by min_gap_seconds min_gap_samples = int(min_gap_seconds * rate) # La fenêtre de durée minimale que le pic doit tenir min_peak_samples = int(rate * min_duration_threshold) moments = [] volumes = [] last_peak_end_index = 0 # Initialize the last peak end index while len(moments) < num_moments and start_index < end_index: # Make sure there is a minimum gap after the last peak and before searching for the next search_start_index = max(start_index, last_peak_end_index + min_gap_samples) if search_start_index >= end_index: break # No room left in the audio data to search for a new peak # Find the peak loudness within the current search range index = np.argmax(volume_dB[search_start_index:end_index]) + search_start_index peak_volume = volume_dB[index] # Calculate peak boundaries peak_start_index = max(index - min_peak_samples // 2, 0) peak_end_index = min(index + min_peak_samples // 2, len(volume_dB)) # Calculate continued duration for which the volume is above a threshold peak_section = volume_dB[peak_start_index:peak_end_index] above_threshold = peak_section > (np.mean(peak_section) - 10) # Example arbitrary threshold # Count the number of consecutive samples above the threshold peak_duration_samples = np.sum(above_threshold) # Convert the number of samples to duration in seconds peak_duration_seconds = peak_duration_samples / rate # If the peak duration meets the threshold criteria, consider it a moment if peak_duration_seconds >= min_duration_threshold: moment_time = index / rate moments.append(moment_time) volumes.append(peak_volume) print(f"Peak accepted: Time={moment_time}, Duration={peak_duration_seconds}s, Volume={peak_volume}dB") # Save the index position at the end of the accepted peak last_peak_end_index = peak_end_index else: print(f"Peak rejected: Time={index / rate}, Duration={peak_duration_seconds}s, Volume={peak_volume}dB") # Update the start_index for the next search to be at least min_gap_seconds after the current peak start_index = last_peak_end_index + min_gap_samples return moments, volumes def perform_extraction(): global starting_offset_seconds, ending_offset_seconds global segment_duration, num_moments, sorting_preference, peak_position, processed_videos, selected_video_path, max_segments # Vérifiez si une vidéo a été sélectionnée. if not selected_video_path: print("Aucune vidéo sélectionnée pour l'extraction.") return # Vérifiez si la vidéo sélectionnée est contenue dans video_durations. if selected_video_path not in video_durations: print(f"La vidéo sélectionnée '{selected_video_path}' n'est pas disponible.") return # Obtenez la durée de la vidéo sélectionnée. duration = video_durations[selected_video_path] available_duration = duration - (starting_offset_seconds + ending_offset_seconds) if available_duration <= segment_duration: print(f"La vidéo {selected_video_path} est trop courte après les décalages. Ignorer.") return if sorting_preference == 5: # Aléatoire all_possible_moments = np.arange(starting_offset_seconds, duration - ending_offset_seconds - segment_duration, segment_duration) + (segment_duration / 2) np.random.shuffle(all_possible_moments) selected_moments = all_possible_moments[:num_moments] volumes = [0] * len(selected_moments) # Les volumes sont justes pour la compatibilité avec extract_segments. else: audio_path = f'temp_audio_{uuid.uuid4().hex}.wav' try: with VideoFileClip(selected_video_path) as video_clip: video_clip.audio.write_audiofile(audio_path, codec='pcm_s16le', fps=44100, nbytes=2, buffersize=2000) min_duration_threshold = 0.5 # Set to your chosen threshold moments, volumes = find_loudest_moments(audio_path, num_moments, segment_duration, duration, starting_offset_seconds, ending_offset_seconds, min_duration_threshold) sorted_moments, sorted_volumes = sort_moments(moments, volumes, sorting_preference) selected_moments = sorted_moments volumes = sorted_volumes finally: if os.path.exists(audio_path): os.remove(audio_path) # Juste avant l'appel à extract_segments, mettez à jour num_moments pour être sûr qu'il ne dépasse pas max_segments num_moments = min(num_moments, max_segments) extract_segments(selected_video_path, selected_moments, segment_duration, duration, peak_position) processed_videos += 1 print(f"Extraction terminée pour la vidéo '{selected_video_path}'") def extract_segments(video_path, moments, segment_duration, video_duration, peak_position): if not os.path.exists(output_folder): os.makedirs(output_folder) base_name = os.path.splitext(os.path.basename(video_path))[0] half_segment_duration = segment_duration / 2 for i, moment in enumerate(moments): if peak_position == '1': # 1/4 start_time = max(moment - segment_duration * 0.25, 0) elif peak_position == '2': # 1/2 start_time = max(moment - segment_duration * 0.5, 0) elif peak_position == '3': # 3/4 start_time = max(moment - segment_duration * 0.75, 0) end_time = min(start_time + segment_duration, video_duration) output_filename = f"{base_name}_moment{i + 1}.mp4" output_path = os.path.join(output_folder, output_filename) command = [ "ffmpeg", "-y", # Overwrite output files without asking "-ss", str(start_time), # Start time "-i", video_path, # Input file "-t", str(min(segment_duration, video_duration - start_time)), # Duration or remaining video "-c:v", "libx264", # Specify video codec for output "-preset", "medium", # Specify the encoding preset (trade-off between encoding speed and quality) "-crf", "23", # Specify the Constant Rate Factor for quality (lower means better quality) "-c:a", "aac", # Specify audio codec for output "-strict", "-2", # Necessary for some versions of ffmpeg to use experimental aac encoder "-b:a", "192k", # Specify the audio bitrate output_path # Output path ] try: subprocess.run(command, check=True, stderr=subprocess.PIPE) print(f"Extracted and re-encoded {output_filename}") except subprocess.CalledProcessError as e: # Catch the exception for this process, print an error message, # but don't break from the for loop. err_msg = e.stderr.decode('utf-8') if e.stderr else 'Unknown error' print(f"Failed to extract segment from {video_path}: {err_msg}") def store_segment_info(video_path, moment, volume, order): base_name = os.path.splitext(os.path.basename(video_path))[0] output_filename = f"{base_name}_moment{order}.mp4" output_path = os.path.join(output_folder, output_filename) extracted_segments.append({ 'path': output_path, 'timestamp': moment, 'volume': volume }) def ask_sorting_preference(): print("Comment souhaitez-vous trier les vidéos extraites ?") print("1- Par ordre de lecture de la vidéo") print("2- Par ordre inverse de lecture de la vidéo") print("3- Par volume croissant") print("4- Par volume décroissant") print("5- Aléatoire") choice = int(input("Veuillez entrer le numéro de votre choix : ")) return choice def sort_moments(moments, volumes, choice): if choice == 1: # Par ordre de lecture de la vidéo zipped = sorted(zip(moments, volumes), key=lambda x: x[0]) elif choice == 2: # Par ordre inverse de lecture de la vidéo zipped = sorted(zip(moments, volumes), key=lambda x: x[0], reverse=True) elif choice == 3: # Par volume croissant zipped = sorted(zip(moments, volumes), key=lambda x: x[1]) elif choice == 4: # Par volume décroissant zipped = sorted(zip(moments, volumes), key=lambda x: x[1], reverse=True) elif choice == 5: # Pas de tri, sélection aléatoire zipped = list(zip(moments, volumes)) random.shuffle(zipped) else: zipped = zip(moments, volumes) # Unzip the list of tuples to two lists sorted_moments, sorted_volumes = zip(*zipped) if zipped else ([], []) return list(sorted_moments), list(sorted_volumes) def get_video_durations(include_subfolders): video_durations = {} for root, dirs, files in os.walk('.', topdown=True): # Si l'utilisateur ne souhaite pas inclure les sous-dossiers, nous modifions dirs sur place if not include_subfolders: dirs[:] = [] # Cela empêchera os.walk de descendre dans tous les sous-dossiers # La suite du traitement des fichiers reste inchangée for file in files: if file.lower().endswith(tuple(video_extensions)): video_path = os.path.join(root, file) try: # Essayez d'ouvrir et d'extraire les métadonnées du fichier vidéo. video_clip = VideoFileClip(video_path) video_duration = video_clip.duration video_durations[video_path] = video_duration except Exception as e: # Si une erreur survient, affichez un message et ignorez le fichier vidéo. print(f"Error processing video {video_path}: {e}") finally: # Assurez-vous de fermer le clip pour libérer les ressources. video_clip.close() return video_durations def confirm_segment_number_or_ask_again(video_duration, starting_offset_seconds, ending_offset_seconds): allowable_duration = video_duration - (starting_offset_seconds + ending_offset_seconds) while True: segment_duration, max_segments = ask_for_segment_duration(allowable_duration, video_duration, starting_offset_seconds, ending_offset_seconds) print(f"Avec cette durée, vous pouvez extraire jusqu'à {max_segments} segments.") confirmation = ask_yes_no_question("Voulez-vous continuer avec ce nombre de segments ?") if confirmation: return segment_duration, max_segments def ask_peak_position(): print("Où doit être situé le pic sonore dans la vidéo extraite ?") print("1- A 1/4 du temps de lecture de la vidéo") print("2- A 1/2 du temps de lecture de la vidéo") print("3- A 3/4 du temps de lecture de la vidéo") while True: choice = input("Veuillez entrer le numéro de votre choix (1, 2, ou 3) : ").strip() if choice in ('1', '2', '3'): return choice else: print("Entrée non valide, veuillez réessayer.") def main(): global include_subfolders, starting_offset_seconds, ending_offset_seconds global segment_duration, num_moments, sorting_preference, peak_position global processed_videos, video_durations, selected_video_path, max_segments # Load video durations at the start of the script video_durations = get_video_durations(include_subfolders) if not video_durations: print("Aucune vidéo trouvée pour l'analyse.") exit() video_list = sorted(list(video_durations.keys()), key=lambda x: os.path.basename(x)) selected_video_path = video_list[0] if video_list else "" # Select the first video by default, if available. # Calculate max_segments with the newly obtained video_durations video_duration = video_durations[selected_video_path] # Obtenez la durée de la vidéo sélectionnée max_segments = calculate_max_segments(video_durations[selected_video_path], segment_duration, starting_offset_seconds, ending_offset_seconds) while True: # Recalculate the max segments using the selected video before displaying the menu update_max_segments_for_selected_video() # Display the menu with the updated max segments print_menu(selected_video_path) choice = input("Veuillez entrer le numéro de votre choix : ").strip() # Handle user choices if choice == '1': # Update include_subfolders and recalculate video durations include_subfolders = ask_directory_preference() video_durations = get_video_durations(include_subfolders) # Optionally, reset selected_video_path if include_subfolders changed video_list = sorted(list(video_durations.keys()), key=lambda x: os.path.basename(x)) selected_video_path = video_list[0] if video_list else "" update_max_segments_for_selected_video() elif choice == '2': # Récupérez les nouvelles valeurs. offset_type = ask_offset_type() # Utilisez une valeur minimale mise à jour si nécessaire. min_duration = min(video_durations.values()) print("Configuration du retrait temporel pour le début de la vidéo:") starting_offset_seconds = get_offset_value(min_duration, offset_type) print("Configuration du retrait temporel pour la fin de la vidéo:") ending_offset_seconds = get_offset_value(min_duration, offset_type) elif choice == '3': # L'utilisateur définit la durée des segments et potentiellement le nombre de moments video_duration = min(video_durations.values()) # Min. duration among all videos allowable_duration = video_duration - (starting_offset_seconds + ending_offset_seconds) segment_duration, max_segments = ask_for_segment_duration( allowable_duration, video_duration, starting_offset_seconds, ending_offset_seconds ) if num_moments > max_segments: num_moments = max_segments # Ce bloc assure que num_moments n'excède jamais max_segments après un changement de durée des segments elif choice == '4': num_moments = ask_for_number_of_moments(max_segments) elif choice == '5': sorting_preference = ask_sorting_preference() elif choice == '6': peak_position = ask_peak_position() elif choice == '7': # Exécutez vos sous-fonctions pour effectuer l'extraction des segments. # Vous souhaiterez probablement encapsuler cela dans une autre fonction. perform_extraction() # Call perform_extraction instead of extract_segments elif choice == '8': video_list = sorted(list(video_durations.keys()), key=lambda x: os.path.basename(x)) prev_video_path = selected_video_path selected_video_path = choose_video(video_list) if selected_video_path != prev_video_path: print(f"Vidéo sélectionnée pour l'extraction : {os.path.basename(selected_video_path)}") update_max_segments_for_selected_video() # Mise à jour des segments max après le choix. elif choice == '9': print("Fin du programme.") break else: print("Choix non valide. Veuillez réessayer.") print(f"Le traitement de toutes les vidéos est terminé. {processed_videos} vidéos ont été traitées.") if __name__ == "__main__": main() MoviePy - Writing audio in temp_audio_746209c76d9e4f07a98acd80c029b221.wav MoviePy - Done. Traceback (most recent call last): File "E:Hydrusdbclient_filesf3bScript extraction moments forts - Menu - Selection vidéo - Neutralisation.py", line 517, in <module> main() File "E:Hydrusdbclient_filesf3bScript extraction moments forts - Menu - Selection vidéo - Neutralisation.py", line 497, in main perform_extraction() # Call perform_extraction instead of extract_segments File "E:Hydrusdbclient_filesf3bScript extraction moments forts - Menu - Selection vidéo - Neutralisation.py", line 284, in perform_extraction moments, volumes = find_loudest_moments(audio_path, num_moments, segment_duration, duration, starting_offset_seconds, ending_offset_seconds, min_duration_threshold) TypeError: find_loudest_moments() missing 1 required positional argument: 'min_gap_seconds'

answer:The error message indicates that the `find_loudest_moments()` function is missing one required argument, `min_gap_seconds`, when it is called within the `perform_extraction()` function. First, you'll need to define `min_gap_seconds` at the same place as the other settings or within the `perform_extraction()` function, depending on whether you want it to be a global setting or a local one within the function. Next, make sure to pass it into the `find_loudest_moments()` function call. Here's an example of how you can resolve the error by declaring the `min_gap_seconds` variable and then passing it into the `find_loudest_moments()` function: 1. Define `min_gap_seconds` at the top of your script with the other default value settings. ```python # Default value for the minimum gap between moments, e.g., 3 seconds min_gap_seconds = 3 ``` 2. Update the `perform_extraction()` function by making sure to pass `min_gap_seconds` to the `find_loudest_moments()` function: ```python def perform_extraction(): global starting_offset_seconds, ending_offset_seconds global segment_duration, num_moments, sorting_preference, peak_position, processed_videos, selected_video_path, max_segments, min_gap_seconds # Added min_gap_seconds to globals # ... existing code ... if sorting_preference == 5: # Aléatoire # ... existing code ... else: audio_path = f'temp_audio_{uuid.uuid4().hex}.wav' try: # ... existing code ... moments, volumes = find_loudest_moments(audio_path, num_moments, segment_duration, duration, starting_offset_seconds, ending_offset_seconds, min_duration_threshold, min_gap_seconds) # Pass min_gap_seconds # ... existing code ... finally: # ... existing code ... # ... existing code ... ``` Note that you will also need to update the `min_gap_seconds` wherever you expect its value to change or be determined by user input, similar to how you did with `min_duration_threshold` and `segment_duration`. Lastly, because the `min_gap_seconds` is a global setting used inside the `perform_extraction()` function, remember to declare it as global within the function scope if you're modifying its value within the function. If `min_gap_seconds` is supposed to be a fixed setting, and you've defined it at the top of the script, you won't need to declare it global within the function scope.

question:(In the city of Narshe...) Natsuki: "Hey, uh...miss Terra, there's something that's been on my mind for a while."

answer:Terra: "Sure, Natsuki. What's been bothering you?" Natsuki: "Well, it's about your past. You mentioned being an Esper, right? But, how does that work? How can someone be both human and an Esper?" Terra: "Ah, I understand why you would be confused. Being an Esper means I possess magical abilities and have a connection to the Espers, who are magical beings. But at the same time, I am still human." Natsuki: "That's...interesting. So, does being an Esper give you any advantages or different abilities?" Terra: "Yes, it does. As an Esper, I can use magic, harnessing the power of the elements. It allows me to cast spells, summon creatures, and manipulate the environment around me. It's a unique ability that not many others possess." Natsuki: "Wow, that's incredible! So, are there any downsides or challenges that come with being an Esper?" Terra: "Yes, there are. Being an Esper can be quite overwhelming at times. The power that flows through me can be difficult to control, and if I lose control, it can be disastrous. Additionally, the connection with the Espers can be both a blessing and a curse. While it grants me their power, it also makes me vulnerable to their emotions and experiences." Natsuki: "That sounds tough. But it also sounds like a fascinating and powerful gift. Have you come to peace with being an Esper? Do you see it as a part of who you are?" Terra: "It took me a while to come to terms with my Esper heritage, but now I see it as an integral part of who I am. It has helped shape my identity and given me the strength to fight for what I believe in. Embracing my gift has allowed me to embrace my true self." Natsuki: "That's really inspiring, Terra. It must have been quite a journey for you. Thank you for sharing your story with me." Terra: "You're welcome, Natsuki. If you have any more questions or if there's anything else you're curious about, don't hesitate to ask. I'm here to help."

question:(In the city of Narshe...) Natsuki: "Hey, uh...miss Terra, there's something that's been on my mind for a while."

answer:Terra: (Turning to face Natsuki) "Sure, Natsuki. What's been on your mind?"

question:Write me a simple GLSL shader that affects only a solid unbroken blob of red color

answer:Certainly! Here's a simple GLSL shader that will create a solid unbroken blob of red color: ```glsl #version 330 core uniform vec2 resolution; out vec4 fragColor; void main() { vec2 center = resolution / 2.0; float radius = min(resolution.x, resolution.y) / 4.0; float distance = length(gl_FragCoord.xy - center); if (distance <= radius) { fragColor = vec4(1.0, 0.0, 0.0, 1.0); } else { discard; } } ``` In this shader, we define a uniform `vec2 resolution` to access the resolution of the screen. We calculate the center of the screen as `center`, and set the radius of the blob as one-fourth of the minimum of the screen's width and height. Inside the `main()` function, we calculate the distance of the current fragment's coordinate from the center. If the distance is less than or equal to the radius, we set the fragment's color to red (`vec4(1.0, 0.0, 0.0, 1.0)`). Otherwise, we discard the fragment. Make sure to set up a basic rendering pipeline in your code to apply this shader to a renderable object.

Released under the AutoML License.

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