more nodes, cleanup
This commit is contained in:
LonicaMewinsky
+14
-1
@@ -1,13 +1,26 @@
|
||||
from .nodes.MakeFrame import(BreakFrames, GetKeyFrames, MakeGrid)
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
|
||||
here = os.path.dirname(__file__)
|
||||
requirements_path = os.path.join(here, "requirements.txt")
|
||||
|
||||
try:
|
||||
from .nodes.MakeFrame import BreakFrames, GetKeyFrames, MakeGrid, BreakGrid
|
||||
except:
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', requirements_path])
|
||||
from .nodes.MakeFrame import BreakFrames, GetKeyFrames, MakeGrid, BreakGrid
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"BreakFrames": BreakFrames,
|
||||
"GetKeyFrames": GetKeyFrames,
|
||||
"MakeGrid": MakeGrid,
|
||||
"BreakGrid": BreakGrid,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"BreakFrames": "BreakFrames",
|
||||
"GetKeyFrames": "GetKeyFrames",
|
||||
"MakeGrid": "MakeGrid",
|
||||
"BreakGrid": "BreakGrid",
|
||||
}
|
||||
+39
-4
@@ -36,7 +36,6 @@ def normalize_size(images):
|
||||
if images[i].size != refimage.size:
|
||||
images[i] = images[i].resize(refimage.size, Image.Resampling.LANCZOS)
|
||||
return_images.append(images[i])
|
||||
np.lcm(6, 8)
|
||||
return return_images
|
||||
|
||||
def constrain_image(image, max_width, max_height):
|
||||
@@ -187,8 +186,44 @@ def cat_to_pils(tensor):
|
||||
pils = [to_pil(tensor[i]) for i in range(tensor.shape[0])]
|
||||
return pils
|
||||
|
||||
def pil_to_cat(pimg):
|
||||
def pil_to_tens(pimg):
|
||||
to_tensor = ToTensor()
|
||||
tensor = to_tensor(pimg).unsqueeze(0).permute(0, 2, 3, 1)
|
||||
print(tensor.shape)
|
||||
return tensor
|
||||
return tensor
|
||||
|
||||
def get_grid_aspect(num_images: int, image_width: int, image_height: int) -> (int, int):
|
||||
|
||||
if num_images == 0:
|
||||
return 0, 0
|
||||
|
||||
min_diff = float('inf')
|
||||
best_layout = (1, num_images)
|
||||
|
||||
if image_width > image_height:
|
||||
for cols in range(1, num_images + 1):
|
||||
rows = -(-num_images // cols)
|
||||
grid_width = cols * image_width
|
||||
grid_height = rows * image_height
|
||||
diff = abs(grid_width - grid_height)
|
||||
|
||||
if diff < min_diff:
|
||||
min_diff = diff
|
||||
best_layout = (rows, cols)
|
||||
|
||||
if cols > num_images / cols:
|
||||
break
|
||||
else:
|
||||
for rows in range(1, num_images + 1):
|
||||
cols = -(-num_images // rows)
|
||||
grid_width = cols * image_width
|
||||
grid_height = rows * image_height
|
||||
diff = abs(grid_height - grid_width)
|
||||
|
||||
if diff < min_diff:
|
||||
min_diff = diff
|
||||
best_layout = (rows, cols)
|
||||
|
||||
if rows > num_images / rows:
|
||||
break
|
||||
|
||||
return best_layout
|
||||
+57
-37
@@ -1,6 +1,5 @@
|
||||
import cv2
|
||||
import torch
|
||||
import torchvision.transforms as transforms
|
||||
import numpy as np
|
||||
import os
|
||||
from PIL import Image
|
||||
@@ -42,19 +41,16 @@ class BreakFrames:
|
||||
print(f"Error: Could not open video file {file_input}.")
|
||||
return None
|
||||
# Read each frame from anim
|
||||
frame_count = 0
|
||||
transformer = transforms.ToTensor()
|
||||
tensors = []
|
||||
while True:
|
||||
ret, frame = video_capture.read()
|
||||
if ret:
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
tensors.append(transformer(frame).to(device).unsqueeze(0))
|
||||
frame_count += 1
|
||||
tensors.append(mfu.pil_to_tens(frame).to(device))
|
||||
else:
|
||||
break
|
||||
video_capture.release()
|
||||
cat_frame_tensors = torch.cat(tensors, dim = 0).permute(0, 2, 3, 1)
|
||||
cat_frame_tensors = torch.cat(tensors, dim = 0).to(device)
|
||||
|
||||
return (cat_frame_tensors,)
|
||||
|
||||
@@ -75,8 +71,8 @@ class GetKeyFrames:
|
||||
}),
|
||||
},
|
||||
}
|
||||
RETURN_TYPES = ("IMAGE", "INT",)
|
||||
RETURN_NAMES = ("Keyframes", "Keyframe indices",)
|
||||
RETURN_TYPES = ("IMAGE", "IMAGE")
|
||||
RETURN_NAMES = ("Keyframes", "Labeled Keyframes")
|
||||
|
||||
FUNCTION = "getkeyframes"
|
||||
CATEGORY = "Frames"
|
||||
@@ -89,8 +85,14 @@ class GetKeyFrames:
|
||||
keyframe_indices = sorted([index.item() + 1 for index in top_indices])
|
||||
keyframe_indices.insert(0, 0)
|
||||
cat_keyframe_tensors = [frames[i].to(device).unsqueeze(0) for i in keyframe_indices]
|
||||
cat_keyframe_tensors = torch.cat(cat_keyframe_tensors, dim = 0)
|
||||
return (cat_keyframe_tensors, keyframe_indices)
|
||||
cat_keyframe_tensors = torch.cat(cat_keyframe_tensors, dim = 0).to(device)
|
||||
|
||||
pils = mfu.cat_to_pils(cat_keyframe_tensors)
|
||||
keyframes_labeled = [mfu.ImgLabeler(frame, str(idx), size=72, color="#ffffff") for frame, idx in zip(pils, keyframe_indices)]
|
||||
keyframe_labeled_tensors = [mfu.pil_to_tens(keyframe) for keyframe in keyframes_labeled]
|
||||
cat_keyframe_tensors_labeled = torch.cat(keyframe_labeled_tensors, dim = 0).to(device)
|
||||
|
||||
return (cat_keyframe_tensors, cat_keyframe_tensors_labeled)
|
||||
|
||||
class MakeGrid:
|
||||
def __init__(self):
|
||||
@@ -101,49 +103,67 @@ class MakeGrid:
|
||||
return {
|
||||
"required": {
|
||||
"frames": ("IMAGE", ),
|
||||
"grid_rows": ("INT", {
|
||||
"default": 4,
|
||||
"min": 2,
|
||||
"max": 24,
|
||||
"step": 1
|
||||
}),
|
||||
"grid_cols": ("INT", {
|
||||
"default": 4,
|
||||
"min": 2,
|
||||
"max": 24,
|
||||
"step": 1
|
||||
}),
|
||||
"max_width": ("INT", {
|
||||
"default": 1024,
|
||||
"default": 2048,
|
||||
"min": 64,
|
||||
"max": 4096,
|
||||
"max": 8000,
|
||||
"step": 8
|
||||
}),
|
||||
"max_height": ("INT", {
|
||||
"default": 1024,
|
||||
"default": 2048,
|
||||
"min": 64,
|
||||
"max": 4096,
|
||||
"max": 8000,
|
||||
"step": 8
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", )
|
||||
RETURN_NAMES = ("Grid",)
|
||||
RETURN_TYPES = ("IMAGE", "INT", "INT")
|
||||
RETURN_NAMES = ("Grid", "Rows", "Columns")
|
||||
|
||||
FUNCTION = "makegrid"
|
||||
CATEGORY = "Frames"
|
||||
|
||||
def makegrid(self, frames, grid_rows, grid_cols, max_width, max_height):
|
||||
# Pad the list with extras; black space frustrates generation
|
||||
def makegrid(self, frames, max_width, max_height):
|
||||
|
||||
# Size needs to be divisible by 8 AND the grid dimension
|
||||
if not grid_rows == 8: max_height = mfu.closest_lcm(max_height, 8, grid_rows)
|
||||
if not grid_cols == 8: max_width = mfu.closest_lcm(max_width, 8, grid_cols)
|
||||
# Build base grid
|
||||
pils = mfu.cat_to_pils(frames)
|
||||
pils = mfu.normalize_size(pils) #normalize sizes to /8
|
||||
grid = mfu.constrain_image(mfu.MakeGrid(pils, grid_rows, grid_cols), max_width, max_height)
|
||||
grid = mfu.pil_to_cat(grid)
|
||||
rows, cols = mfu.get_grid_aspect(len(pils), pils[0].width, pils[0].height)
|
||||
if len(pils) < rows*cols:
|
||||
pils = mfu.padlist(pils, rows*cols) #pad list with repeats (black space bad)
|
||||
if not rows == 8: max_height = mfu.closest_lcm(max_height, 8, rows)
|
||||
if not cols == 8: max_width = mfu.closest_lcm(max_width, 8, cols)
|
||||
|
||||
return (grid,)
|
||||
grid = mfu.constrain_image(mfu.MakeGrid(pils, rows, cols), max_width, max_height)
|
||||
grid = mfu.pil_to_tens(grid).to(device)
|
||||
return (grid, rows, cols)
|
||||
|
||||
class BreakGrid:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"grid": ("IMAGE",),
|
||||
"rows": ("INT",{}),
|
||||
"columns": ("INT",{}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("Frames",)
|
||||
|
||||
FUNCTION = "breakgrid"
|
||||
CATEGORY = "Frames"
|
||||
|
||||
def breakgrid(self, grid, rows, columns):
|
||||
pilgrids = mfu.cat_to_pils(grid)
|
||||
frames =[]
|
||||
for pilgrid in pilgrids:
|
||||
frames.extend(mfu.BreakGrid(pilgrid, rows, columns))
|
||||
frame_tensors = [mfu.pil_to_tens(frame) for frame in frames]
|
||||
cat_frame_tensors = torch.cat(frame_tensors, dim = 0).unsqueeze(0)
|
||||
|
||||
return (cat_frame_tensors)
|
||||
@@ -0,0 +1 @@
|
||||
opencv-python
|
||||
Reference in New Issue
Block a user