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#---------------------------------------------------------------------------------------------------------------------#
# CR Animation Nodes by RockOfFire and Akatsuzi https://github.com/Suzie1/CR-Animation-Nodes
# for ComfyUI https://github.com/comfyanonymous/ComfyUI
#---------------------------------------------------------------------------------------------------------------------#
from PIL import Image, ImageSequence
import comfy.sd
import re
import torch
import numpy as np
import os
import sys
import folder_paths
import math
import json
import csv
from typing import List
from PIL.PngImagePlugin import PngInfo
from nodes import SaveImage
import glob
from ..categories import icons
#MAX_RESOLUTION=8192
ALLOWED_EXT = ('.jpeg', '.jpg', '.png', '.tiff', '.gif', '.bmp', '.webp')
def resolve_pattern(pattern):
folder_path, file_pattern = os.path.split(pattern)
frame_pattern = re.sub(r"#+", "*", file_pattern)
matching_files = glob.glob(os.path.join(folder_path, frame_pattern))
#print(f"[Debug] Found {len(matching_files)} matching files for frame pattern {frame_pattern}")
return matching_files
def get_files(image_path, sort_by="Index", pattern=None):
if pattern is not None:
matching_files = resolve_pattern(os.path.join(image_path, pattern))
else:
matching_files = os.listdir(image_path)
if sort_by == "Index":
sorted_files = sorted(matching_files, key=lambda s: sum(((s, int(n)) for s, n in re.findall(r'(\D+)(\d+)', 'a%s0' % s)), ()))
elif sort_by == "Alphabetic":
sorted_files = sorted(matching_files, key=lambda s: (re.split(r'(\d+)', s), s))
else:
raise ValueError("Invalid sort_by value. Use 'Index' or 'Alphabetic'.")
return sorted_files
#---------------------------------------------------------------------------------------------------------------------#
# NODES
#---------------------------------------------------------------------------------------------------------------------#
class CR_LoadAnimationFrames:
#input_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))), 'input')
input_dir = folder_paths.input_directory
#print(f"CR_LoadAnimationFrames: input directory {input_dir}")
@classmethod
def INPUT_TYPES(s):
#if not os.path.exists(s.input_dir):
#os.makedirs(s.input_dir)
image_folder = [name for name in os.listdir(s.input_dir) if os.path.isdir(os.path.join(s.input_dir,name)) and len(os.listdir(os.path.join(s.input_dir,name))) != 0]
return {"required":
{"image_sequence_folder": (sorted(image_folder), ),
"start_index": ("INT", {"default": 1, "min": 1, "max": 10000}),
"max_frames": ("INT", {"default": 1, "min": 1, "max": 10000})
}
}
RETURN_TYPES = ("IMAGE", "STRING", )
RETURN_NAMES = ("IMAGE", "show_help", )
FUNCTION = "load_image_sequence"
CATEGORY = icons.get("Comfyroll/Animation/IO")
def load_image_sequence(self, image_sequence_folder, start_index, max_frames):
image_path = os.path.join(self.input_dir, image_sequence_folder)
file_list = sorted(os.listdir(image_path), key=lambda s: sum(((s, int(n)) for s, n in re.findall(r'(\D+)(\d+)', 'a%s0' % s)), ()))
sample_frames = []
sample_frames_mask = []
sample_index = list(range(start_index-1, len(file_list), 1))[:max_frames]
for num in sample_index:
i = Image.open(os.path.join(image_path, file_list[num]))
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
image = image.squeeze()
sample_frames.append(image)
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/IO-Nodes#cr-load-animation-frames"
return (torch.stack(sample_frames), show_help, )
#---------------------------------------------------------------------------------------------------------------------#
class CR_LoadFlowFrames:
# based on Load Image Sequence in vid2vid and mtb
@classmethod
def INPUT_TYPES(s):
sort_methods = ["Index", "Alphabetic"]
#sort_methods = ["Date modified", "Alphabetic", "Index"]
input_dir = folder_paths.input_directory
input_folders = [name for name in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir,name)) and len(os.listdir(os.path.join(input_dir,name))) != 0]
return {"required":
{"input_folder": (sorted(input_folders), ),
"sort_by": (sort_methods, ),
"current_frame": ("INT", {"default": 0, "min": 0, "max": 10000, "forceInput": True}),
"skip_start_frames": ("INT", {"default": 0, "min": 0, "max": 10000}),
},
"optional":
{"input_path": ("STRING", {"default": '', "multiline": False}),
"file_pattern": ("STRING", {"default": '*.png', "multiline": False}),
}
}
CATEGORY = icons.get("Comfyroll/Animation/IO")
RETURN_TYPES = ("IMAGE", "IMAGE", "INT", "STRING", )
RETURN_NAMES = ("current_image", "previous_image", "current_frame", "show_help", )
FUNCTION = "load_images"
def load_images(self, file_pattern, skip_start_frames, input_folder, sort_by, current_frame, input_path=''):
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/IO-Nodes#cr-load-flow-frames"
input_dir = folder_paths.input_directory
current_frame = current_frame + skip_start_frames
print(f"[Info] CR Load Flow Frames: current_frame {current_frame}")
if input_path != '':
if not os.path.exists(input_path):
print(f"[Warning] CR Load Flow Frames: The input_path `{input_path}` does not exist")
return ("", )
image_path = os.path.join('', input_path)
else:
image_path = os.path.join(input_dir, input_folder)
print(f"[Info] CR Load Flow Frames: ComfyUI Input directory is `{image_path}`")
file_list = get_files(image_path, sort_by, file_pattern)
if os.path.exists(image_path + '.DS_Store'):
file_list.remove('.DS_Store') # For Mac users
if len(file_list) == 0:
print(f"[Warning] CR Load Flow Frames: No matching files found for loading")
return ()
remaining_files = len(file_list) - current_frame
print(f"[Info] CR Load Flow Frames: {remaining_files} input files remaining for processing")
img = Image.open(os.path.join(image_path, file_list[current_frame]))
cur_image = img.convert("RGB")
cur_image = np.array(cur_image).astype(np.float32) / 255.0
cur_image = torch.from_numpy(cur_image)[None,]
print(f"[Debug] CR Load Flow Frames: Current image {file_list[current_frame]}")
# Load first frame as previous frame if no frames skipped
if current_frame == 0 and skip_start_frames == 0:
img = Image.open(os.path.join(image_path, file_list[current_frame]))
pre_image = img.convert("RGB")
pre_image = np.array(pre_image).astype(np.float32) / 255.0
pre_image = torch.from_numpy(pre_image)[None,]
print(f"[Debug] CR Load Flow Frames: Previous image {file_list[current_frame]}")
else:
img = Image.open(os.path.join(image_path, file_list[current_frame - 1]))
pre_image = img.convert("RGB")
pre_image = np.array(pre_image).astype(np.float32) / 255.0
pre_image = torch.from_numpy(pre_image)[None,]
print(f"[Debug] CR Load Flow Frames: Previous image {file_list[current_frame - 1]}")
return (cur_image, pre_image, current_frame, show_help, )
#---------------------------------------------------------------------------------------------------------------------#
class CR_OutputFlowFrames:
# based on SaveImageSequence by mtb
def __init__(self):
self.type = "output"
@classmethod
def INPUT_TYPES(cls):
output_dir = folder_paths.output_directory
output_folders = [name for name in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir,name)) and len(os.listdir(os.path.join(output_dir,name))) != 0]
return {
"required": {"output_folder": (sorted(output_folders), ),
"current_image": ("IMAGE", ),
"filename_prefix": ("STRING", {"default": "CR"}),
"current_frame": ("INT", {"default": 0, "min": 0, "max": 9999999, "forceInput": True}),
},
"optional": {"interpolated_img": ("IMAGE", ),
"output_path": ("STRING", {"default": '', "multiline": False}),
}
}
RETURN_TYPES = ()
FUNCTION = "save_images"
OUTPUT_NODE = True
CATEGORY = icons.get("Comfyroll/Animation/IO")
def save_images(self, output_folder, current_image, current_frame, output_path='', filename_prefix="CR", interpolated_img=None):
output_dir = folder_paths.get_output_directory()
out_folder = os.path.join(output_dir, output_folder)
if output_path != '':
if not os.path.exists(output_path):
print(f"[Warning] CR Output Flow Frames: The input_path `{output_path}` does not exist")
return ("",)
out_path = output_path # os.path.join("", output_path)
else:
out_path = os.path.join(output_dir, out_folder)
print(f"[Info] CR Output Flow Frames: Output path is `{out_path}`")
if interpolated_img is not None:
output_image0 = current_image[0].cpu().numpy()
output_image1 = interpolated_img[0].cpu().numpy()
img0 = Image.fromarray(np.clip(output_image0 * 255.0, 0, 255).astype(np.uint8))
img1 = Image.fromarray(np.clip(output_image1 * 255.0, 0, 255).astype(np.uint8))
output_filename0 = f"{filename_prefix}_{current_frame:05}_0.png"
output_filename1 = f"{filename_prefix}_{current_frame:05}_1.png"
print(f"[Warning] CR Output Flow Frames: Saved {filename_prefix}_{current_frame:05}_0.png")
print(f"[Warning] CR Output Flow Frames: Saved {filename_prefix}_{current_frame:05}_1.png")
resolved_image_path0 = out_path + "/" + output_filename0
resolved_image_path1 = out_path + "/" + output_filename1
img0.save(resolved_image_path0, pnginfo="", compress_level=4)
img1.save(resolved_image_path1, pnginfo="", compress_level=4)
else:
output_image0 = current_image[0].cpu().numpy()
img0 = Image.fromarray(np.clip(output_image0 * 255.0, 0, 255).astype(np.uint8))
output_filename0 = f"{filename_prefix}_{current_frame:05}.png"
resolved_image_path0 = out_path + "/" + output_filename0
img0.save(resolved_image_path0, pnginfo="", compress_level=4)
print(f"[Info] CR Output Flow Frames: Saved {filename_prefix}_{current_frame:05}.png")
result = {"ui": {"images": [{"filename": output_filename0,"subfolder": out_path,"type": self.type,}]}}
return result
#---------------------------------------------------------------------------------------------------------------------#
# MAPPINGS
#---------------------------------------------------------------------------------------------------------------------#
# For reference only, actual mappings are in __init__.py
# 3 nodes released
'''
NODE_CLASS_MAPPINGS = {
# IO
"CR Load Animation Frames":CR_LoadAnimationFrames,
"CR Load Flow Frames":CR_LoadFlowFrames,
"CR Output Flow Frames":CR_OutputFlowFrames,
}
'''
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