Update core/visual_engine.py
Browse files- core/visual_engine.py +970 -301
core/visual_engine.py
CHANGED
@@ -1,19 +1,7 @@
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# core/visual_engine.py
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from PIL import Image, ImageDraw, ImageFont, ImageOps
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if hasattr(Image, 'Resampling') and hasattr(Image.Resampling, 'LANCZOS'): # Pillow 9+
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if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.Resampling.LANCZOS
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elif hasattr(Image, 'LANCZOS'): # Pillow 8
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if not hasattr(Image, 'ANTIALIAS'): Image.ANTIALIAS = Image.LANCZOS
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elif not hasattr(Image, 'ANTIALIAS'):
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print("WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. Video effects might fail.")
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except Exception as e_mp: print(f"WARNING: ANTIALIAS monkey-patch error: {e_mp}")
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# --- END MONKEY PATCH ---
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from moviepy.editor import (ImageClip, VideoFileClip, concatenate_videoclips, TextClip,
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CompositeVideoClip, AudioFileClip)
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import moviepy.video.fx.all as vfx
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import numpy as np
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import os
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import openai
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import random
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import logging
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logger = logging.getLogger(__name__)
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# ---
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ELEVENLABS_CLIENT_IMPORTED = False
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try:
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from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
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from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
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ElevenLabsAPIClient = ImportedElevenLabsClient; Voice = ImportedVoice; VoiceSettings = ImportedVoiceSettings
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ELEVENLABS_CLIENT_IMPORTED = True; logger.info("ElevenLabs client components imported.")
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except Exception as e_eleven: logger.warning(f"ElevenLabs client import failed: {e_eleven}. Audio disabled.")
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try:
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class VisualEngine:
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self.output_dir = output_dir
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os.makedirs(self.output_dir, exist_ok=True)
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font_paths_to_try = [
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self.
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f"/usr/share/fonts/truetype/dejavu/
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f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf",
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f"/System/Library/Fonts/Supplemental/Arial.ttf",
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f"/
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]
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self.
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self.video_overlay_font_color = 'white'
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self.video_overlay_font = 'DejaVu-Sans-Bold'
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self.openai_api_key = None
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self.
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self.video_frame_size = (1280, 720)
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self.elevenlabs_voice_id = default_elevenlabs_voice_id
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if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED:
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logger.info("VisualEngine initialized.")
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def
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self.
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if api_key and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient:
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try:
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try:
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if hasattr(
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bbox =
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def _create_placeholder_image_content(self, text_description, filename, size=None):
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else:
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if
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current_line = ""
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else: current_line = word + " "
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if current_line.strip(): lines.append(current_line.strip())
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if not lines and text_description:
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avg_char_w = self._get_text_dimensions("
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filepath = os.path.join(self.output_dir, filename)
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try: img.save(filepath); return filepath
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except Exception as e: logger.error(f"Error saving placeholder image {filepath}: {e}", exc_info=True); return None
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def _search_pexels_image(self, q, ofnb):
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if not self.USE_PEXELS or not self.pexels_api_key: return None; h={"Authorization":self.pexels_api_key};p={"query":q,"per_page":1,"orientation":"landscape","size":"large2x"}
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pfn=ofnb.replace(".png",f"_pexels_{random.randint(1000,9999)}.jpg").replace(".mp4",f"_pexels_{random.randint(1000,9999)}.jpg");fp=os.path.join(self.output_dir,pfn)
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try: logger.info(f"Pexels search: '{q}'");eq=" ".join(q.split()[:5]);p["query"]=eq;r=requests.get("https://api.pexels.com/v1/search",headers=h,params=p,timeout=20)
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r.raise_for_status();d=r.json()
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if d.get("photos") and len(d["photos"])>0:pu=d["photos"][0]["src"]["large2x"];ir=requests.get(pu,timeout=60);ir.raise_for_status();id_img=Image.open(io.BytesIO(ir.content)) # Renamed id to id_img
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if id_img.mode!='RGB':id_img=id_img.convert('RGB');id_img.save(fp);logger.info(f"Pexels saved: {fp}");return fp
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except Exception as e:logger.error(f"Pexels error ('{q}'): {e}",exc_info=True);return None
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def _generate_video_clip_with_runwayml(self, pt, sifnb, tds=5, iip=None): # Default tds to 5s for Gen-4
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if not self.USE_RUNWAYML or not self.runway_api_key: logger.warning("RunwayML disabled."); return None
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if not iip or not os.path.exists(iip): logger.error(f"Runway Gen-4 needs input image. Path invalid: {iip}"); return None
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runway_dur = 10 if tds > 7 else 5 # Map to 5s or 10s
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ovfn = sifnb.replace(".png", f"_runway_gen4_d{runway_dur}s.mp4")
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ovfp = os.path.join(self.output_dir, ovfn)
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logger.info(f"Runway Gen-4 (Placeholder) img: {os.path.basename(iip)}, motion: '{pt[:100]}...', dur: {runway_dur}s")
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# --- ACTUAL RUNWAYML API CALL (NEEDS IMPLEMENTATION) ---
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logger.warning("Using PLACEHOLDER video for Runway Gen-4.")
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img_clip=None; txt_c=None; final_ph_clip=None # Initialize for finally block
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try:
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finally:
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if tc and hasattr(tc,
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base_name, _ = os.path.splitext(scene_identifier_filename_base)
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asset_info = {
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input_image_for_runway_path = None
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#
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if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
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if generate_as_video_clip:
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if
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asset_info = temp_image_asset_info; asset_info['error'] = True; asset_info['error_message'] = "RunwayML video gen failed; using base image."; asset_info['type'] = 'image'
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return asset_info
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elif not self.USE_RUNWAYML: # Video requested, but RunwayML disabled
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asset_info = temp_image_asset_info; asset_info['error_message'] = "RunwayML disabled; using base image."; asset_info['type'] = 'image'
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return asset_info
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else: # Video requested, but base image failed
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asset_info = temp_image_asset_info; asset_info['error_message'] = (asset_info.get('error_message',"") + " Base image failed, Runway video not attempted.").strip(); asset_info['type'] = 'image'
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return asset_info
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logger.info(f"Assembling from {len(asset_data_list)} assets. Frame: {self.video_frame_size}.")
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for i, asset_info in enumerate(asset_data_list):
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asset_path
|
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logger.info(f"S{scene_num}: Path='{asset_path}', Type='{asset_type}', Dur='{scene_dur}'s")
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if not (asset_path and os.path.exists(asset_path)):
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current_scene_mvpy_clip = None
|
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try:
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if asset_type ==
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pil_img = Image.open(asset_path)
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clip_fx = clip_base
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-
try:
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current_scene_mvpy_clip = clip_fx
|
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-
elif asset_type ==
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src_clip=None
|
314 |
try:
|
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src_clip=VideoFileClip(
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else:
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finally:
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if src_clip and src_clip is not current_scene_mvpy_clip and hasattr(src_clip,
|
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if current_scene_mvpy_clip and key_action:
|
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try:
|
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to_dur=
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finally:
|
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if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip,
|
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try:
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|
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td=0.75
|
345 |
try:
|
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-
logger.info(f"Concatenating {len(processed_clips)} clips.")
|
347 |
-
if len(processed_clips)>1:
|
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-
|
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-
|
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|
350 |
logger.info(f"Concatenated dur:{final_clip.duration:.2f}s")
|
351 |
-
if td>0 and final_clip.duration>0:
|
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if final_clip.duration>td*2:
|
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|
364 |
finally:
|
365 |
logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` finally block.")
|
366 |
-
|
367 |
-
for
|
368 |
-
if
|
369 |
-
try:
|
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|
1 |
# core/visual_engine.py
|
2 |
from PIL import Image, ImageDraw, ImageFont, ImageOps
|
3 |
+
import base64
|
4 |
+
import mimetypes
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
5 |
import numpy as np
|
6 |
import os
|
7 |
import openai
|
|
|
11 |
import random
|
12 |
import logging
|
13 |
|
14 |
+
# --- MoviePy Imports ---
|
15 |
+
from moviepy.editor import (
|
16 |
+
ImageClip,
|
17 |
+
VideoFileClip,
|
18 |
+
concatenate_videoclips,
|
19 |
+
TextClip,
|
20 |
+
CompositeVideoClip,
|
21 |
+
AudioFileClip,
|
22 |
+
)
|
23 |
+
import moviepy.video.fx.all as vfx
|
24 |
+
|
25 |
+
# --- MONKEY PATCH for Pillow/MoviePy compatibility ---
|
26 |
+
try:
|
27 |
+
if hasattr(Image, "Resampling") and hasattr(Image.Resampling, "LANCZOS"): # Pillow 9+
|
28 |
+
if not hasattr(Image, "ANTIALIAS"):
|
29 |
+
Image.ANTIALIAS = Image.Resampling.LANCZOS
|
30 |
+
elif hasattr(Image, "LANCZOS"): # Pillow 8
|
31 |
+
if not hasattr(Image, "ANTIALIAS"):
|
32 |
+
Image.ANTIALIAS = Image.LANCZOS
|
33 |
+
elif not hasattr(Image, "ANTIALIAS"): # Fallback if no common resampling attributes found
|
34 |
+
print(
|
35 |
+
"WARNING: Pillow version lacks common Resampling attributes or ANTIALIAS. MoviePy effects might fail or look different."
|
36 |
+
)
|
37 |
+
except Exception as e_monkey_patch:
|
38 |
+
print(
|
39 |
+
f"WARNING: An unexpected error occurred during Pillow ANTIALIAS monkey-patch: {e_monkey_patch}"
|
40 |
+
)
|
41 |
+
|
42 |
logger = logging.getLogger(__name__)
|
43 |
+
# Consider setting level in main app if not already configured:
|
44 |
+
# logger.setLevel(logging.DEBUG) # For very verbose output during debugging
|
45 |
|
46 |
+
# --- External Service Client Imports ---
|
47 |
+
ELEVENLABS_CLIENT_IMPORTED = False
|
48 |
+
ElevenLabsAPIClient = None
|
49 |
+
Voice = None
|
50 |
+
VoiceSettings = None
|
51 |
try:
|
52 |
from elevenlabs.client import ElevenLabs as ImportedElevenLabsClient
|
53 |
from elevenlabs import Voice as ImportedVoice, VoiceSettings as ImportedVoiceSettings
|
|
|
|
|
|
|
54 |
|
55 |
+
ElevenLabsAPIClient = ImportedElevenLabsClient
|
56 |
+
Voice = ImportedVoice
|
57 |
+
VoiceSettings = ImportedVoiceSettings
|
58 |
+
ELEVENLABS_CLIENT_IMPORTED = True
|
59 |
+
logger.info("ElevenLabs client components imported successfully.")
|
60 |
+
except ImportError:
|
61 |
+
logger.warning(
|
62 |
+
"ElevenLabs SDK not found (pip install elevenlabs). Audio generation will be disabled."
|
63 |
+
)
|
64 |
+
except Exception as e_eleven_import:
|
65 |
+
logger.warning(
|
66 |
+
f"Error importing ElevenLabs client components: {e_eleven_import}. Audio generation disabled."
|
67 |
+
)
|
68 |
+
|
69 |
+
RUNWAYML_SDK_IMPORTED = False
|
70 |
+
RunwayMLAPIClient = None # Using a more specific name for the client class
|
71 |
try:
|
72 |
+
from runwayml import RunwayML as ImportedRunwayMLClient # Actual SDK import
|
73 |
+
|
74 |
+
RunwayMLAPIClient = ImportedRunwayMLClient
|
75 |
+
RUNWAYML_SDK_IMPORTED = True
|
76 |
+
logger.info("RunwayML SDK imported successfully.")
|
77 |
+
except ImportError:
|
78 |
+
logger.warning(
|
79 |
+
"RunwayML SDK not found (pip install runwayml). RunwayML video generation will be disabled."
|
80 |
+
)
|
81 |
+
except Exception as e_runway_sdk_import:
|
82 |
+
logger.warning(
|
83 |
+
f"Error importing RunwayML SDK: {e_runway_sdk_import}. RunwayML features disabled."
|
84 |
+
)
|
85 |
|
86 |
|
87 |
class VisualEngine:
|
88 |
+
DEFAULT_FONT_SIZE_PIL = 10 # For default Pillow font
|
89 |
+
PREFERRED_FONT_SIZE_PIL = 20 # For custom font
|
90 |
+
VIDEO_OVERLAY_FONT_SIZE = 30
|
91 |
+
VIDEO_OVERLAY_FONT_COLOR = "white"
|
92 |
+
# Standard font names ImageMagick (used by TextClip) is likely to find in Linux containers
|
93 |
+
DEFAULT_MOVIEPY_FONT = "DejaVu-Sans-Bold"
|
94 |
+
PREFERRED_MOVIEPY_FONT = "Liberation-Sans-Bold" # Often available
|
95 |
+
|
96 |
+
def __init__(
|
97 |
+
self, output_dir="temp_cinegen_media", default_elevenlabs_voice_id="Rachel"
|
98 |
+
):
|
99 |
self.output_dir = output_dir
|
100 |
os.makedirs(self.output_dir, exist_ok=True)
|
101 |
+
|
102 |
+
self.font_filename_pil = "DejaVuSans-Bold.ttf" # A more standard Linux font
|
103 |
font_paths_to_try = [
|
104 |
+
self.font_filename_pil, # If in working dir or PATH
|
105 |
+
f"/usr/share/fonts/truetype/dejavu/{self.font_filename_pil}",
|
106 |
+
f"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf", # Alternative
|
107 |
+
f"/System/Library/Fonts/Supplemental/Arial.ttf", # macOS fallback
|
108 |
+
f"C:/Windows/Fonts/arial.ttf", # Windows fallback
|
109 |
+
f"/usr/local/share/fonts/truetype/mycustomfonts/arial.ttf", # User's previous custom path
|
110 |
]
|
111 |
+
self.font_path_pil_resolved = next(
|
112 |
+
(p for p in font_paths_to_try if os.path.exists(p)), None
|
113 |
+
)
|
|
|
|
|
114 |
|
115 |
+
self.font_pil = ImageFont.load_default() # Default
|
116 |
+
self.current_font_size_pil = self.DEFAULT_FONT_SIZE_PIL
|
117 |
+
|
118 |
+
if self.font_path_pil_resolved:
|
119 |
+
try:
|
120 |
+
self.font_pil = ImageFont.truetype(
|
121 |
+
self.font_path_pil_resolved, self.PREFERRED_FONT_SIZE_PIL
|
122 |
+
)
|
123 |
+
self.current_font_size_pil = self.PREFERRED_FONT_SIZE_PIL
|
124 |
+
logger.info(
|
125 |
+
f"Pillow font loaded: {self.font_path_pil_resolved} at size {self.current_font_size_pil}."
|
126 |
+
)
|
127 |
+
# Determine MoviePy font based on loaded PIL font
|
128 |
+
if "dejavu" in self.font_path_pil_resolved.lower():
|
129 |
+
self.video_overlay_font = "DejaVu-Sans-Bold"
|
130 |
+
elif "liberation" in self.font_path_pil_resolved.lower():
|
131 |
+
self.video_overlay_font = "Liberation-Sans-Bold"
|
132 |
+
else: # Fallback if custom font doesn't have an obvious ImageMagick name
|
133 |
+
self.video_overlay_font = self.DEFAULT_MOVIEPY_FONT
|
134 |
+
except IOError as e_font_load:
|
135 |
+
logger.error(
|
136 |
+
f"Pillow font loading IOError for '{self.font_path_pil_resolved}': {e_font_load}. Using default."
|
137 |
+
)
|
138 |
+
else:
|
139 |
+
logger.warning("Custom Pillow font not found. Using default.")
|
140 |
|
141 |
+
self.openai_api_key = None
|
142 |
+
self.USE_AI_IMAGE_GENERATION = False
|
143 |
+
self.dalle_model = "dall-e-3"
|
144 |
+
self.image_size_dalle3 = "1792x1024"
|
145 |
self.video_frame_size = (1280, 720)
|
146 |
+
|
147 |
+
self.elevenlabs_api_key = None
|
148 |
+
self.USE_ELEVENLABS = False
|
149 |
+
self.elevenlabs_client = None
|
150 |
self.elevenlabs_voice_id = default_elevenlabs_voice_id
|
151 |
+
if VoiceSettings and ELEVENLABS_CLIENT_IMPORTED:
|
152 |
+
self.elevenlabs_voice_settings = VoiceSettings(
|
153 |
+
stability=0.60,
|
154 |
+
similarity_boost=0.80,
|
155 |
+
style=0.15,
|
156 |
+
use_speaker_boost=True,
|
157 |
+
)
|
158 |
+
else:
|
159 |
+
self.elevenlabs_voice_settings = None
|
160 |
+
|
161 |
+
self.pexels_api_key = None
|
162 |
+
self.USE_PEXELS = False
|
163 |
+
self.runway_api_key = None
|
164 |
+
self.USE_RUNWAYML = False
|
165 |
+
self.runway_ml_client_instance = None # More specific name
|
166 |
+
|
167 |
+
# Attempt to initialize Runway client if SDK is present and env var might be set
|
168 |
+
if (
|
169 |
+
RUNWAYML_SDK_IMPORTED
|
170 |
+
and RunwayMLAPIClient
|
171 |
+
and os.getenv("RUNWAYML_API_SECRET")
|
172 |
+
):
|
173 |
+
try:
|
174 |
+
self.runway_ml_client_instance = RunwayMLAPIClient() # SDK uses env var
|
175 |
+
self.USE_RUNWAYML = True # Assume enabled if client initializes
|
176 |
+
logger.info(
|
177 |
+
"RunwayML Client initialized from RUNWAYML_API_SECRET env var at startup."
|
178 |
+
)
|
179 |
+
except Exception as e_runway_init_startup:
|
180 |
+
logger.error(
|
181 |
+
f"Initial RunwayML client init failed (env var RUNWAYML_API_SECRET might be invalid): {e_runway_init_startup}"
|
182 |
+
)
|
183 |
+
self.USE_RUNWAYML = False
|
184 |
+
|
185 |
logger.info("VisualEngine initialized.")
|
186 |
|
187 |
+
# --- API Key Setters ---
|
188 |
+
def set_openai_api_key(self, api_key):
|
189 |
+
self.openai_api_key = api_key
|
190 |
+
self.USE_AI_IMAGE_GENERATION = bool(api_key)
|
191 |
+
logger.info(
|
192 |
+
f"DALL-E ({self.dalle_model}) status: {'Ready' if self.USE_AI_IMAGE_GENERATION else 'Disabled'}"
|
193 |
+
)
|
194 |
+
|
195 |
+
def set_elevenlabs_api_key(self, api_key, voice_id_from_secret=None):
|
196 |
+
self.elevenlabs_api_key = api_key
|
197 |
+
if voice_id_from_secret:
|
198 |
+
self.elevenlabs_voice_id = voice_id_from_secret
|
199 |
if api_key and ELEVENLABS_CLIENT_IMPORTED and ElevenLabsAPIClient:
|
200 |
+
try:
|
201 |
+
self.elevenlabs_client = ElevenLabsAPIClient(api_key=api_key)
|
202 |
+
self.USE_ELEVENLABS = bool(self.elevenlabs_client)
|
203 |
+
logger.info(
|
204 |
+
f"ElevenLabs Client status: {'Ready' if self.USE_ELEVENLABS else 'Failed Initialization'} (Using Voice ID: {self.elevenlabs_voice_id})"
|
205 |
+
)
|
206 |
+
except Exception as e:
|
207 |
+
logger.error(
|
208 |
+
f"ElevenLabs client initialization error: {e}. Service Disabled.",
|
209 |
+
exc_info=True,
|
210 |
+
)
|
211 |
+
self.USE_ELEVENLABS = False
|
212 |
+
self.elevenlabs_client = None
|
213 |
+
else:
|
214 |
+
self.USE_ELEVENLABS = False
|
215 |
+
logger.info(
|
216 |
+
f"ElevenLabs Service Disabled (API key not provided or SDK import issue)."
|
217 |
+
)
|
218 |
+
|
219 |
+
def set_pexels_api_key(self, api_key):
|
220 |
+
self.pexels_api_key = api_key
|
221 |
+
self.USE_PEXELS = bool(api_key)
|
222 |
+
logger.info(
|
223 |
+
f"Pexels Search status: {'Ready' if self.USE_PEXELS else 'Disabled'}"
|
224 |
+
)
|
225 |
+
|
226 |
+
def set_runway_api_key(self, api_key):
|
227 |
+
self.runway_api_key = api_key # Store key regardless for potential direct HTTP use
|
228 |
+
if api_key:
|
229 |
+
if RUNWAYML_SDK_IMPORTED and RunwayMLAPIClient:
|
230 |
+
if not self.runway_ml_client_instance: # If not already initialized by env var
|
231 |
+
try:
|
232 |
+
# The RunwayML Python SDK expects the API key via the RUNWAYML_API_SECRET env var.
|
233 |
+
# If it's not set, we set it temporarily for client initialization.
|
234 |
+
original_env_secret = os.getenv("RUNWAYML_API_SECRET")
|
235 |
+
if not original_env_secret:
|
236 |
+
logger.info(
|
237 |
+
"Temporarily setting RUNWAYML_API_SECRET from provided key for SDK client init."
|
238 |
+
)
|
239 |
+
os.environ["RUNWAYML_API_SECRET"] = api_key
|
240 |
+
|
241 |
+
self.runway_ml_client_instance = RunwayMLAPIClient()
|
242 |
+
self.USE_RUNWAYML = True # SDK client successfully initialized
|
243 |
+
logger.info(
|
244 |
+
"RunwayML Client initialized successfully using provided API key."
|
245 |
+
)
|
246 |
+
|
247 |
+
if not original_env_secret: # Clean up if we set it
|
248 |
+
del os.environ["RUNWAYML_API_SECRET"]
|
249 |
+
logger.info(
|
250 |
+
"Cleared temporary RUNWAYML_API_SECRET env var."
|
251 |
+
)
|
252 |
+
|
253 |
+
except Exception as e_client_init:
|
254 |
+
logger.error(
|
255 |
+
f"RunwayML Client initialization via set_runway_api_key failed: {e_client_init}",
|
256 |
+
exc_info=True,
|
257 |
+
)
|
258 |
+
self.USE_RUNWAYML = False
|
259 |
+
self.runway_ml_client_instance = None
|
260 |
+
else: # Client was already initialized (likely via env var during __init__)
|
261 |
+
self.USE_RUNWAYML = True
|
262 |
+
logger.info(
|
263 |
+
"RunwayML Client was already initialized (likely from env var). API key stored."
|
264 |
+
)
|
265 |
+
else: # SDK not imported
|
266 |
+
logger.warning(
|
267 |
+
"RunwayML SDK not imported. API key stored, but integration requires SDK. Service effectively disabled."
|
268 |
+
)
|
269 |
+
self.USE_RUNWAYML = False
|
270 |
+
else: # No API key provided
|
271 |
+
self.USE_RUNWAYML = False
|
272 |
+
self.runway_ml_client_instance = None
|
273 |
+
logger.info("RunwayML Service Disabled (no API key provided).")
|
274 |
+
|
275 |
+
# --- Helper Methods ---
|
276 |
+
def _image_to_data_uri(self, image_path):
|
277 |
+
try:
|
278 |
+
mime_type, _ = mimetypes.guess_type(image_path)
|
279 |
+
if not mime_type:
|
280 |
+
ext = os.path.splitext(image_path)[1].lower()
|
281 |
+
mime_map = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg"}
|
282 |
+
mime_type = mime_map.get(ext, "application/octet-stream")
|
283 |
+
if mime_type == "application/octet-stream":
|
284 |
+
logger.warning(
|
285 |
+
f"Could not determine MIME type for {image_path}, using default."
|
286 |
+
)
|
287 |
+
|
288 |
+
with open(image_path, "rb") as image_file:
|
289 |
+
encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
|
290 |
+
data_uri = f"data:{mime_type};base64,{encoded_string}"
|
291 |
+
logger.debug(
|
292 |
+
f"Generated data URI for {os.path.basename(image_path)} (first 100 chars): {data_uri[:100]}..."
|
293 |
+
)
|
294 |
+
return data_uri
|
295 |
+
except FileNotFoundError:
|
296 |
+
logger.error(f"Image file not found at {image_path} for data URI conversion.")
|
297 |
+
return None
|
298 |
+
except Exception as e:
|
299 |
+
logger.error(
|
300 |
+
f"Error converting image {image_path} to data URI: {e}", exc_info=True
|
301 |
+
)
|
302 |
+
return None
|
303 |
+
|
304 |
+
def _map_resolution_to_runway_ratio(self, width, height):
|
305 |
+
ratio_str = f"{width}:{height}"
|
306 |
+
# Gen-4 supports: "1280:720", "720:1280", "1104:832", "832:1104", "960:960", "1584:672"
|
307 |
+
supported_ratios_gen4 = [
|
308 |
+
"1280:720",
|
309 |
+
"720:1280",
|
310 |
+
"1104:832",
|
311 |
+
"832:1104",
|
312 |
+
"960:960",
|
313 |
+
"1584:672",
|
314 |
+
]
|
315 |
+
if ratio_str in supported_ratios_gen4:
|
316 |
+
return ratio_str
|
317 |
+
# Fallback or find closest - for now, strict matching or default
|
318 |
+
logger.warning(
|
319 |
+
f"Resolution {ratio_str} not directly in Gen-4 supported list. Defaulting to 1280:720."
|
320 |
+
)
|
321 |
+
return "1280:720"
|
322 |
+
|
323 |
+
def _get_text_dimensions(self, text_content, font_object):
|
324 |
+
# (Robust version from before)
|
325 |
+
default_char_height = getattr(font_object, "size", self.current_font_size_pil)
|
326 |
+
if not text_content:
|
327 |
+
return 0, default_char_height
|
328 |
try:
|
329 |
+
if hasattr(font_object, "getbbox"):
|
330 |
+
bbox = font_object.getbbox(text_content)
|
331 |
+
w = bbox[2] - bbox[0]
|
332 |
+
h = bbox[3] - bbox[1]
|
333 |
+
return w, h if h > 0 else default_char_height
|
334 |
+
elif hasattr(font_object, "getsize"):
|
335 |
+
w, h = font_object.getsize(text_content)
|
336 |
+
return w, h if h > 0 else default_char_height
|
337 |
+
else:
|
338 |
+
return (
|
339 |
+
int(len(text_content) * default_char_height * 0.6),
|
340 |
+
int(default_char_height * 1.2),
|
341 |
+
)
|
342 |
+
except Exception as e:
|
343 |
+
logger.warning(f"Error in _get_text_dimensions: {e}")
|
344 |
+
return (
|
345 |
+
int(len(text_content) * self.current_font_size_pil * 0.6),
|
346 |
+
int(self.current_font_size_pil * 1.2),
|
347 |
+
)
|
348 |
|
349 |
def _create_placeholder_image_content(self, text_description, filename, size=None):
|
350 |
+
# (Corrected version from previous response)
|
351 |
+
if size is None:
|
352 |
+
size = self.video_frame_size
|
353 |
+
img = Image.new("RGB", size, color=(20, 20, 40))
|
354 |
+
d = ImageDraw.Draw(img)
|
355 |
+
padding = 25
|
356 |
+
max_w = size[0] - (2 * padding)
|
357 |
+
lines = []
|
358 |
+
if not text_description:
|
359 |
+
text_description = "(Placeholder Image)"
|
360 |
+
words = text_description.split()
|
361 |
+
current_line_text = ""
|
362 |
+
for word_idx, word in enumerate(words):
|
363 |
+
prospective_addition = word + (" " if word_idx < len(words) - 1 else "")
|
364 |
+
test_line_text = current_line_text + prospective_addition
|
365 |
+
current_w, _ = self._get_text_dimensions(test_line_text, self.font_pil)
|
366 |
+
if current_w == 0 and test_line_text.strip():
|
367 |
+
current_w = len(test_line_text) * (self.current_font_size_pil * 0.6) # Estimate
|
368 |
+
|
369 |
+
if current_w <= max_w:
|
370 |
+
current_line_text = test_line_text
|
371 |
else:
|
372 |
+
if current_line_text.strip():
|
373 |
+
lines.append(current_line_text.strip())
|
374 |
+
current_line_text = prospective_addition # Start new line
|
375 |
+
if current_line_text.strip():
|
376 |
+
lines.append(current_line_text.strip())
|
377 |
+
|
|
|
|
|
|
|
378 |
if not lines and text_description:
|
379 |
+
avg_char_w, _ = self._get_text_dimensions("W", self.font_pil)
|
380 |
+
avg_char_w = avg_char_w or (self.current_font_size_pil * 0.6)
|
381 |
+
chars_per_line = int(max_w / avg_char_w) if avg_char_w > 0 else 20
|
382 |
+
lines.append(
|
383 |
+
text_description[:chars_per_line]
|
384 |
+
+ ("..." if len(text_description) > chars_per_line else "")
|
385 |
+
)
|
386 |
+
elif not lines:
|
387 |
+
lines.append("(Placeholder Error)")
|
388 |
+
|
389 |
+
_, single_line_h = self._get_text_dimensions("Ay", self.font_pil)
|
390 |
+
single_line_h = single_line_h if single_line_h > 0 else self.current_font_size_pil + 2
|
391 |
+
max_lines = (
|
392 |
+
min(len(lines), (size[1] - (2 * padding)) // (single_line_h + 2))
|
393 |
+
if single_line_h > 0
|
394 |
+
else 1
|
395 |
+
)
|
396 |
+
max_lines = max(1, max_lines) # Ensure at least one line
|
397 |
+
|
398 |
+
y_pos = padding + (size[1] - (2 * padding) - max_lines * (single_line_h + 2)) / 2.0
|
399 |
+
for i in range(max_lines):
|
400 |
+
line_text = lines[i]
|
401 |
+
line_w, _ = self._get_text_dimensions(line_text, self.font_pil)
|
402 |
+
if line_w == 0 and line_text.strip():
|
403 |
+
line_w = len(line_text) * (self.current_font_size_pil * 0.6)
|
404 |
+
x_pos = (size[0] - line_w) / 2.0
|
405 |
+
try:
|
406 |
+
d.text((x_pos, y_pos), line_text, font=self.font_pil, fill=(200, 200, 180))
|
407 |
+
except Exception as e_draw:
|
408 |
+
logger.error(f"Pillow d.text error: {e_draw} for '{line_text}'")
|
409 |
+
y_pos += single_line_h + 2
|
410 |
+
if i == 6 and max_lines > 7:
|
411 |
+
try:
|
412 |
+
d.text((x_pos, y_pos), "...", font=self.font_pil, fill=(200, 200, 180))
|
413 |
+
except Exception as e_elip:
|
414 |
+
logger.error(f"Pillow d.text ellipsis error: {e_elip}")
|
415 |
+
break
|
416 |
+
|
417 |
filepath = os.path.join(self.output_dir, filename)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
418 |
try:
|
419 |
+
img.save(filepath)
|
420 |
+
return filepath
|
421 |
+
except Exception as e_save:
|
422 |
+
logger.error(
|
423 |
+
f"Saving placeholder image '{filepath}' error: {e_save}", exc_info=True
|
424 |
+
)
|
425 |
+
return None
|
426 |
+
|
427 |
+
def _search_pexels_image(self, query, output_filename_base):
|
428 |
+
# <<< THIS IS THE CORRECTED METHOD >>>
|
429 |
+
if not self.USE_PEXELS or not self.pexels_api_key:
|
430 |
+
return None
|
431 |
+
headers = {"Authorization": self.pexels_api_key}
|
432 |
+
params = {"query": query, "per_page": 1, "orientation": "landscape", "size": "large2x"}
|
433 |
+
base_name_for_pexels, _ = os.path.splitext(output_filename_base)
|
434 |
+
pexels_filename = base_name_for_pexels + f"_pexels_{random.randint(1000,9999)}.jpg"
|
435 |
+
filepath = os.path.join(self.output_dir, pexels_filename)
|
436 |
+
try:
|
437 |
+
logger.info(f"Pexels: Searching for '{query}'")
|
438 |
+
effective_query = " ".join(query.split()[:5])
|
439 |
+
params["query"] = effective_query
|
440 |
+
response = requests.get(
|
441 |
+
"https://api.pexels.com/v1/search", headers=headers, params=params, timeout=20
|
442 |
+
)
|
443 |
+
response.raise_for_status()
|
444 |
+
data = response.json()
|
445 |
+
if data.get("photos") and len(data["photos"]) > 0:
|
446 |
+
photo_details = data["photos"][0]
|
447 |
+
photo_url = photo_details.get("src", {}).get("large2x")
|
448 |
+
if not photo_url:
|
449 |
+
logger.warning(
|
450 |
+
f"Pexels: 'large2x' URL missing for '{effective_query}'. Details: {photo_details}"
|
451 |
+
)
|
452 |
+
return None
|
453 |
+
image_response = requests.get(photo_url, timeout=60)
|
454 |
+
image_response.raise_for_status()
|
455 |
+
img_data_pil = Image.open(io.BytesIO(image_response.content))
|
456 |
+
if img_data_pil.mode != "RGB":
|
457 |
+
img_data_pil = img_data_pil.convert("RGB")
|
458 |
+
img_data_pil.save(filepath)
|
459 |
+
logger.info(f"Pexels: Image saved to {filepath}")
|
460 |
+
return filepath
|
461 |
+
else:
|
462 |
+
logger.info(f"Pexels: No photos for '{effective_query}'.")
|
463 |
+
return None
|
464 |
+
except requests.exceptions.RequestException as e_req:
|
465 |
+
logger.error(f"Pexels: RequestException for '{query}': {e_req}", exc_info=False)
|
466 |
+
return None # Less verbose for network
|
467 |
+
except Exception as e:
|
468 |
+
logger.error(f"Pexels: General error for '{query}': {e}", exc_info=True)
|
469 |
+
return None
|
470 |
+
|
471 |
+
# --- RunwayML Video Generation (Gen-4 Aligned with SDK) ---
|
472 |
+
def _generate_video_clip_with_runwayml(
|
473 |
+
self,
|
474 |
+
text_prompt_for_motion,
|
475 |
+
input_image_path,
|
476 |
+
scene_identifier_filename_base,
|
477 |
+
target_duration_seconds=5,
|
478 |
+
):
|
479 |
+
if not self.USE_RUNWAYML or not self.runway_ml_client_instance:
|
480 |
+
logger.warning("RunwayML not enabled or client not initialized. Cannot generate video clip.")
|
481 |
+
return None
|
482 |
+
if not input_image_path or not os.path.exists(input_image_path):
|
483 |
+
logger.error(
|
484 |
+
f"Runway Gen-4 requires an input image. Path not provided or invalid: {input_image_path}"
|
485 |
+
)
|
486 |
+
return None
|
487 |
+
|
488 |
+
image_data_uri = self._image_to_data_uri(input_image_path)
|
489 |
+
if not image_data_uri:
|
490 |
+
return None
|
491 |
+
|
492 |
+
runway_duration = 10 if target_duration_seconds >= 8 else 5 # Map to 5s or 10s for Gen-4
|
493 |
+
runway_ratio_str = self._map_resolution_to_runway_ratio(
|
494 |
+
self.video_frame_size[0], self.video_frame_size[1]
|
495 |
+
)
|
496 |
+
|
497 |
+
# Use a more descriptive output filename for Runway videos
|
498 |
+
base_name_for_runway, _ = os.path.splitext(scene_identifier_filename_base)
|
499 |
+
output_video_filename = base_name_for_runway + f"_runway_gen4_d{runway_duration}s.mp4"
|
500 |
+
output_video_filepath = os.path.join(self.output_dir, output_video_filename)
|
501 |
+
|
502 |
+
logger.info(
|
503 |
+
f"Initiating Runway Gen-4 task: motion='{text_prompt_for_motion[:100]}...', image='{os.path.basename(input_image_path)}', dur={runway_duration}s, ratio='{runway_ratio_str}'"
|
504 |
+
)
|
505 |
+
try:
|
506 |
+
# Using the RunwayML Python SDK structure
|
507 |
+
task_submission = self.runway_ml_client_instance.image_to_video.create(
|
508 |
+
model="gen4_turbo",
|
509 |
+
prompt_image=image_data_uri,
|
510 |
+
prompt_text=text_prompt_for_motion, # This is the motion prompt
|
511 |
+
duration=runway_duration,
|
512 |
+
ratio=runway_ratio_str,
|
513 |
+
# seed=random.randint(0, 4294967295), # Optional: for reproducibility
|
514 |
+
# Other Gen-4 params (motion_score, upscale, watermark etc. can be added here if available in SDK)
|
515 |
+
)
|
516 |
+
task_id = task_submission.id
|
517 |
+
logger.info(f"Runway Gen-4 task created with ID: {task_id}. Polling for completion...")
|
518 |
+
|
519 |
+
poll_interval_seconds = 10
|
520 |
+
max_polling_duration_seconds = 6 * 60 # 6 minutes
|
521 |
+
start_time = time.time()
|
522 |
+
|
523 |
+
while time.time() - start_time < max_polling_duration_seconds:
|
524 |
+
time.sleep(poll_interval_seconds)
|
525 |
+
task_details = self.runway_ml_client_instance.tasks.retrieve(id=task_id)
|
526 |
+
logger.info(f"Runway task {task_id} status: {task_details.status}")
|
527 |
+
|
528 |
+
if task_details.status == "SUCCEEDED":
|
529 |
+
# Determine output URL (this structure might vary based on SDK version)
|
530 |
+
output_url = None
|
531 |
+
if hasattr(task_details, "output") and task_details.output and hasattr(
|
532 |
+
task_details.output, "url"
|
533 |
+
):
|
534 |
+
output_url = task_details.output.url
|
535 |
+
elif (
|
536 |
+
hasattr(task_details, "artifacts")
|
537 |
+
and task_details.artifacts
|
538 |
+
and isinstance(task_details.artifacts, list)
|
539 |
+
and len(task_details.artifacts) > 0
|
540 |
+
):
|
541 |
+
first_artifact = task_details.artifacts[0]
|
542 |
+
if hasattr(first_artifact, "url"):
|
543 |
+
output_url = first_artifact.url
|
544 |
+
elif hasattr(first_artifact, "download_url"):
|
545 |
+
output_url = first_artifact.download_url
|
546 |
+
|
547 |
+
if not output_url:
|
548 |
+
logger.error(
|
549 |
+
f"Runway task {task_id} SUCCEEDED, but no output URL found. Details: {vars(task_details) if hasattr(task_details,'__dict__') else str(task_details)}"
|
550 |
+
)
|
551 |
+
return None
|
552 |
+
|
553 |
+
logger.info(f"Runway task {task_id} SUCCEEDED. Downloading video from: {output_url}")
|
554 |
+
video_response = requests.get(output_url, stream=True, timeout=300)
|
555 |
+
video_response.raise_for_status()
|
556 |
+
with open(output_video_filepath, "wb") as f:
|
557 |
+
for chunk in video_response.iter_content(chunk_size=8192):
|
558 |
+
f.write(chunk)
|
559 |
+
logger.info(
|
560 |
+
f"Runway Gen-4 video successfully downloaded to: {output_video_filepath}"
|
561 |
+
)
|
562 |
+
return output_video_filepath
|
563 |
+
|
564 |
+
elif task_details.status in ["FAILED", "ABORTED", "ERROR"]: # Added ERROR
|
565 |
+
error_msg = (
|
566 |
+
getattr(task_details, "error_message", None)
|
567 |
+
or getattr(getattr(task_details, "output", None), "error", "Unknown error from Runway task.")
|
568 |
+
)
|
569 |
+
logger.error(
|
570 |
+
f"Runway task {task_id} final status: {task_details.status}. Error: {error_msg}"
|
571 |
+
)
|
572 |
+
return None
|
573 |
+
|
574 |
+
logger.warning(
|
575 |
+
f"Runway task {task_id} timed out polling after {max_polling_duration_seconds} seconds."
|
576 |
+
)
|
577 |
+
return None
|
578 |
+
|
579 |
+
except AttributeError as ae: # If SDK methods are not as expected
|
580 |
+
logger.error(
|
581 |
+
f"AttributeError with RunwayML SDK: {ae}. Ensure SDK is up to date and methods/attributes match documentation.",
|
582 |
+
exc_info=True,
|
583 |
+
)
|
584 |
+
return None
|
585 |
+
except Exception as e_runway_call:
|
586 |
+
logger.error(
|
587 |
+
f"General error during Runway Gen-4 API call or processing: {e_runway_call}",
|
588 |
+
exc_info=True,
|
589 |
+
)
|
590 |
+
return None
|
591 |
+
|
592 |
+
def _create_placeholder_video_content(self, text_description, filename, duration=4, size=None):
|
593 |
+
# (Keeping as before)
|
594 |
+
if size is None:
|
595 |
+
size = self.video_frame_size
|
596 |
+
fp = os.path.join(self.output_dir, filename)
|
597 |
+
tc = None
|
598 |
+
try:
|
599 |
+
tc = TextClip(
|
600 |
+
text_description,
|
601 |
+
fontsize=50,
|
602 |
+
color="white",
|
603 |
+
font=self.video_overlay_font,
|
604 |
+
bg_color="black",
|
605 |
+
size=size,
|
606 |
+
method="caption",
|
607 |
+
).set_duration(duration)
|
608 |
+
tc.write_videofile(
|
609 |
+
fp, fps=24, codec="libx264", preset="ultrafast", logger=None, threads=2
|
610 |
+
)
|
611 |
+
logger.info(f"Generic placeholder video: {fp}")
|
612 |
+
return fp
|
613 |
+
except Exception as e:
|
614 |
+
logger.error(f"Generic placeholder video error {fp}: {e}", exc_info=True)
|
615 |
+
return None
|
616 |
finally:
|
617 |
+
if tc and hasattr(tc, "close"):
|
618 |
+
tc.close()
|
619 |
|
620 |
+
# --- generate_scene_asset (Main asset generation logic using Runway Gen-4 workflow) ---
|
621 |
+
def generate_scene_asset(
|
622 |
+
self,
|
623 |
+
image_generation_prompt_text,
|
624 |
+
motion_prompt_text_for_video,
|
625 |
+
scene_data,
|
626 |
+
scene_identifier_filename_base,
|
627 |
+
generate_as_video_clip=False,
|
628 |
+
runway_target_duration=5,
|
629 |
+
):
|
630 |
+
# (Logic updated for improved DALL·E and RunwayML fallback)
|
631 |
base_name, _ = os.path.splitext(scene_identifier_filename_base)
|
632 |
+
asset_info = {
|
633 |
+
"path": None,
|
634 |
+
"type": "none",
|
635 |
+
"error": True,
|
636 |
+
"prompt_used": image_generation_prompt_text,
|
637 |
+
"error_message": "Asset generation init failed",
|
638 |
+
}
|
639 |
input_image_for_runway_path = None
|
640 |
+
# Use a distinct name for the base image if it's only an intermediate step for video
|
641 |
+
base_image_filename = base_name + ("_base_for_video.png" if generate_as_video_clip else ".png")
|
642 |
+
base_image_filepath = os.path.join(self.output_dir, base_image_filename)
|
643 |
|
644 |
+
# STEP 1: Generate/acquire the base image via DALL·E
|
645 |
if self.USE_AI_IMAGE_GENERATION and self.openai_api_key:
|
646 |
+
try:
|
647 |
+
logger.info(f"Calling DALL·E with prompt: {image_generation_prompt_text[:70]}...")
|
648 |
+
response = openai.Image.create(
|
649 |
+
prompt=image_generation_prompt_text,
|
650 |
+
n=1,
|
651 |
+
size=self.image_size_dalle3,
|
652 |
+
model=self.dalle_model,
|
653 |
+
)
|
654 |
+
image_url = response["data"][0]["url"]
|
655 |
+
ir = requests.get(image_url, timeout=120)
|
656 |
+
ir.raise_for_status()
|
657 |
+
id_img = Image.open(io.BytesIO(ir.content))
|
658 |
+
if id_img.mode != "RGB":
|
659 |
+
id_img = id_img.convert("RGB")
|
660 |
+
id_img.save(base_image_filepath)
|
661 |
+
logger.info(f"DALL·E base image saved: {base_image_filepath}")
|
662 |
+
input_image_for_runway_path = base_image_filepath
|
663 |
+
asset_info = {
|
664 |
+
"path": base_image_filepath,
|
665 |
+
"type": "image",
|
666 |
+
"error": False,
|
667 |
+
"prompt_used": image_generation_prompt_text,
|
668 |
+
}
|
669 |
+
except openai.error.OpenAIError as e:
|
670 |
+
logger.warning(f"DALL·E error: {e}. Falling back to Pexels or placeholder.")
|
671 |
+
asset_info["error_message"] = str(e)
|
672 |
+
except Exception as e:
|
673 |
+
logger.error(f"Unexpected DALL·E error: {e}", exc_info=True)
|
674 |
+
asset_info["error_message"] = str(e)
|
675 |
+
|
676 |
+
# STEP 2: If DALL·E failed, try Pexels
|
677 |
+
if asset_info["error"] and self.USE_PEXELS:
|
678 |
+
logger.info("Attempting Pexels fallback for base image.")
|
679 |
+
pqt = scene_data.get(
|
680 |
+
"pexels_search_query_감독", f"{scene_data.get('emotional_beat','')} {scene_data.get('setting_description','')}"
|
681 |
+
)
|
682 |
+
pp = self._search_pexels_image(pqt, base_image_filename)
|
683 |
+
if pp:
|
684 |
+
input_image_for_runway_path = pp
|
685 |
+
asset_info = {
|
686 |
+
"path": pp,
|
687 |
+
"type": "image",
|
688 |
+
"error": False,
|
689 |
+
"prompt_used": f"Pexels:{pqt}",
|
690 |
+
}
|
691 |
+
else:
|
692 |
+
current_em = asset_info.get("error_message", "")
|
693 |
+
asset_info["error_message"] = (current_em + " Pexels fallback failed.").strip()
|
694 |
+
|
695 |
+
# STEP 3: If both DALL·E and Pexels failed, create placeholder
|
696 |
+
if asset_info["error"]:
|
697 |
+
logger.warning("Both DALL·E and Pexels failed. Creating placeholder image.")
|
698 |
+
ppt = asset_info.get("prompt_used", image_generation_prompt_text)
|
699 |
+
php = self._create_placeholder_image_content(
|
700 |
+
f"[Placeholder for] {ppt[:70]}...", base_image_filename
|
701 |
+
)
|
702 |
+
if php:
|
703 |
+
input_image_for_runway_path = php
|
704 |
+
asset_info = {
|
705 |
+
"path": php,
|
706 |
+
"type": "image",
|
707 |
+
"error": False,
|
708 |
+
"prompt_used": ppt,
|
709 |
+
}
|
710 |
+
else:
|
711 |
+
current_em = asset_info.get("error_message", "")
|
712 |
+
asset_info["error_message"] = (current_em + " Placeholder creation failed.").strip()
|
713 |
+
|
714 |
+
# STEP 4: If a video clip is requested, attempt RunwayML
|
715 |
if generate_as_video_clip:
|
716 |
+
if not input_image_for_runway_path or not os.path.exists(input_image_for_runway_path):
|
717 |
+
logger.error("No valid base image for RunwayML. Skipping video generation.")
|
718 |
+
asset_info["error"] = True
|
719 |
+
asset_info["error_message"] = (asset_info.get("error_message", "") + " No base image.").strip()
|
720 |
+
asset_info["type"] = "none"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
721 |
return asset_info
|
722 |
+
|
723 |
+
if self.USE_RUNWAYML and self.runway_ml_client_instance:
|
724 |
+
video_path = self._generate_video_clip_with_runwayml(
|
725 |
+
motion_prompt_text_for_video,
|
726 |
+
input_image_for_runway_path,
|
727 |
+
base_name,
|
728 |
+
runway_target_duration,
|
729 |
+
)
|
730 |
+
if video_path and os.path.exists(video_path):
|
731 |
+
asset_info = {
|
732 |
+
"path": video_path,
|
733 |
+
"type": "video",
|
734 |
+
"error": False,
|
735 |
+
"prompt_used": motion_prompt_text_for_video,
|
736 |
+
"base_image_path": input_image_for_runway_path,
|
737 |
+
}
|
738 |
+
else:
|
739 |
+
logger.warning("RunwayML video generation failed. Returning base image instead.")
|
740 |
+
asset_info = {
|
741 |
+
"path": input_image_for_runway_path,
|
742 |
+
"type": "image",
|
743 |
+
"error": True,
|
744 |
+
"prompt_used": image_generation_prompt_text,
|
745 |
+
"error_message": (asset_info.get("error_message", "") + " RunwayML failed.").strip(),
|
746 |
+
}
|
747 |
+
else:
|
748 |
+
logger.warning("RunwayML not enabled or client not initialized. Skipping video generation.")
|
749 |
+
asset_info = {
|
750 |
+
"path": input_image_for_runway_path,
|
751 |
+
"type": "image",
|
752 |
+
"error": True,
|
753 |
+
"prompt_used": image_generation_prompt_text,
|
754 |
+
"error_message": (asset_info.get("error_message", "") + " RunwayML disabled.").strip(),
|
755 |
+
}
|
756 |
+
|
757 |
+
return asset_info
|
758 |
+
|
759 |
+
def generate_narration_audio(self, text_to_narrate, output_filename="narration_overall.mp3"):
|
760 |
+
# (Keep as before - robust enough)
|
761 |
+
if not self.USE_ELEVENLABS or not self.elevenlabs_client or not text_to_narrate:
|
762 |
+
logger.info("ElevenLabs audio skipped.")
|
763 |
+
return None
|
764 |
+
|
765 |
+
afp = os.path.join(self.output_dir, output_filename)
|
766 |
+
try:
|
767 |
+
logger.info(f"ElevenLabs audio (Voice:{self.elevenlabs_voice_id}): {text_to_narrate[:70]}...")
|
768 |
+
asm = None
|
769 |
+
|
770 |
+
if hasattr(self.elevenlabs_client, "text_to_speech") and hasattr(
|
771 |
+
self.elevenlabs_client.text_to_speech, "stream"
|
772 |
+
):
|
773 |
+
asm = self.elevenlabs_client.text_to_speech.stream
|
774 |
+
logger.info("Using ElevenLabs .text_to_speech.stream()")
|
775 |
+
elif hasattr(self.elevenlabs_client, "generate_stream"):
|
776 |
+
asm = self.elevenlabs_client.generate_stream
|
777 |
+
logger.info("Using ElevenLabs .generate_stream()")
|
778 |
+
elif hasattr(self.elevenlabs_client, "generate"):
|
779 |
+
logger.info("Using ElevenLabs .generate()")
|
780 |
+
vp = (
|
781 |
+
Voice(voice_id=str(self.elevenlabs_voice_id), settings=self.elevenlabs_voice_settings)
|
782 |
+
if Voice and self.elevenlabs_voice_settings
|
783 |
+
else str(self.elevenlabs_voice_id)
|
784 |
+
)
|
785 |
+
ab = self.elevenlabs_client.generate(
|
786 |
+
text=text_to_narrate, voice=vp, model="eleven_multilingual_v2"
|
787 |
+
)
|
788 |
+
with open(afp, "wb") as f:
|
789 |
+
f.write(ab)
|
790 |
+
logger.info(f"ElevenLabs audio (non-stream) saved: {afp}")
|
791 |
+
return afp
|
792 |
+
else:
|
793 |
+
logger.error("No ElevenLabs audio method available.")
|
794 |
+
return None
|
795 |
+
|
796 |
+
# If we have a streaming method (asm), use it
|
797 |
+
if asm:
|
798 |
+
vps = {"voice_id": str(self.elevenlabs_voice_id)}
|
799 |
+
if self.elevenlabs_voice_settings:
|
800 |
+
if hasattr(self.elevenlabs_voice_settings, "model_dump"):
|
801 |
+
vps["voice_settings"] = self.elevenlabs_voice_settings.model_dump()
|
802 |
+
elif hasattr(self.elevenlabs_voice_settings, "dict"):
|
803 |
+
vps["voice_settings"] = self.elevenlabs_voice_settings.dict()
|
804 |
+
else:
|
805 |
+
vps["voice_settings"] = self.elevenlabs_voice_settings
|
806 |
+
|
807 |
+
adi = asm(text=text_to_narrate, model_id="eleven_multilingual_v2", **vps)
|
808 |
+
with open(afp, "wb") as f:
|
809 |
+
for c in adi:
|
810 |
+
if c:
|
811 |
+
f.write(c)
|
812 |
+
logger.info(f"ElevenLabs audio (stream) saved: {afp}")
|
813 |
+
return afp
|
814 |
+
|
815 |
+
except Exception as e:
|
816 |
+
logger.error(f"ElevenLabs audio error: {e}", exc_info=True)
|
817 |
+
return None
|
818 |
+
|
819 |
+
# --- assemble_animatic_from_assets (Still contains crucial debug saves for blank video issue) ---
|
820 |
+
def assemble_animatic_from_assets(
|
821 |
+
self, asset_data_list, overall_narration_path=None, output_filename="final_video.mp4", fps=24
|
822 |
+
):
|
823 |
+
# (Keep the version with robust image processing, C-contiguous arrays, debug saves, and pix_fmt)
|
824 |
+
if not asset_data_list:
|
825 |
+
logger.warning("No assets for animatic.")
|
826 |
+
return None
|
827 |
+
|
828 |
+
processed_clips = []
|
829 |
+
narration_clip = None
|
830 |
+
final_clip = None
|
831 |
logger.info(f"Assembling from {len(asset_data_list)} assets. Frame: {self.video_frame_size}.")
|
832 |
|
833 |
for i, asset_info in enumerate(asset_data_list):
|
834 |
+
asset_path = asset_info.get("path")
|
835 |
+
asset_type = asset_info.get("type")
|
836 |
+
scene_dur = asset_info.get("duration", 4.5)
|
837 |
+
scene_num = asset_info.get("scene_num", i + 1)
|
838 |
+
key_action = asset_info.get("key_action", "")
|
839 |
logger.info(f"S{scene_num}: Path='{asset_path}', Type='{asset_type}', Dur='{scene_dur}'s")
|
840 |
|
841 |
+
if not (asset_path and os.path.exists(asset_path)):
|
842 |
+
logger.warning(f"S{scene_num}: Not found '{asset_path}'. Skip.")
|
843 |
+
continue
|
844 |
+
if scene_dur <= 0:
|
845 |
+
logger.warning(f"S{scene_num}: Invalid duration ({scene_dur}s). Skip.")
|
846 |
+
continue
|
847 |
|
848 |
current_scene_mvpy_clip = None
|
849 |
try:
|
850 |
+
if asset_type == "image":
|
851 |
+
pil_img = Image.open(asset_path)
|
852 |
+
logger.debug(f"S{scene_num}: Loaded img. Mode:{pil_img.mode}, Size:{pil_img.size}")
|
853 |
+
img_rgba = pil_img.convert("RGBA") if pil_img.mode != "RGBA" else pil_img.copy()
|
854 |
+
thumb = img_rgba.copy()
|
855 |
+
rf = Image.Resampling.LANCZOS if hasattr(Image.Resampling, "LANCZOS") else Image.BILINEAR
|
856 |
+
thumb.thumbnail(self.video_frame_size, rf)
|
857 |
+
cv_rgba = Image.new("RGBA", self.video_frame_size, (0, 0, 0, 0))
|
858 |
+
xo, yo = (
|
859 |
+
(self.video_frame_size[0] - thumb.width) // 2,
|
860 |
+
(self.video_frame_size[1] - thumb.height) // 2,
|
861 |
+
)
|
862 |
+
cv_rgba.paste(thumb, (xo, yo), thumb)
|
863 |
+
final_rgb_pil = Image.new("RGB", self.video_frame_size, (0, 0, 0))
|
864 |
+
final_rgb_pil.paste(cv_rgba, mask=cv_rgba.split()[3])
|
865 |
+
dbg_path = os.path.join(self.output_dir, f"debug_PRE_NUMPY_S{scene_num}.png")
|
866 |
+
final_rgb_pil.save(dbg_path)
|
867 |
+
logger.info(f"DEBUG: Saved PRE_NUMPY_S{scene_num} to {dbg_path}")
|
868 |
+
frame_np = np.array(final_rgb_pil, dtype=np.uint8)
|
869 |
+
if not frame_np.flags["C_CONTIGUOUS"]:
|
870 |
+
frame_np = np.ascontiguousarray(frame_np, dtype=np.uint8)
|
871 |
+
logger.debug(
|
872 |
+
f"S{scene_num}: NumPy for MoviePy. Shape:{frame_np.shape}, DType:{frame_np.dtype}, C-Contig:{frame_np.flags['C_CONTIGUOUS']}"
|
873 |
+
)
|
874 |
+
if frame_np.size == 0 or frame_np.ndim != 3 or frame_np.shape[2] != 3:
|
875 |
+
logger.error(f"S{scene_num}: Invalid NumPy. Skip.")
|
876 |
+
continue
|
877 |
+
clip_base = ImageClip(frame_np, transparent=False).set_duration(scene_dur)
|
878 |
+
mvpy_dbg_path = os.path.join(self.output_dir, f"debug_MOVIEPY_FRAME_S{scene_num}.png")
|
879 |
+
clip_base.save_frame(mvpy_dbg_path, t=0.1)
|
880 |
+
logger.info(f"DEBUG: Saved MOVIEPY_FRAME_S{scene_num} to {mvpy_dbg_path}")
|
881 |
clip_fx = clip_base
|
882 |
+
try:
|
883 |
+
es = random.uniform(1.03, 1.08)
|
884 |
+
clip_fx = clip_base.fx(
|
885 |
+
vfx.resize, lambda t: 1 + (es - 1) * (t / scene_dur) if scene_dur > 0 else 1
|
886 |
+
).set_position("center")
|
887 |
+
except Exception as e:
|
888 |
+
logger.error(f"S{scene_num} Ken Burns error: {e}", exc_info=False)
|
889 |
current_scene_mvpy_clip = clip_fx
|
890 |
|
891 |
+
elif asset_type == "video":
|
892 |
+
src_clip = None
|
893 |
try:
|
894 |
+
src_clip = VideoFileClip(
|
895 |
+
asset_path,
|
896 |
+
target_resolution=(
|
897 |
+
self.video_frame_size[1],
|
898 |
+
self.video_frame_size[0],
|
899 |
+
)
|
900 |
+
if self.video_frame_size
|
901 |
+
else None,
|
902 |
+
audio=False,
|
903 |
+
)
|
904 |
+
tmp_clip = src_clip
|
905 |
+
if src_clip.duration != scene_dur:
|
906 |
+
if src_clip.duration > scene_dur:
|
907 |
+
tmp_clip = src_clip.subclip(0, scene_dur)
|
908 |
else:
|
909 |
+
if scene_dur / src_clip.duration > 1.5 and src_clip.duration > 0.1:
|
910 |
+
tmp_clip = src_clip.loop(duration=scene_dur)
|
911 |
+
else:
|
912 |
+
tmp_clip = src_clip.set_duration(src_clip.duration)
|
913 |
+
logger.info(
|
914 |
+
f"S{scene_num} Video clip ({src_clip.duration:.2f}s) shorter than target ({scene_dur:.2f}s)."
|
915 |
+
)
|
916 |
+
current_scene_mvpy_clip = tmp_clip.set_duration(scene_dur)
|
917 |
+
if current_scene_mvpy_clip.size != list(self.video_frame_size):
|
918 |
+
current_scene_mvpy_clip = current_scene_mvpy_clip.resize(self.video_frame_size)
|
919 |
+
except Exception as e:
|
920 |
+
logger.error(f"S{scene_num} Video load error '{asset_path}':{e}", exc_info=True)
|
921 |
+
continue
|
922 |
finally:
|
923 |
+
if src_clip and src_clip is not current_scene_mvpy_clip and hasattr(src_clip, "close"):
|
924 |
+
src_clip.close()
|
925 |
+
else:
|
926 |
+
logger.warning(f"S{scene_num} Unknown asset type '{asset_type}'. Skip.")
|
927 |
+
continue
|
928 |
+
|
929 |
if current_scene_mvpy_clip and key_action:
|
930 |
try:
|
931 |
+
to_dur = (
|
932 |
+
min(current_scene_mvpy_clip.duration - 0.5, current_scene_mvpy_clip.duration * 0.8)
|
933 |
+
if current_scene_mvpy_clip.duration > 0.5
|
934 |
+
else current_scene_mvpy_clip.duration
|
935 |
+
)
|
936 |
+
to_start = 0.25
|
937 |
+
if to_dur > 0:
|
938 |
+
txt_c = TextClip(
|
939 |
+
f"Scene {scene_num}\n{key_action}",
|
940 |
+
fontsize=self.VIDEO_OVERLAY_FONT_SIZE,
|
941 |
+
color=self.VIDEO_OVERLAY_FONT_COLOR,
|
942 |
+
font=self.video_overlay_font,
|
943 |
+
bg_color="rgba(10,10,20,0.7)",
|
944 |
+
method="caption",
|
945 |
+
align="West",
|
946 |
+
size=(self.video_frame_size[0] * 0.9, None),
|
947 |
+
kerning=-1,
|
948 |
+
stroke_color="black",
|
949 |
+
stroke_width=1.5,
|
950 |
+
).set_duration(to_dur).set_start(to_start).set_position(
|
951 |
+
("center", 0.92), relative=True
|
952 |
+
)
|
953 |
+
current_scene_mvpy_clip = CompositeVideoClip(
|
954 |
+
[current_scene_mvpy_clip, txt_c], size=self.video_frame_size, use_bgclip=True
|
955 |
+
)
|
956 |
+
else:
|
957 |
+
logger.warning(f"S{scene_num}: Text overlay duration is zero. Skip text.")
|
958 |
+
except Exception as e:
|
959 |
+
logger.error(f"S{scene_num} TextClip error:{e}. No text.", exc_info=True)
|
960 |
+
|
961 |
+
if current_scene_mvpy_clip:
|
962 |
+
processed_clips.append(current_scene_mvpy_clip)
|
963 |
+
logger.info(f"S{scene_num} Processed. Dur:{current_scene_mvpy_clip.duration:.2f}s.")
|
964 |
+
except Exception as e:
|
965 |
+
logger.error(f"MAJOR Error S{scene_num} ({asset_path}):{e}", exc_info=True)
|
966 |
finally:
|
967 |
+
if current_scene_mvpy_clip and hasattr(current_scene_mvpy_clip, "close"):
|
968 |
+
try:
|
969 |
+
current_scene_mvpy_clip.close()
|
970 |
+
except:
|
971 |
+
pass
|
972 |
+
|
973 |
+
if not processed_clips:
|
974 |
+
logger.warning("No clips processed. Abort.")
|
975 |
+
return None
|
976 |
|
977 |
+
td = 0.75
|
|
|
978 |
try:
|
979 |
+
logger.info(f"Concatenating {len(processed_clips)} clips.")
|
980 |
+
if len(processed_clips) > 1:
|
981 |
+
final_clip = concatenate_videoclips(processed_clips, padding=-td if td > 0 else 0, method="compose")
|
982 |
+
elif processed_clips:
|
983 |
+
final_clip = processed_clips[0]
|
984 |
+
if not final_clip:
|
985 |
+
logger.error("Concatenation failed.")
|
986 |
+
return None
|
987 |
+
|
988 |
logger.info(f"Concatenated dur:{final_clip.duration:.2f}s")
|
989 |
+
if td > 0 and final_clip.duration > 0:
|
990 |
+
if final_clip.duration > td * 2:
|
991 |
+
final_clip = final_clip.fx(vfx.fadein, td).fx(vfx.fadeout, td)
|
992 |
+
else:
|
993 |
+
final_clip = final_clip.fx(vfx.fadein, min(td, final_clip.duration / 2.0))
|
994 |
+
|
995 |
+
if overall_narration_path and os.path.exists(overall_narration_path) and final_clip.duration > 0:
|
996 |
+
try:
|
997 |
+
narration_clip = AudioFileClip(overall_narration_path)
|
998 |
+
final_clip = final_clip.set_audio(narration_clip)
|
999 |
+
logger.info("Narration added.")
|
1000 |
+
except Exception as e:
|
1001 |
+
logger.error(f"Narration add error:{e}", exc_info=True)
|
1002 |
+
elif final_clip.duration <= 0:
|
1003 |
+
logger.warning("Video no duration. No audio.")
|
1004 |
+
|
1005 |
+
if final_clip and final_clip.duration > 0:
|
1006 |
+
op = os.path.join(self.output_dir, output_filename)
|
1007 |
+
logger.info(f"Writing video:{op} (Dur:{final_clip.duration:.2f}s)")
|
1008 |
+
final_clip.write_videofile(
|
1009 |
+
op,
|
1010 |
+
fps=fps,
|
1011 |
+
codec="libx264",
|
1012 |
+
preset="medium",
|
1013 |
+
audio_codec="aac",
|
1014 |
+
temp_audiofile=os.path.join(self.output_dir, f"temp-audio-{os.urandom(4).hex()}.m4a"),
|
1015 |
+
remove_temp=True,
|
1016 |
+
threads=os.cpu_count() or 2,
|
1017 |
+
logger="bar",
|
1018 |
+
bitrate="5000k",
|
1019 |
+
ffmpeg_params=["-pix_fmt", "yuv420p"],
|
1020 |
+
)
|
1021 |
+
logger.info(f"Video created:{op}")
|
1022 |
+
return op
|
1023 |
+
else:
|
1024 |
+
logger.error("Final clip invalid. No write.")
|
1025 |
+
return None
|
1026 |
+
except Exception as e:
|
1027 |
+
logger.error(f"Video write error:{e}", exc_info=True)
|
1028 |
+
return None
|
1029 |
finally:
|
1030 |
logger.debug("Closing all MoviePy clips in `assemble_animatic_from_assets` finally block.")
|
1031 |
+
all_clips_to_close = processed_clips + ([narration_clip] if narration_clip else []) + ([final_clip] if final_clip else [])
|
1032 |
+
for clip_obj_to_close in all_clips_to_close:
|
1033 |
+
if clip_obj_to_close and hasattr(clip_obj_to_close, "close"):
|
1034 |
+
try:
|
1035 |
+
clip_obj_to_close.close()
|
1036 |
+
except Exception as e_close:
|
1037 |
+
logger.warning(
|
1038 |
+
f"Ignoring error while closing a clip: {type(clip_obj_to_close).__name__} - {e_close}"
|
1039 |
+
)
|