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a_person_mask_generator_comfyui.py
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/
a_person_mask_generator_comfyui.py
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import os
import sys
sys.path.append(
os.path.dirname(os.path.abspath(__file__))
)
from functools import reduce
import wget
import cv2
import torch
import numpy as np
from PIL import Image
import mediapipe as mp
import folder_paths
def get_a_person_mask_generator_model_path() -> str:
model_folder_name = 'mediapipe'
model_name = 'selfie_multiclass_256x256.tflite'
model_folder_path = os.path.join(folder_paths.models_dir, model_folder_name)
model_file_path = os.path.join(model_folder_path, model_name)
if not os.path.exists(model_file_path):
model_url = f'https://storage.googleapis.com/mediapipe-models/image_segmenter/selfie_multiclass_256x256/float32/latest/{model_name}'
print(f"Downloading '{model_name}' model")
os.makedirs(model_folder_path, exist_ok=True)
wget.download(model_url, model_file_path)
return model_file_path
class APersonMaskGenerator:
def __init__(self):
# download the model if we need it
get_a_person_mask_generator_model_path()
@classmethod
def INPUT_TYPES(self):
return {
"required":
{
"images": ("IMAGE",),
},
"optional":
{
"face_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
"background_mask": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"hair_mask": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"body_mask": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"clothes_mask": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"confidence": ("FLOAT", {"default": 0.40, "min": 0.01, "max": 1.0, "step": 0.01},),
}
}
CATEGORY = "A Person Mask Generator - David Bielejeski"
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("masks",)
FUNCTION = "generate_mask"
def get_mediapipe_image(self, image: Image) -> mp.Image:
# Convert image to NumPy array
numpy_image = np.asarray(image)
image_format = mp.ImageFormat.SRGB
# Convert BGR to RGB (if necessary)
if numpy_image.shape[-1] == 4:
image_format = mp.ImageFormat.SRGBA
elif numpy_image.shape[-1] == 3:
image_format = mp.ImageFormat.SRGB
numpy_image = cv2.cvtColor(numpy_image, cv2.COLOR_BGR2RGB)
return mp.Image(image_format=image_format, data=numpy_image)
def generate_mask(self, images, face_mask: bool, background_mask: bool, hair_mask: bool, body_mask: bool, clothes_mask: bool, confidence: float):
"""Create a segmentation mask from an image
Args:
image (torch.Tensor): The image to create the mask for.
face_mask (bool): create a mask for the background.
background_mask (bool): create a mask for the hair.
hair_mask (bool): create a mask for the body .
body_mask (bool): create a mask for the face.
clothes_mask (bool): create a mask for the clothes.
Returns:
torch.Tensor: The segmentation masks.
"""
a_person_mask_generator_model_path = get_a_person_mask_generator_model_path()
a_person_mask_generator_model_buffer = None
with open(a_person_mask_generator_model_path, "rb") as f:
a_person_mask_generator_model_buffer = f.read()
image_segmenter_base_options = mp.tasks.BaseOptions(model_asset_buffer=a_person_mask_generator_model_buffer)
options = mp.tasks.vision.ImageSegmenterOptions(
base_options=image_segmenter_base_options,
running_mode=mp.tasks.vision.RunningMode.IMAGE,
output_category_mask=True)
# Create the image segmenter
res_masks = []
with mp.tasks.vision.ImageSegmenter.create_from_options(options) as segmenter:
for image in images:
# Convert the Tensor to a PIL image
i = 255. * image.cpu().numpy()
image_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
# create our foreground and background arrays for storing the mask results
mask_background_array = np.zeros((image_pil.size[0], image_pil.size[1], 4), dtype=np.uint8)
mask_background_array[:] = (0, 0, 0, 255)
mask_foreground_array = np.zeros((image_pil.size[0], image_pil.size[1], 4), dtype=np.uint8)
mask_foreground_array[:] = (255, 255, 255, 255)
# Retrieve the masks for the segmented image
media_pipe_image = self.get_mediapipe_image(image=image_pil)
segmented_masks = segmenter.segment(media_pipe_image)
# https://developers.google.com/mediapipe/solutions/vision/image_segmenter#multiclass-model
# 0 - background
# 1 - hair
# 2 - body - skin
# 3 - face - skin
# 4 - clothes
# 5 - others(accessories)
masks = []
if background_mask:
masks.append(segmented_masks.confidence_masks[0])
if hair_mask:
masks.append(segmented_masks.confidence_masks[1])
if body_mask:
masks.append(segmented_masks.confidence_masks[2])
if face_mask:
masks.append(segmented_masks.confidence_masks[3])
if clothes_mask:
masks.append(segmented_masks.confidence_masks[4])
image_data = media_pipe_image.numpy_view()
image_shape = image_data.shape
# convert the image shape from "rgb" to "rgba" aka add the alpha channel
if image_shape[-1] == 3:
image_shape = (image_shape[0], image_shape[1], 4)
mask_background_array = np.zeros(image_shape, dtype=np.uint8)
mask_background_array[:] = (0, 0, 0, 255)
mask_foreground_array = np.zeros(image_shape, dtype=np.uint8)
mask_foreground_array[:] = (255, 255, 255, 255)
mask_arrays = []
if len(masks) == 0:
mask_arrays.append(mask_background_array)
else:
for i, mask in enumerate(masks):
condition = np.stack((mask.numpy_view(),) * image_shape[-1], axis=-1) > confidence
mask_array = np.where(condition, mask_foreground_array, mask_background_array)
mask_arrays.append(mask_array)
# Merge our masks taking the maximum from each
merged_mask_arrays = reduce(np.maximum, mask_arrays)
# Create the image
mask_image = Image.fromarray(merged_mask_arrays)
# convert PIL image to tensor image
tensor_mask = mask_image.convert("RGB")
tensor_mask = np.array(tensor_mask).astype(np.float32) / 255.0
tensor_mask = torch.from_numpy(tensor_mask)[None,]
tensor_mask = tensor_mask.squeeze(3)[..., 0]
res_masks.append(tensor_mask)
return (torch.cat(res_masks, dim=0),)