# core/visual_engine.py from PIL import Image, ImageDraw, ImageFont, ImageOps # --- MONKEY PATCH FOR Image.ANTIALIAS --- try: if hasattr(Image, 'Resampling') and hasattr(Image.Resampling, 'LANCZOS'): # Pillow 9+ if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.Resampling.LANCZOS elif hasattr(Image, 'LANCZOS'): # Pillow 8 if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.LANCZOS elif not hasattr(Image, 'ANTIALIAS'): print("WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. Video effects might fail.") except Exception as e_mp: print(f"WARNING: ANTIALIAS monkey-patch error: {e_mp}") # --- END MONKEY PATCH --- from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip, CompositeVideoClip, AudioFileClip) import moviepy.video.fx.all as vfx import numpy as np import os import openai import requests import io import time import random import logging logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) # --- ElevenLabs Client Import --- ELEVENLABS_CLIENT_IMPORTED = False ElevenLabsAPIClient = None Voice = None VoiceSettings = None try: from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings ElevenLabsAPIClient = ImportedElevenLabsClient Voice = ImportedVoice VoiceSettings = ImportedVoiceSettings ELEVENLABS_CLIENT_IMPORTED = True logger.info("ElevenLabs client components imported.") except Exception as e_eleven: logger.warning(f"ElevenLabs client import failed: {e_eleven}. Audio generation disabled.") # --- RunwayML Client Import (Placeholder) --- RUNWAYML_SDK_IMPORTED = False RunwayMLClient = None # Placeholder for the actual RunwayML client class try: # This is a hypothetical import. Replace with actual RunwayML SDK import if available. # Example: from runwayml import RunwayClient as ImportedRunwayMLClient # RunwayMLClient = ImportedRunwayMLClient # RUNWAYML_SDK_IMPORTED = True # logger.info("RunwayML SDK (placeholder) imported.") logger.info("RunwayML SDK import is a placeholder. Actual SDK needed for Runway features.") except ImportError: logger.warning("RunwayML SDK (placeholder) not found. RunwayML video generation will be disabled.") except Exception as e_runway_sdk: logger.warning(f"Error importing RunwayML SDK (placeholder): {e_runway_sdk}. RunwayML features disabled.") class VisualEngine: def __init__(self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"): self.output_dir = output_dir os.makedirs(self.output_dir, exist_ok=True) self.font_filename = "arial.ttf" font_paths_to_try = [ self.font_filename, f"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf", f"/System/Library/Fonts/Supplemental/Arial.ttf", f"C:/Windows/Fonts/arial.ttf", f"/usr/local/share/fonts/truetype/mycustomfonts/{self.font_filename}" ] self.font_path_pil = next((p for p in font_paths_to_try if os.path.exists(p)), None) self.font_size_pil = 20 self.video_overlay_font_size = 30 self.video_overlay_font_color = 'white' self.video_overlay_font = 'Liberation-Sans-Bold' # For MoviePy TextClip try: self.font = ImageFont.truetype(self.font_path_pil, self.font_size_pil) if self.font_path_pil else ImageFont.load_default() if self.font_path_pil: logger.info(f"Pillow font loaded: {self.font_path_pil}.") else: logger.warning("Using default Pillow font."); self.font_size_pil = 10 except IOError: logger.warning("Pillow font error. Using default."); self.font = ImageFont.load_default(); self.font_size_pil = 10 self.openai_api_key = None; self.USE_AI_IMAGE_GENERATION = False self.dalle_model = "dall-e-3"; self.image_size_dalle3 = "1792x1024" self.video_frame_size = (1280, 720) self.elevenlabs_api_key = None; self.USE_ELEVENLABS = False self.elevenlabs_client = None self.elevenlabs_voice_id = default_elevenlabs_voice_id if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED: self.elevenlabs_voice_settings = VoiceSettings(stability=0.60, similarity_boost=0.80, style=0.15, use_speaker_boost=True) else: self.elevenlabs_voice_settings = None self.pexels_api_key = None; self.USE_PEXELS = False # <<< RUNWAYML START >>> self.runway_api_key = None; self.USE_RUNWAYML = False self.runway_client = None # Placeholder for the actual RunwayML client instance # <<< RUNWAYML END >>> logger.info("VisualEngine initialized.") def set_openai_api_key(self,k): self.openai_api_key=k; self.USE_AI_IMAGE_GENERATION=bool(k) logger.info(f"DALL-E ({self.dalle_model}) {'Ready.' if k else 'Disabled.'}") def set_elevenlabs_api_key(self,api_key, voice_id_from_secret=None): self.elevenlabs_api_key=api_key if voice_id_from_secret: self.elevenlabs_voice_id = voice_id_from_secret if api_key and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient: try: self.elevenlabs_client = ElevenLabsAPIClient(api_key=api_key) self.USE_ELEVENLABS=bool(self.elevenlabs_client) logger.info(f"ElevenLabs Client {'Ready' if self.USE_ELEVENLABS else 'Failed Init'} (Voice ID: {self.elevenlabs_voice_id}).") except Exception as e: logger.error(f"ElevenLabs client init error: {e}. Disabled.", exc_info=True); self.USE_ELEVENLABS=False else: self.USE_ELEVENLABS=False; logger.info("ElevenLabs Disabled (no key or SDK).") def set_pexels_api_key(self,k): self.pexels_api_key=k; self.USE_PEXELS=bool(k) logger.info(f"Pexels Search {'Ready.' if k else 'Disabled.'}") # <<< RUNWAYML START >>> def set_runway_api_key(self, k): self.runway_api_key = k if k and RUNWAYML_SDK_IMPORTED and RunwayMLClient: # Assuming RunwayMLClient is the SDK's client class try: # self.runway_client = RunwayMLClient(api_key=k) # Actual initialization self.USE_RUNWAYML = True # Assume success for placeholder logger.info(f"RunwayML Client (Placeholder) {'Ready.' if self.USE_RUNWAYML else 'Failed Init.'}") except Exception as e: logger.error(f"RunwayML client (Placeholder) init error: {e}. Disabled.", exc_info=True) self.USE_RUNWAYML = False elif k and not (RUNWAYML_SDK_IMPORTED and RunwayMLClient): self.USE_RUNWAYML = True # Allow use with direct HTTP requests if SDK isn't used/available logger.info("RunwayML API Key set. SDK (Placeholder) not imported/used. Direct API calls would be needed.") else: self.USE_RUNWAYML = False logger.info("RunwayML Disabled (no API key or SDK issue).") # <<< RUNWAYML END >>> def _get_text_dimensions(self,text_content,font_obj): # ... (no changes from your previous version) if not text_content: return 0,self.font_size_pil try: if hasattr(font_obj,'getbbox'): # Pillow 8.0.0+ bbox=font_obj.getbbox(text_content);w=bbox[2]-bbox[0];h=bbox[3]-bbox[1] return w, h if h > 0 else self.font_size_pil elif hasattr(font_obj,'getsize'): # Older Pillow w,h=font_obj.getsize(text_content) return w, h if h > 0 else self.font_size_pil else: # Should not happen with standard ImageFont objects return int(len(text_content)*self.font_size_pil*0.6),int(self.font_size_pil*1.2 if self.font_size_pil*1.2>0 else self.font_size_pil) except Exception as e: logger.warning(f"Error in _get_text_dimensions for '{text_content[:20]}...': {e}") return int(len(text_content)*self.font_size_pil*0.6),int(self.font_size_pil*1.2) # Fallback def _create_placeholder_image_content(self,text_description,filename,size=None): # ... (no changes from your previous version, ensure filename includes extension e.g. .png) if size is None: size = self.video_frame_size img=Image.new('RGB',size,color=(20,20,40));d=ImageDraw.Draw(img);padding=25;max_w=size[0]-(2*padding);lines=[]; if not text_description: text_description="(Placeholder: No prompt text)" words=text_description.split();current_line="" for word in words: test_line=current_line+word+" "; if self._get_text_dimensions(test_line,self.font)[0] <= max_w: current_line=test_line else: if current_line: lines.append(current_line.strip()); current_line=word+" " if current_line.strip(): lines.append(current_line.strip()) # Add last line if not lines and text_description: lines.append(text_description[:int(max_w//(self.font_size_pil*0.6 +1))]+"..." if text_description else "(Text too long)") # Handle single very long word elif not lines: lines.append("(Placeholder Text Error)") _,single_line_h=self._get_text_dimensions("Ay",self.font); single_line_h = single_line_h if single_line_h > 0 else self.font_size_pil + 2 max_lines_to_display=min(len(lines),(size[1]-(2*padding))//(single_line_h+2)) if single_line_h > 0 else 1 if max_lines_to_display <=0: max_lines_to_display = 1 # Ensure at least one line can be attempted y_text_start = padding + (size[1]-(2*padding) - max_lines_to_display*(single_line_h+2))/2.0 y_text = y_text_start for i in range(max_lines_to_display): line_content=lines[i];line_w,_=self._get_text_dimensions(line_content,self.font);x_text=(size[0]-line_w)/2.0 d.text((x_text,y_text),line_content,font=self.font,fill=(200,200,180));y_text+=single_line_h+2 if i==6 and max_lines_to_display > 7: d.text((x_text,y_text),"...",font=self.font,fill=(200,200,180));break filepath=os.path.join(self.output_dir,filename); try:img.save(filepath);return filepath except Exception as e:logger.error(f"Saving placeholder image {filepath}: {e}", exc_info=True);return None def _search_pexels_image(self, query, output_filename_base): # ... (no changes from your previous version, ensure output_filename_base has .png for consistency, it will be replaced) if not self.USE_PEXELS or not self.pexels_api_key: return None headers = {"Authorization": self.pexels_api_key}; params = {"query": query, "per_page": 1, "orientation": "landscape", "size": "large"} # Use a more unique filename for Pexels images to avoid clashes if query is similar pexels_filename = output_filename_base.replace(".png", f"_pexels_{random.randint(1000,9999)}.jpg").replace(".mp4", f"_pexels_{random.randint(1000,9999)}.jpg") filepath = os.path.join(self.output_dir, pexels_filename) try: logger.info(f"Searching Pexels for: '{query}'"); effective_query = " ".join(query.split()[:5]); params["query"] = effective_query response = requests.get("https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20) response.raise_for_status(); data = response.json() if data.get("photos") and len(data["photos"]) > 0: photo_url = data["photos"][0]["src"]["large2x"] # Using large2x for better quality image_response = requests.get(photo_url, timeout=60); image_response.raise_for_status() img_data = Image.open(io.BytesIO(image_response.content)) if img_data.mode != 'RGB': img_data = img_data.convert('RGB') img_data.save(filepath); logger.info(f"Pexels image saved: {filepath}"); return filepath else: logger.info(f"No photos found on Pexels for query: '{effective_query}'") except Exception as e: logger.error(f"Pexels search/download for query '{query}': {e}", exc_info=True) return None # <<< RUNWAYML START >>> def _generate_video_clip_with_runwayml(self, prompt_text, scene_identifier_filename_base, target_duration_seconds=4, input_image_path=None): """ Placeholder for generating a video clip using RunwayML. This needs to be implemented with the actual RunwayML SDK or API. """ if not self.USE_RUNWAYML or not self.runway_api_key: logger.warning("RunwayML not enabled or API key missing. Cannot generate video clip.") return None output_video_filename = scene_identifier_filename_base.replace(".png", ".mp4") # Ensure .mp4 extension output_video_filepath = os.path.join(self.output_dir, output_video_filename) logger.info(f"Attempting RunwayML video generation for: {prompt_text[:100]}... (Target duration: {target_duration_seconds}s)") logger.info(f"RunwayML Output (Placeholder): {output_video_filepath}") # --- START ACTUAL RUNWAYML API INTERACTION (HYPOTHETICAL) --- # This section is highly dependent on RunwayML's specific API/SDK. # Example using a hypothetical SDK: # try: # if not self.runway_client: # # self.runway_client = RunwayMLClient(api_key=self.runway_api_key) # Or however it's initialized # logger.warning("RunwayML client not initialized (Placeholder).") # # For placeholder, simulate creating a dummy video file # return self._create_placeholder_video_content(prompt_text, output_video_filename, duration=target_duration_seconds) # generation_params = { # "text_prompt": prompt_text, # "duration_seconds": target_duration_seconds, # "width": self.video_frame_size[0], # Or Runway's supported sizes # "height": self.video_frame_size[1], # # Add other params like seed, motion scale, etc. # } # if input_image_path and os.path.exists(input_image_path): # generation_params["input_image_path"] = input_image_path # For image-to-video # logger.info(f"Using input image for RunwayML: {input_image_path}") # task_id = self.runway_client.submit_video_generation_task(**generation_params) # Hypothetical # logger.info(f"RunwayML task submitted: {task_id}. Polling for completion...") # while True: # status = self.runway_client.get_task_status(task_id) # Hypothetical # if status == "completed": # video_url = self.runway_client.get_video_url(task_id) # Hypothetical # video_response = requests.get(video_url, stream=True, timeout=300) # video_response.raise_for_status() # with open(output_video_filepath, 'wb') as f: # for chunk in video_response.iter_content(chunk_size=8192): # f.write(chunk) # logger.info(f"RunwayML video downloaded and saved: {output_video_filepath}") # return output_video_filepath # elif status in ["failed", "error"]: # logger.error(f"RunwayML task {task_id} failed.") # return None # time.sleep(10) # Poll interval # except Exception as e: # logger.error(f"Error during RunwayML video generation: {e}", exc_info=True) # return None # --- END ACTUAL RUNWAYML API INTERACTION (HYPOTHETICAL) --- # For now, as a placeholder, create a dummy MP4 file with MoviePy # This allows the rest of the pipeline to be tested. # **REPLACE THIS WITH ACTUAL RUNWAYML CALLS** logger.warning("Using PLACEHOLDER video generation for RunwayML.") return self._create_placeholder_video_content(f"[RunwayML Placeholder] {prompt_text}", output_video_filename, duration=target_duration_seconds) def _create_placeholder_video_content(self, text_description, filename, duration=4, size=None): """Creates a short video clip with text as a placeholder.""" if size is None: size = self.video_frame_size filepath = os.path.join(self.output_dir, filename) # Create a simple text clip txt_clip = TextClip(text_description, fontsize=50, color='white', font=self.video_overlay_font, bg_color='black', size=size, method='caption').set_duration(duration) try: txt_clip.write_videofile(filepath, fps=24, codec='libx264', preset='ultrafast', logger=None) logger.info(f"Placeholder video saved: {filepath}") return filepath except Exception as e: logger.error(f"Failed to create placeholder video {filepath}: {e}", exc_info=True) return None finally: if hasattr(txt_clip, 'close'): txt_clip.close() # <<< RUNWAYML END >>> def generate_scene_asset(self, image_prompt_text, scene_data, scene_identifier_filename_base, generate_as_video_clip=False, runway_target_duration=4, input_image_for_runway=None): """ Generates either an image or a video clip for a scene. Returns a dictionary: {'path': asset_path, 'type': 'image'/'video', 'error': bool} """ # Ensure scene_identifier_filename_base does not have an extension yet, or handle it base_name, _ = os.path.splitext(scene_identifier_filename_base) if generate_as_video_clip and self.USE_RUNWAYML: logger.info(f"Attempting RunwayML video clip generation for {base_name}") video_path = self._generate_video_clip_with_runwayml( image_prompt_text, # Use DALL-E prompt also for Runway text-to-video base_name, # Pass base name, function will add .mp4 target_duration_seconds=runway_target_duration, input_image_path=input_image_for_runway ) if video_path and os.path.exists(video_path): return {'path': video_path, 'type': 'video', 'error': False, 'prompt_used': image_prompt_text} else: logger.warning(f"RunwayML video clip generation failed for {base_name}. Falling back to image.") # Fall through to image generation # Image Generation (DALL-E, Pexels, Placeholder) # Ensure image filename has .png image_filename_with_ext = base_name + ".png" filepath = os.path.join(self.output_dir, image_filename_with_ext) if self.USE_AI_IMAGE_GENERATION and self.openai_api_key: max_retries = 2 for attempt in range(max_retries): try: # ... (DALL-E generation logic - no changes from your previous version) ... logger.info(f"Attempt {attempt+1}: DALL-E ({self.dalle_model}) for: {image_prompt_text[:100]}...") client = openai.OpenAI(api_key=self.openai_api_key, timeout=90.0) response = client.images.generate(model=self.dalle_model, prompt=image_prompt_text, n=1, size=self.image_size_dalle3, quality="hd", response_format="url", style="vivid") image_url = response.data[0].url; revised_prompt = getattr(response.data[0], 'revised_prompt', None) if revised_prompt: logger.info(f"DALL-E 3 revised_prompt: {revised_prompt[:100]}...") image_response = requests.get(image_url, timeout=120); image_response.raise_for_status() img_data = Image.open(io.BytesIO(image_response.content)); if img_data.mode != 'RGB': img_data = img_data.convert('RGB') img_data.save(filepath); logger.info(f"AI Image (DALL-E) saved: {filepath}"); return {'path': filepath, 'type': 'image', 'error': False, 'prompt_used': image_prompt_text, 'revised_prompt': revised_prompt} except openai.RateLimitError as e: logger.warning(f"OpenAI Rate Limit: {e}. Retrying after {5*(attempt+1)}s..."); time.sleep(5 * (attempt + 1)) if attempt == max_retries - 1: logger.error("Max retries for RateLimitError."); break except openai.APIError as e: logger.error(f"OpenAI API Error: {e}"); break except requests.exceptions.RequestException as e: logger.error(f"Requests Error (DALL-E download): {e}"); break except Exception as e: logger.error(f"Generic error (DALL-E gen): {e}", exc_info=True); break logger.warning("DALL-E generation failed. Trying Pexels fallback...") # Pexels or Placeholder if DALL-E failed or disabled if self.USE_PEXELS: pexels_query_text = scene_data.get('pexels_search_query_감독', f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}") pexels_path = self._search_pexels_image(pexels_query_text, image_filename_with_ext) # Pass filename with extension if pexels_path: return {'path': pexels_path, 'type': 'image', 'error': False, 'prompt_used': f"Pexels: {pexels_query_text}"} logger.warning("Pexels also failed/disabled. Using placeholder image.") placeholder_path = self._create_placeholder_image_content( f"[AI/Pexels Failed] {image_prompt_text[:100]}...", image_filename_with_ext ) if placeholder_path: return {'path': placeholder_path, 'type': 'image', 'error': False, 'prompt_used': image_prompt_text} else: return {'path': None, 'type': 'none', 'error': True, 'prompt_used': image_prompt_text} def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"): # ... (no changes from your previous version) ... if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate: logger.info("ElevenLabs conditions not met (API key, client init, or text). Skipping audio.") return None audio_filepath = os.path.join(self.output_dir, output_filename) try: logger.info(f"Generating ElevenLabs audio (Voice ID: {self.elevenlabs_voice_id}) for: {text_to_narrate[:70]}...") audio_stream_method = None if hasattr(self.elevenlabs_client, 'text_to_speech') and hasattr(self.elevenlabs_client.text_to_speech, 'stream'): audio_stream_method = self.elevenlabs_client.text_to_speech.stream logger.info("Using elevenlabs_client.text_to_speech.stream()") elif hasattr(self.elevenlabs_client, 'generate_stream') : # Older SDK might have this audio_stream_method = self.elevenlabs_client.generate_stream logger.info("Using elevenlabs_client.generate_stream()") elif hasattr(self.elevenlabs_client, 'generate'): # Fallback to non-streaming logger.info("Using elevenlabs_client.generate() (non-streaming).") # This one doesn't return a stream, it returns bytes directly voice_param = Voice(voice_id=str(self.elevenlabs_voice_id), settings=self.elevenlabs_voice_settings) if Voice and self.elevenlabs_voice_settings else str(self.elevenlabs_voice_id) audio_bytes = self.elevenlabs_client.generate( text=text_to_narrate, voice=voice_param, model="eleven_multilingual_v2" # or other suitable model ) with open(audio_filepath, "wb") as f: f.write(audio_bytes) logger.info(f"ElevenLabs audio (non-streamed) saved: {audio_filepath}") return audio_filepath else: logger.error("No recognized audio generation method found on ElevenLabs client.") return None # If we have a streaming method if audio_stream_method: voice_param_for_stream = {"voice_id": str(self.elevenlabs_voice_id)} # For Pydantic v1 style for elevenlabs sdk <1.0 # if self.elevenlabs_voice_settings and hasattr(self.elevenlabs_voice_settings, 'dict'): # voice_param_for_stream["voice_settings"] = self.elevenlabs_voice_settings.dict() # For Pydantic v2 style for elevenlabs skd >=1.0 if self.elevenlabs_voice_settings and hasattr(self.elevenlabs_voice_settings, 'model_dump'): voice_param_for_stream["voice_settings"] = self.elevenlabs_voice_settings.model_dump() elif self.elevenlabs_voice_settings : # If not a pydantic model, pass as is if supported voice_param_for_stream["voice_settings"] = self.elevenlabs_voice_settings audio_data_iterator = audio_stream_method( text=text_to_narrate, model_id="eleven_multilingual_v2", **voice_param_for_stream ) with open(audio_filepath, "wb") as f: for chunk in audio_data_iterator: if chunk: f.write(chunk) logger.info(f"ElevenLabs audio (streamed) saved: {audio_filepath}") return audio_filepath except AttributeError as ae: logger.error(f"AttributeError with ElevenLabs client: {ae}. SDK method/params might be different.", exc_info=True) except Exception as e: logger.error(f"Error generating ElevenLabs audio: {e}", exc_info=True) return None def assemble_animatic_from_assets(self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24): """ Assembles the final video from a list of assets (images or video clips). Each item in asset_data_list should be a dict like: {'path': 'path/to/asset', 'type': 'image'|'video', 'duration': desired_scene_duration_in_animatic, 'scene_num': num, 'key_action': 'text'} """ if not asset_data_list: logger.warning("No asset data provided for animatic assembly.") return None processed_moviepy_clips = [] narration_audio_clip = None final_composite_clip = None total_video_duration_from_assets = sum(item.get('duration', 4.5) for item in asset_data_list) logger.info(f"Assembling animatic from {len(asset_data_list)} assets. Target frame: {self.video_frame_size}. Approx total duration: {total_video_duration_from_assets:.2f}s.") for i, asset_info in enumerate(asset_data_list): asset_path = asset_info.get('path') asset_type = asset_info.get('type') # This 'duration' is the desired display duration of THIS scene in the final animatic target_scene_duration = asset_info.get('duration', 4.5) # Default if not specified scene_num = asset_info.get('scene_num', i + 1) key_action = asset_info.get('key_action', '') if not (asset_path and os.path.exists(asset_path)): logger.warning(f"Asset not found for Scene {scene_num}: {asset_path}. Skipping.") continue if target_scene_duration <= 0: logger.warning(f"Scene {scene_num} has invalid duration ({target_scene_duration}s). Skipping.") continue current_clip = None try: if asset_type == 'image': pil_img = Image.open(asset_path) if pil_img.mode != 'RGB': pil_img = pil_img.convert('RGB') img_copy = pil_img.copy() resample_filter = Image.Resampling.LANCZOS if hasattr(Image.Resampling, 'LANCZOS') else (Image.ANTIALIAS if hasattr(Image, 'ANTIALIAS') else Image.BILINEAR) img_copy.thumbnail(self.video_frame_size, resample_filter) canvas = Image.new('RGB', self.video_frame_size, (random.randint(0,10), random.randint(0,10), random.randint(0,10))) xo, yo = (self.video_frame_size[0] - img_copy.width) // 2, (self.video_frame_size[1] - img_copy.height) // 2 canvas.paste(img_copy, (xo, yo)) frame_np = np.array(canvas) current_clip_base = ImageClip(frame_np).set_duration(target_scene_duration) # Ken Burns for ImageClips try: end_scale = random.uniform(1.03, 1.08) current_clip = current_clip_base.fx(vfx.resize, lambda t: 1 + (end_scale - 1) * (t / target_scene_duration)).set_position('center') except Exception as e_fx: logger.error(f"Ken Burns error for image {asset_path}: {e_fx}. Using static image.") current_clip = current_clip_base elif asset_type == 'video': source_video_clip = VideoFileClip(asset_path, target_resolution=(self.video_frame_size[1], self.video_frame_size[0])) # Fit video into target_scene_duration: # If source is shorter, it will play once. If longer, it will be cut. # For more complex looping/speed adjustments, more logic is needed. if source_video_clip.duration > target_scene_duration: current_clip = source_video_clip.subclip(0, target_scene_duration) elif source_video_clip.duration < target_scene_duration: # Simple loop if significantly shorter, or just play once if close if target_scene_duration / source_video_clip.duration > 1.5 and source_video_clip.duration > 0.1 : # Loop if target is >150% of source current_clip = source_video_clip.loop(duration=target_scene_duration) else: # Play once, duration will be its own, MoviePy handles concatenation padding current_clip = source_video_clip.set_duration(source_video_clip.duration) # Explicitly set logger.info(f"Runway clip for S{scene_num} ({source_video_clip.duration:.2f}s) shorter than target ({target_scene_duration:.2f}s), will play once.") else: # Durations match current_clip = source_video_clip # Ensure the clip has the target duration for consistent concatenation if current_clip.duration != target_scene_duration: current_clip = current_clip.set_duration(target_scene_duration) # Resize if necessary (MoviePy does this on CompositeVideoClip too, but explicit can be good) if current_clip.size != list(self.video_frame_size): current_clip = current_clip.resize(self.video_frame_size) # Close the original source_video_clip if it's different from current_clip (e.g., after subclip) if current_clip != source_video_clip and hasattr(source_video_clip, 'close'): source_video_clip.close() else: logger.warning(f"Unknown asset type '{asset_type}' for Scene {scene_num}. Skipping.") continue # Add text overlay if current_clip and key_action: text_overlay_duration = min(target_scene_duration - 0.5, target_scene_duration * 0.8) if target_scene_duration > 0.5 else target_scene_duration text_overlay_start = (target_scene_duration - text_overlay_duration) / 2.0 if text_overlay_duration > 0: txt_clip = TextClip(f"Scene {scene_num}\n{key_action}", fontsize=self.video_overlay_font_size, color=self.video_overlay_font_color, font=self.video_overlay_font, bg_color='rgba(10,10,20,0.7)', method='caption', align='West', size=(self.video_frame_size[0] * 0.9, None), kerning=-1, stroke_color='black', stroke_width=1.5 ).set_duration(text_overlay_duration).set_start(text_overlay_start).set_position(('center', 0.92), relative=True) current_clip = CompositeVideoClip([current_clip, txt_clip], size=self.video_frame_size, use_bgclip=True, bg_color=(0,0,0)) if current_clip: processed_moviepy_clips.append(current_clip) except Exception as e: logger.error(f"Error processing asset for Scene {scene_num} ({asset_path}): {e}", exc_info=True) if current_clip and hasattr(current_clip, 'close'): current_clip.close() # Ensure closure on error if not processed_moviepy_clips: logger.warning("No MoviePy clips successfully processed. Aborting animatic assembly.") return None transition_duration = 0.75 try: if len(processed_moviepy_clips) > 1: final_composite_clip = concatenate_videoclips(processed_moviepy_clips, padding=-transition_duration, method="compose") elif processed_moviepy_clips: final_composite_clip = processed_moviepy_clips[0] else: # Should have been caught above, but defensive logger.error("No clips available for final concatenation.") return None if final_composite_clip.duration > transition_duration * 2: final_composite_clip = final_composite_clip.fx(vfx.fadein, transition_duration).fx(vfx.fadeout, transition_duration) elif final_composite_clip.duration > 0: final_composite_clip = final_composite_clip.fx(vfx.fadein, min(transition_duration, final_composite_clip.duration/2.0)) if overall_narration_path and os.path.exists(overall_narration_path): try: narration_audio_clip = AudioFileClip(overall_narration_path) if final_composite_clip.duration > 0 and narration_audio_clip.duration < final_composite_clip.duration: logger.info(f"Narration ({narration_audio_clip.duration:.2f}s) shorter than visuals ({final_composite_clip.duration:.2f}s). Trimming video.") final_composite_clip = final_composite_clip.subclip(0, narration_audio_clip.duration) elif final_composite_clip.duration <= 0: logger.warning("Video has no duration. Audio not added.") if narration_audio_clip and final_composite_clip.duration > 0: # Check again final_composite_clip = final_composite_clip.set_audio(narration_audio_clip) logger.info("Overall narration added.") except Exception as e: logger.error(f"Adding narration error: {e}", exc_info=True) if final_composite_clip and final_composite_clip.duration > 0: output_path = os.path.join(self.output_dir, output_filename) logger.info(f"Writing final animatic: {output_path} (Duration: {final_composite_clip.duration:.2f}s)") final_composite_clip.write_videofile( output_path, fps=fps, codec='libx264', preset='medium', audio_codec='aac', temp_audiofile=os.path.join(self.output_dir, f'temp-audio-{os.urandom(4).hex()}.m4a'), remove_temp=True, threads=os.cpu_count() or 2, logger='bar', bitrate="5000k" ) logger.info(f"Animatic created: {output_path}"); return output_path else: logger.error("Final animatic clip invalid or has no duration. Not writing file."); return None except Exception as e: logger.error(f"Animatic writing error: {e}", exc_info=True); return None finally: for clip in processed_moviepy_clips: if hasattr(clip, 'close'): clip.close() if narration_audio_clip and hasattr(narration_audio_clip, 'close'): narration_audio_clip.close() if final_composite_clip and hasattr(final_composite_clip, 'close'): final_composite_clip.close()