mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 16:26:39 +00:00
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
import hashlib
|
|
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image, ImageOps, ImageSequence
|
|
|
|
import folder_paths
|
|
import node_helpers
|
|
import nodes
|
|
from comfy_api.v3 import io
|
|
|
|
|
|
class WebcamCapture(io.ComfyNodeV3):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.SchemaV3(
|
|
node_id="WebcamCapture_V3",
|
|
display_name="Webcam Capture _V3",
|
|
category="image",
|
|
inputs=[
|
|
io.Webcam.Input("image"),
|
|
io.Int.Input("width", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
|
|
io.Int.Input("height", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
|
|
io.Boolean.Input("capture_on_queue", default=True),
|
|
],
|
|
outputs=[
|
|
io.Image.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, image, **kwargs) -> io.NodeOutput:
|
|
img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
|
|
|
|
output_images = []
|
|
output_masks = []
|
|
w, h = None, None
|
|
|
|
excluded_formats = ["MPO"]
|
|
|
|
for i in ImageSequence.Iterator(img):
|
|
i = node_helpers.pillow(ImageOps.exif_transpose, i)
|
|
|
|
if i.mode == "I":
|
|
i = i.point(lambda i: i * (1 / 255))
|
|
image = i.convert("RGB")
|
|
|
|
if len(output_images) == 0:
|
|
w = image.size[0]
|
|
h = image.size[1]
|
|
|
|
if image.size[0] != w or image.size[1] != h:
|
|
continue
|
|
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
if "A" in i.getbands():
|
|
mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
|
|
mask = 1.0 - torch.from_numpy(mask)
|
|
elif i.mode == "P" and "transparency" in i.info:
|
|
mask = np.array(i.convert("RGBA").getchannel("A")).astype(np.float32) / 255.0
|
|
mask = 1.0 - torch.from_numpy(mask)
|
|
else:
|
|
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
|
|
output_images.append(image)
|
|
output_masks.append(mask.unsqueeze(0))
|
|
|
|
if len(output_images) > 1 and img.format not in excluded_formats:
|
|
output_image = torch.cat(output_images, dim=0)
|
|
output_mask = torch.cat(output_masks, dim=0)
|
|
else:
|
|
output_image = output_images[0]
|
|
output_mask = output_masks[0]
|
|
|
|
return io.NodeOutput(output_image, output_mask)
|
|
|
|
@classmethod
|
|
def fingerprint_inputs(s, image, width, height, capture_on_queue):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
m = hashlib.sha256()
|
|
with open(image_path, "rb") as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
@classmethod
|
|
def validate_inputs(s, image):
|
|
if not folder_paths.exists_annotated_filepath(image):
|
|
return "Invalid image file: {}".format(image)
|
|
return True
|
|
|
|
|
|
NODES_LIST: list[type[io.ComfyNodeV3]] = [WebcamCapture]
|