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Commented out face detectio node and requirements

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wallish77
2023-08-06 12:09:35 -05:00
parent e56bbe60df
commit e9f1873524
2 changed files with 36 additions and 35 deletions
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requirements.txt
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@@ -0,0 +1 @@
piexif
wlsh_nodes.py
+35 -35
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@@ -13,8 +13,8 @@ from PIL import Image, ImageOps, ImageFilter, ImageDraw
from PIL.PngImagePlugin import PngInfo
import numpy as np
from torchvision.transforms import ToPILImage
import cv2
from deepface import DeepFace
#import cv2
#from deepface import DeepFace
import re
import latent_preview
@@ -650,44 +650,44 @@ class WLSH_Generate_Edge_Mask:
mask2 = torch.from_numpy(mask2)[None,]
return (mask2,)
class WLSH_Generate_Face_Mask:
detectors = ["opencv", "retinaface", "ssd", "mtcnn"]
channels = ["red", "blue", "green"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",),
"detector": (s.detectors,),
"channel": (s.channels,),
"mask_padding": ("INT",{"default": 6, "min": 0, "max": 32, "step": 2})
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "gen_face_mask"
# class WLSH_Generate_Face_Mask:
# detectors = ["opencv", "retinaface", "ssd", "mtcnn"]
# channels = ["red", "blue", "green"]
# @classmethod
# def INPUT_TYPES(s):
# return {"required": { "image": ("IMAGE",),
# "detector": (s.detectors,),
# "channel": (s.channels,),
# "mask_padding": ("INT",{"default": 6, "min": 0, "max": 32, "step": 2})
# }}
# RETURN_TYPES = ("IMAGE",)
# FUNCTION = "gen_face_mask"
CATEGORY = "WLSH Nodes/inpainting"
def gen_face_mask(self, image, mask_padding, detector, channel):
image = tensor2pil(image)
# CATEGORY = "WLSH Nodes/inpainting"
# def gen_face_mask(self, image, mask_padding, detector, channel):
# image = tensor2pil(image)
faces = DeepFace.extract_faces(np.array(image),detector_backend=detector)
# cv_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# # Convert to grayscale
# gray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
# faces = DeepFace.extract_faces(np.array(image),detector_backend=detector)
# # cv_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# # # Convert to grayscale
# # gray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
# # Detect faces in the image
# face_cascade = cv2.CascadeClassifier('custom_nodes/haarcascade_frontalface_default.xml')
# faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
mask = Image.new('RGB',image.size)
# # # Detect faces in the image
# # face_cascade = cv2.CascadeClassifier('custom_nodes/haarcascade_frontalface_default.xml')
# # faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
# mask = Image.new('RGB',image.size)
# Draw a rectangle on the PIL Image object
colors = {"red": "RGB(255,0,0)", "green": "RGB(0,255,0)", "blue": "RGB(0,0,255)"}
draw = ImageDraw.Draw(mask)
for face in faces:
x,y,w,h = face['facial_area'].values()
draw.rectangle((x-mask_padding,y-mask_padding,x+w+mask_padding,y+h+mask_padding), outline=colors[channel], fill=colors[channel])
mask = mask.filter(ImageFilter.GaussianBlur(radius=6))
# # Draw a rectangle on the PIL Image object
# colors = {"red": "RGB(255,0,0)", "green": "RGB(0,255,0)", "blue": "RGB(0,0,255)"}
# draw = ImageDraw.Draw(mask)
# for face in faces:
# x,y,w,h = face['facial_area'].values()
# draw.rectangle((x-mask_padding,y-mask_padding,x+w+mask_padding,y+h+mask_padding), outline=colors[channel], fill=colors[channel])
# mask = mask.filter(ImageFilter.GaussianBlur(radius=6))
mask = np.array(mask).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)[None,]
return (mask,)
# mask = np.array(mask).astype(np.float32) / 255.0
# mask = torch.from_numpy(mask)[None,]
# return (mask,)
# image I/O
class WLSH_Image_Save_With_Prompt_Info: