272 lines
8.2 KiB
Python
272 lines
8.2 KiB
Python
import os
|
|
import random
|
|
import torch
|
|
from PIL import Image, ImageOps
|
|
import numpy as np
|
|
import cv2
|
|
import time
|
|
|
|
|
|
class RandomFilePathNode:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"directory_path": ("STRING", {"default": ""}),
|
|
},
|
|
}
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, **kwargs):
|
|
return float("NaN")
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
FUNCTION = "get_random_file_path"
|
|
CATEGORY = "🥭 芒果节点/文件"
|
|
|
|
def get_random_file_path(self, directory_path: str) -> str:
|
|
if not os.path.isdir(directory_path):
|
|
raise NotADirectoryError(
|
|
f"'{directory_path}' is not a valid directory path.")
|
|
|
|
files = []
|
|
|
|
# Walk through the directory tree
|
|
for root, dirs, files_in_dir in os.walk(directory_path):
|
|
for file_name in files_in_dir:
|
|
# Build full path to the file
|
|
full_file_path = os.path.join(root, file_name)
|
|
# Check if the file has a valid extension
|
|
files.append(full_file_path)
|
|
|
|
if not files:
|
|
raise FileNotFoundError(
|
|
f"No files found in directory: {directory_path}")
|
|
|
|
path = random.choice(files)
|
|
return (path,)
|
|
|
|
|
|
class RandomImagePathNode:
|
|
def __init__(self):
|
|
self.current_seed = 0
|
|
self.current_index = 0
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"directory_path": ("STRING", {"default": ""}),
|
|
"sort_mode": (["完全随机" , "顺序循环"],),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
}
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, **kwargs):
|
|
return float("NaN")
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
|
|
RETURN_NAMES = ("image", "mask", "text_content")
|
|
FUNCTION = "get_random_image_path"
|
|
CATEGORY = "🥭 芒果节点/文件"
|
|
|
|
def get_random_image_path(self, directory_path, sort_mode, seed) -> tuple:
|
|
if not os.path.isdir(directory_path):
|
|
raise NotADirectoryError(
|
|
f"'{directory_path}' is not a valid directory path.")
|
|
|
|
valid_extensions = (".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp")
|
|
files = []
|
|
|
|
for root, dirs, files_in_dir in os.walk(directory_path):
|
|
for file_name in files_in_dir:
|
|
full_file_path = os.path.join(root, file_name)
|
|
if file_name.lower().endswith(valid_extensions):
|
|
files.append(full_file_path)
|
|
|
|
if not files:
|
|
raise FileNotFoundError(
|
|
f"No image files found in directory: {directory_path}")
|
|
|
|
# 根据排序模式选择文件
|
|
elif sort_mode == "顺序循环":
|
|
files.sort(key=lambda x: os.path.basename(x).lower())
|
|
path = files[self.current_index]
|
|
self.current_index = (self.current_index + 1) % len(files)
|
|
else: # 完全随机模式
|
|
path = random.choice(files)
|
|
|
|
image = Image.open(path)
|
|
image = ImageOps.exif_transpose(image)
|
|
|
|
# 处理alpha通道
|
|
if image.mode == 'RGBA':
|
|
rgb = image.convert('RGB')
|
|
alpha = image.split()[3]
|
|
|
|
image = np.array(rgb).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
|
|
mask = np.array(alpha).astype(np.float32) / 255.0
|
|
mask = torch.from_numpy(mask)[None,]
|
|
else:
|
|
image = image.convert('RGB')
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
|
|
mask = torch.ones((1, image.shape[2], image.shape[3]), dtype=torch.float32)
|
|
|
|
# 获取对应的txt文件内容
|
|
txt_path = os.path.splitext(path)[0] + '.txt'
|
|
text_content = ""
|
|
try:
|
|
if os.path.exists(txt_path):
|
|
with open(txt_path, 'r', encoding='utf-8') as f:
|
|
text_content = f.read().strip()
|
|
else:
|
|
text_content = "No corresponding text file found"
|
|
except Exception as e:
|
|
text_content = f"Error reading text file: {str(e)}"
|
|
|
|
return (image, mask, text_content)
|
|
|
|
|
|
video_extensions = ('webm', 'mp4', 'mkv', 'gif')
|
|
|
|
|
|
class RandomVideoPathNode:
|
|
def __init__(self):
|
|
self.current_seed = 0
|
|
self.current_index = 0
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"directory_path": ("STRING", {"default": ""}),
|
|
"sort_mode": (["完全随机", "顺序循环"],),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
}
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, **kwargs):
|
|
return float("NaN")
|
|
|
|
RETURN_TYPES = ("IMAGE", "STRING", "STRING")
|
|
RETURN_NAMES = ("images", "path", "text_content")
|
|
FUNCTION = "get_random_video_path"
|
|
CATEGORY = "🥭 芒果节点/文件"
|
|
|
|
def get_random_video_path(self, directory_path, sort_mode, seed) -> tuple:
|
|
if not os.path.isdir(directory_path):
|
|
raise NotADirectoryError(
|
|
f"'{directory_path}' is not a valid directory path.")
|
|
|
|
files = []
|
|
for root, dirs, files_in_dir in os.walk(directory_path):
|
|
for file_name in files_in_dir:
|
|
full_file_path = os.path.join(root, file_name)
|
|
if file_name.lower().endswith(video_extensions):
|
|
files.append(full_file_path)
|
|
|
|
if not files:
|
|
raise FileNotFoundError(
|
|
f"No video files found in directory: {directory_path}")
|
|
|
|
# 根据排序模式选择文件
|
|
if sort_mode == "顺序循环":
|
|
files.sort(key=lambda x: os.path.basename(x).lower())
|
|
path = files[self.current_index]
|
|
self.current_index = (self.current_index + 1) % len(files)
|
|
else: # 完全随机模式
|
|
random.seed(seed)
|
|
path = random.choice(files)
|
|
|
|
images = FrameGenerator(path)
|
|
|
|
# 获取对应的txt文件内容
|
|
txt_path = os.path.splitext(path)[0] + '.txt'
|
|
text_content = ""
|
|
try:
|
|
if os.path.exists(txt_path):
|
|
with open(txt_path, 'r', encoding='utf-8') as f:
|
|
text_content = f.read().strip()
|
|
else:
|
|
text_content = "No corresponding text file found"
|
|
except Exception as e:
|
|
text_content = f"Error reading text file: {str(e)}"
|
|
|
|
return (images, path, text_content)
|
|
|
|
|
|
def get_video_frames(video_path):
|
|
video_cap = cv2.VideoCapture(video_path)
|
|
|
|
if not video_cap.isOpened():
|
|
raise ValueError(f"Could not open video file: {video_path}")
|
|
|
|
frames = []
|
|
while True:
|
|
ret, frame = video_cap.read()
|
|
if not ret:
|
|
break
|
|
frames.append(frame)
|
|
|
|
video_cap.release()
|
|
return frames
|
|
|
|
|
|
class FrameGenerator:
|
|
def __init__(self, video_path):
|
|
self.video_path = video_path
|
|
self.frames = self._load_frames()
|
|
|
|
def _load_frames(self):
|
|
video_cap = cv2.VideoCapture(self.video_path)
|
|
if not video_cap.isOpened():
|
|
raise ValueError(f"Could not open video file: {self.video_path}")
|
|
|
|
frames = []
|
|
while True:
|
|
ret, frame = video_cap.read()
|
|
if not ret:
|
|
break
|
|
|
|
# Convert frame from BGR to RGB
|
|
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
|
|
|
# Convert frame to a torch tensor and normalize it
|
|
frame_tensor = torch.from_numpy(frame).float() / 255.0
|
|
|
|
frames.append(frame_tensor)
|
|
|
|
video_cap.release()
|
|
return frames
|
|
|
|
def __len__(self):
|
|
return len(self.frames)
|
|
|
|
def __getitem__(self, index):
|
|
return self.frames[index]
|
|
|
|
def __iter__(self):
|
|
return iter(self.frames)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"Random Video Path": RandomVideoPathNode,
|
|
"Random Image Path": RandomImagePathNode,
|
|
"Random File Path": RandomFilePathNode,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"Random Video Path": "随机视频 🥭",
|
|
"Random Image Path": "随机图片 🥭",
|
|
"Random File Path": "随机文件 🥭",
|
|
}
|