926 lines
45 KiB
Python
926 lines
45 KiB
Python
import sys
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import os
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cwd = os.getcwd()
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utils_dir = os.path.join(cwd, 'extensions', 'batch-face-swap', 'scripts')
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sys.path.extend([utils_dir])
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from bfs_utils import *
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from face_detect import *
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from sd_helpers import renderImg2Img, renderTxt2Img
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import modules.scripts as scripts
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import gradio as gr
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import time
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from modules import images, masking, generation_parameters_copypaste, script_callbacks
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from modules.processing import process_images, create_infotext, Processed
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from modules.shared import opts, cmd_opts, state
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from modules.sd_models import list_models
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import modules.shared as shared
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import modules.sd_samplers
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import cv2
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import numpy as np
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from PIL import Image, ImageOps, ImageDraw, ImageFilter, UnidentifiedImageError
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import math
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def apply_checkpoint(x):
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info = modules.sd_models.get_closet_checkpoint_match(x)
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if info is None:
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raise RuntimeError(f"Unknown checkpoint: {x}")
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modules.sd_models.reload_model_weights(shared.sd_model, info)
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def findFaces(
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facecfg,
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image,
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width,
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height,
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divider,
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onlyHorizontal,
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onlyVertical,
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file,
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totalNumberOfFaces,
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singleMaskPerImage,
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countFaces,
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maskSize,
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skip
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):
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rejected = 0
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masks = []
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faces_info = []
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imageOriginal = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
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heightOriginal = height
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widthOriginal = width
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# Calculate the size of each small image
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small_width = width if onlyHorizontal else math.ceil(width / divider)
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small_height = height if onlyVertical else math.ceil(height / divider)
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# Divide the large image into a list of small images
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small_images = []
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for i in range(0, height, small_height):
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for j in range(0, width, small_width):
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small_images.append(image.crop((j, i, j + small_width, i + small_height)))
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# Process each small image
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processed_images = []
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facesInImage = 0
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for i, small_image in enumerate(small_images):
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small_image_index = i
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small_image = cv2.cvtColor(np.array(small_image), cv2.COLOR_RGB2BGR)
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faces = []
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if facecfg.faceMode != FaceMode.ORIGINAL:
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# use OpenCV2 multi-scale face detector to find all the faces
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known_face_rects = []
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# first find the faces the old way, since OpenCV is BAD at faces near the camera
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# save the convex hulls, but also getting bounding boxes so OpenCV can skip those
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landmarks = getFacialLandmarks(small_image, facecfg)
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for landmark in landmarks:
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face_info = {}
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convexhull = cv2.convexHull(landmark)
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faces.append(convexhull)
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bounds = cv2.boundingRect(convexhull)
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known_face_rects.append(list(bounds)) # convert tuple to array for consistency
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faces_info.append(computeFaceInfo(landmark, onlyHorizontal, divider, small_width, small_height, small_image_index))
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faceRects = getFaceRectangles(small_image, known_face_rects, facecfg)
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for rect in faceRects:
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landmarkHull, face_info = getFacialLandmarkConvexHull(image, rect, onlyHorizontal, divider, small_width, small_height, small_image_index, facecfg)
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if landmarkHull is not None:
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faces.append(landmarkHull)
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faces_info.append(face_info)
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else:
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rejected += 1
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numberOfFaces = int(len(faces))
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totalNumberOfFaces += numberOfFaces
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if countFaces:
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continue
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else:
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landmarks = []
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landmarks = getFacialLandmarks(small_image, facecfg)
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numberOfFaces = int(len(landmarks))
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totalNumberOfFaces += numberOfFaces
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if countFaces:
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continue
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faces = []
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for landmark in landmarks:
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face_info = {}
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convexhull = cv2.convexHull(landmark)
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faces.append(convexhull)
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faces_info.append(computeFaceInfo(landmark, onlyHorizontal, divider, small_width, small_height, small_image_index))
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if len(faces) == 0:
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small_image[:] = (0, 0, 0)
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if numberOfFaces > 0:
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facesInImage += numberOfFaces
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if facesInImage == 0 and i == len(small_images) - 1:
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skip = 1
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mask = np.zeros((small_height, small_width), np.uint8)
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for i in range(len(faces)):
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small_image = cv2.fillConvexPoly(mask, faces[i], 255)
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processed_image = Image.fromarray(small_image)
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processed_images.append(processed_image)
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if countFaces:
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return totalNumberOfFaces
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if file != None:
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if FaceDetectDevelopment:
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print(f"Found {facesInImage} face(s) in {str(file)} (rejected {rejected} from OpenCV)")
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else:
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print(f"Found {facesInImage} face(s) in {str(file)}")
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# else:
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# print(f"Found {facesInImage} face(s)")
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# Create a new image with the same size as the original large image
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new_image = Image.new('RGB', (width, height))
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# Paste the processed small images into the new image
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if onlyHorizontal == True:
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for i, processed_image in enumerate(processed_images):
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x = (i // divider) * small_width
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y = (i % divider) * small_height
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new_image.paste(processed_image, (x, y))
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else:
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for i, processed_image in enumerate(processed_images):
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x = (i % divider) * small_width
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y = (i // divider) * small_height
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new_image.paste(processed_image, (x, y))
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image = cv2.cvtColor(np.array(new_image), cv2.COLOR_RGB2BGR)
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imageOriginal[:] = (0, 0, 0)
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imageOriginal[0:heightOriginal, 0:widthOriginal] = image[0:height, 0:width]
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# convert to grayscale
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imageOriginal = cv2.cvtColor(imageOriginal, cv2.COLOR_RGB2GRAY)
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# convert grayscale to binary
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thresh = 100
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imageOriginal = cv2.threshold(imageOriginal,thresh,255,cv2.THRESH_BINARY)[1]
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binary_image = cv2.convertScaleAbs(imageOriginal)
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try:
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kernel = np.ones((int(math.ceil(0.011*height)),int(math.ceil(0.011*height))),'uint8')
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dilated = cv2.dilate(binary_image,kernel,iterations=1)
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kernel = np.ones((int(math.ceil(0.0045*height)),int(math.ceil(0.0025*height))),'uint8')
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dilated = cv2.dilate(dilated,kernel,iterations=1,anchor=(1, -1))
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kernel = np.ones((int(math.ceil(0.014*height)),int(math.ceil(0.0025*height))),'uint8')
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dilated = cv2.dilate(dilated,kernel,iterations=1,anchor=(-1, 1))
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mask = dilated
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except cv2.error:
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mask = dilated
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if maskSize != 0:
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mask = maskResize(mask, maskSize, height)
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if not singleMaskPerImage:
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if facesInImage > 1:
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segmentFaces = True
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while (segmentFaces):
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currentBiggest = findBiggestFace(mask)
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masks.append(currentBiggest)
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mask = mask - currentBiggest
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whitePixels = cv2.countNonZero(mask)
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whitePixelThreshold = 0.0005 * (widthOriginal * heightOriginal)
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if (whitePixels < whitePixelThreshold):
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segmentFaces = False
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return masks, totalNumberOfFaces, faces_info, skip
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masks.append(mask)
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return masks, totalNumberOfFaces, faces_info, skip
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# generate debug image
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def faceDebug(
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p,
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masks,
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image,
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finishedImages,
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invertMask,
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forced_filename,
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output_path,
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info
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):
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generatedImages = []
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paste_to = []
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imageOriginal = image
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overlay_image = image
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for n, mask in enumerate(masks):
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mask = Image.fromarray(masks[n])
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if invertMask:
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mask = ImageOps.invert(mask)
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image_masked = Image.new('RGBa', (image.width, image.height))
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image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
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overlay_image = image_masked.convert('RGBA')
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debugsave(overlay_image)
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def faceSwap(
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p,
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masks,
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image,
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finishedImages,
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invertMask,
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forced_filename,
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output_path,
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info,
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selectedTab,
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mainTab,
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geninfo,
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faces_info,
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rotation_threshold,
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overrideDenoising,
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overrideMaskBlur,
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sd_model
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):
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original_model = modules.sd_models.select_checkpoint()
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apply_checkpoint(sd_model)
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batch_size = 1 if mainTab == "txt2img" else p.batch_size
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n_iter = 1 if mainTab == "txt2img" else p.n_iter
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denoising_strength = 0.5 #TODO add a slider for denoising strength
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mask_blur = int(math.ceil(0.01*image.height)) #TODO add a slider for mask blur
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inpaint_full_res_padding = 32 #TODO add a slider for padding
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inpainting_fill = 1
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p.do_not_save_samples = True
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index = 0
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generatedImages = []
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paste_to = []
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imageOriginal = image
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overlay_image = image
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for n, mask in enumerate(masks):
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rotate = False
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mask = Image.fromarray(masks[n])
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if invertMask:
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image_mask = ImageOps.invert(mask)
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else:
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image_masked = Image.new('RGBa', (image.width, image.height))
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image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
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overlay_image = image_masked.convert('RGBA')
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crop_region = masking.get_crop_region(np.array(mask), inpaint_full_res_padding)
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crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height)
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x1, y1, x2, y2 = crop_region
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paste_to.append((x1, y1, x2-x1, y2-y1))
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for i in range(len(faces_info)):
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try:
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pixel_color = mask.getpixel((faces_info[i]["center"][0],faces_info[i]["center"][1]))
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except IndexError:
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pixel_color = 0
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if pixel_color == 255:
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index = i
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break
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mask = mask.crop(crop_region)
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image_mask = images.resize_image(2, mask, p.width, p.height)
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image = image.crop(crop_region)
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image = images.resize_image(2, image, p.width, p.height)
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image_cropped = image
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rotation_threshold = rotation_threshold
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if 90+rotation_threshold > faces_info[index]["angle"] and 90-rotation_threshold < faces_info[index]["angle"]:
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pass
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else:
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angle_difference = (90-int(faces_info[index]["angle"]) + 360) % 360
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image = image.rotate(angle_difference, expand=True)
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image_mask = image_mask.rotate(angle_difference, expand=True)
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rotate = True
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if geninfo != "":
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p.prompt = str(geninfo.get("Prompt"))
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p.negative_prompt = str(geninfo.get("Negative prompt"))
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p.sampler_name = str(geninfo.get("Sampler"))
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p.cfg_scale = float(geninfo.get("CFG scale"))
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p.width = int(geninfo.get("Size-1"))
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p.height = int(geninfo.get("Size-2"))
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proc = renderImg2Img(
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p.prompt,
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p.negative_prompt,
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p.sampler_name,
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p.steps,
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p.cfg_scale,
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p.seed,
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p.width,
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p.height,
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image,
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image_mask,
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batch_size,
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n_iter,
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denoising_strength if overrideDenoising == True else p.denoising_strength, #TODO add a slider for denoising strength
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mask_blur if overrideMaskBlur else p.mask_blur, #TODO add a slider for mask blur
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inpainting_fill,
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1,
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inpaint_full_res_padding, #TODO add a slider for padding
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do_not_save_samples = True,
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)
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apply_checkpoint(original_model.title)
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if rotate:
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for i in range(len(proc.images)):
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image_copy = image_cropped.copy()
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proc.images[i] = proc.images[i].rotate(int(faces_info[index]["angle"])-90)
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w1, h1 = image_cropped.size
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w2, h2 = proc.images[i].size
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x = (w1 - w2) // 2
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y = (h1 - h2) // 2
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image_copy.paste(proc.images[i], (x, y))
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proc.images[i] = image_copy
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generatedImages.append(proc.images)
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image = imageOriginal
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for j in range(n_iter * batch_size):
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if not invertMask:
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image = imageOriginal
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for k in range(len(generatedImages)):
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mask = Image.fromarray(masks[k])
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mask = mask.filter(ImageFilter.GaussianBlur(mask_blur))
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image = apply_overlay(generatedImages[k][j], paste_to[k], image, mask)
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else:
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image = proc.images[j]
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info = infotext(p)
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final_forced_filename = forced_filename+"_"+str(j+1) if forced_filename != None and (batch_size > 1 or n_iter > 1) else forced_filename
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if opts.samples_format != "png" and image.mode != 'RGB':
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image = image.convert('RGB')
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images.save_image(image, output_path if output_path !="" else opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=final_forced_filename)
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finishedImages.append(image)
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p.do_not_save_samples = False
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return finishedImages
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def generateImages(p,
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facecfg,
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sd_model,
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input_image,
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input_path,
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searchSubdir,
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viewResults,
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divider,
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howSplit,
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saveMask,
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output_path,
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saveToOriginalFolder,
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onlyMask,
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saveNoFace,
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overrideDenoising,
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overrideMaskBlur,
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invertMask,
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singleMaskPerImage,
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countFaces,
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maskSize,
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keepOriginalName,
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pathExisting,
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pathMasksExisting,
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output_pathExisting,
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selectedTab,
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mainTab,
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loadGenParams,
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rotation_threshold):
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suffix = ''
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info = infotext(p)
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if selectedTab == "generateMasksTab":
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finishedImages = []
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wasCountFaces = False
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totalNumberOfFaces = 0
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allFiles = []
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geninfo = ""
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onlyHorizontal = ("Horizontal" in howSplit)
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onlyVertical = ("Vertical" in howSplit)
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# if neither path nor image, we're done
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if input_path == '' and input_image is None:
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return finishedImages
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# flag whether we're processing a directory or a specified image
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# (the code after this supports multiple images in an array, but the UI only allows a single image)
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usingFilenames = (input_path != '')
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if usingFilenames:
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allFiles = listFiles(input_path, searchSubdir, allFiles)
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else:
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allFiles += input_image
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start_time = time.thread_time()
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if countFaces:
|
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print("\nCounting faces...")
|
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for i, file in enumerate(allFiles):
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skip = 0
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image = Image.open(file) if usingFilenames else file
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width, height = image.size
|
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masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskSize, skip)
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|
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if not onlyMask and countFaces:
|
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print(f"\nWill process {len(allFiles)} images, found {totalNumberOfFaces} faces, generating {p.n_iter * p.batch_size} new images for each.")
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state.job_count = totalNumberOfFaces * p.n_iter
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elif not onlyMask and not countFaces:
|
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print(f"\nWill process {len(allFiles)} images, generating {p.n_iter * p.batch_size} new images for each.")
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state.job_count = len(allFiles) * p.n_iter
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|
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for i, file in enumerate(allFiles):
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if usingFilenames and keepOriginalName:
|
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forced_filename = os.path.splitext(os.path.basename(file))[0]
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else:
|
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forced_filename = None
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|
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if usingFilenames and saveToOriginalFolder:
|
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output_path = os.path.dirname(file)
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|
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if countFaces:
|
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state.job = f"{i+1} out of {totalNumberOfFaces}"
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totalNumberOfFaces = 0
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wasCountFaces = True
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countFaces = False
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else:
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state.job = f"{i+1} out of {len(allFiles)}"
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|
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if state.skipped:
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state.skipped = False
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if state.interrupted and onlyMask:
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state.interrupted = False
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elif state.interrupted:
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break
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try:
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image = Image.open(file) if usingFilenames else file
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width, height = image.size
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|
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if loadGenParams:
|
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geninfo, _ = read_info_from_image(image)
|
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geninfo = generation_parameters_copypaste.parse_generation_parameters(geninfo)
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|
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except UnidentifiedImageError:
|
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print(f"\nUnable to open {file}, skipping")
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continue
|
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|
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skip = 0
|
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masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskSize, skip)
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|
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if facecfg.debugSave:
|
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faceDebug(p, masks, image, finishedImages, invertMask, forced_filename, output_path, info)
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|
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# Only generate mask
|
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if onlyMask:
|
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suffix = '_mask'
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|
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# Load mask
|
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for i, mask in enumerate(masks):
|
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mask = Image.fromarray(mask)
|
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|
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# Invert mask if needed
|
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if invertMask:
|
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mask = ImageOps.invert(mask)
|
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finishedImages.append(mask)
|
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|
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if saveMask and skip != 1:
|
|
custom_save_image(p, mask, output_path, forced_filename, suffix, info)
|
|
elif saveMask and skip == 1 and saveNoFace:
|
|
custom_save_image(p, mask, output_path, forced_filename, suffix, info)
|
|
|
|
# If face was not found but user wants to save images without face
|
|
if skip == 1 and saveNoFace and not onlyMask:
|
|
custom_save_image(p, image, output_path, forced_filename, suffix, info)
|
|
|
|
finishedImages.append(image)
|
|
state.skipped = True
|
|
continue
|
|
|
|
# If face was not found, just skip
|
|
if skip == 1:
|
|
state.skipped = True
|
|
continue
|
|
|
|
if not onlyMask:
|
|
finishedImages = faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, output_path, info, selectedTab, mainTab, geninfo, faces_info, rotation_threshold, overrideDenoising, overrideMaskBlur, sd_model)
|
|
|
|
if usingFilenames and not viewResults:
|
|
finishedImages = []
|
|
|
|
if wasCountFaces == True:
|
|
countFaces = True
|
|
|
|
timing = time.thread_time() - start_time
|
|
print(f"Found {totalNumberOfFaces} faces in {len(allFiles)} images in {timing} seconds.")
|
|
|
|
# EXISTING MASKS
|
|
elif selectedTab == "existingMasksTab":
|
|
finishedImages = []
|
|
allImages = []
|
|
allMasks = []
|
|
searchSubdir = False
|
|
|
|
if pathExisting != '' and pathMasksExisting != '':
|
|
allImages = listFiles(pathExisting, searchSubdir, allImages)
|
|
allMasks = listFiles(pathMasksExisting, searchSubdir, allMasks)
|
|
|
|
print(f"\nWill process {len(allImages)} images, generating {p.n_iter * p.batch_size} new images for each.")
|
|
state.job_count = len(allImages) * p.n_iter
|
|
for i, file in enumerate(allImages):
|
|
forced_filename = os.path.splitext(os.path.basename(file))[0]
|
|
|
|
state.job = f"{i+1} out of {len(allImages)}"
|
|
|
|
if state.skipped:
|
|
state.skipped = False
|
|
elif state.interrupted:
|
|
break
|
|
|
|
try:
|
|
image = Image.open(file)
|
|
width, height = image.size
|
|
|
|
masks = []
|
|
masks.append(Image.open(os.path.join(pathMasksExisting, os.path.splitext(os.path.basename(file))[0])+os.path.splitext(allMasks[i])[1]))
|
|
except UnidentifiedImageError:
|
|
print(f"\nUnable to open {file}, skipping")
|
|
continue
|
|
|
|
|
|
finishedImages = faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, output_pathExisting, info, selectedTab, mainTab, faces_info, rotation_threshold, overrideDenoising, overrideMaskBlur, sd_model)
|
|
|
|
if not viewResults:
|
|
finishedImages = []
|
|
|
|
return finishedImages
|
|
|
|
class Script(scripts.Script):
|
|
def title(self):
|
|
return "Batch Face Swap"
|
|
|
|
def show(self, is_img2img):
|
|
return scripts.AlwaysVisible
|
|
|
|
def ui(self, is_img2img):
|
|
# available_samplers = [s.name for s in modules.sd_samplers.samplers if "UniPc" not in s.name]
|
|
with gr.Accordion("Batch Face Swap", open = False, elem_id="batch_face_swap"):
|
|
with gr.Row():
|
|
enabled = gr.Checkbox(label='Enable', value=False)
|
|
available_models = modules.sd_models.checkpoint_tiles()
|
|
sd_model = gr.Dropdown(label="SD Model", choices=available_models, value=shared.sd_model.sd_checkpoint_info.title, type="value", interactive=True)
|
|
modules.ui.create_refresh_button(sd_model, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_sd_checkpoint")
|
|
|
|
with gr.Accordion("Settings", open = False):
|
|
def updateVisualizer(searchSubdir: bool, howSplit: str, divider: int, maskSize: int, input_path: str, visualizationOpacity: int, faceMode: int):
|
|
facecfg = FaceDetectConfig(faceMode) # this is a huge pain to patch through so don't bother
|
|
allFiles = []
|
|
totalNumberOfFaces = 0
|
|
|
|
usingFilenames = (input_path != '')
|
|
if usingFilenames:
|
|
allFiles = listFiles(input_path, searchSubdir, allFiles)
|
|
|
|
if len(allFiles) > 0:
|
|
file = allFiles[0]
|
|
try:
|
|
image = Image.open(file)
|
|
maxsize = (1000, 500)
|
|
image.thumbnail(maxsize,Image.ANTIALIAS)
|
|
except (UnidentifiedImageError, AttributeError):
|
|
allFiles = []
|
|
|
|
visualizationOpacity = (visualizationOpacity/100)*255
|
|
color = "white"
|
|
thickness = 5
|
|
|
|
if "Both" in howSplit:
|
|
onlyHorizontal = False
|
|
onlyVertical = False
|
|
if len(allFiles) == 0:
|
|
image = Image.open("./extensions/batch-face-swap/images/exampleB.jpg")
|
|
width, height = image.size
|
|
|
|
# if len(masks)==0 and path != '':
|
|
masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskSize=maskSize, skip=0)
|
|
|
|
mask = masks[0]
|
|
|
|
mask = maskResize(mask, maskSize, height)
|
|
|
|
mask = Image.fromarray(mask)
|
|
redImage = Image.new("RGB", (width, height), (255, 0, 0))
|
|
|
|
mask.convert("L")
|
|
draw = ImageDraw.Draw(mask, "L")
|
|
|
|
if divider > 1:
|
|
for i in range(divider-1):
|
|
start_point = (0, int((height/divider)*(i+1)))
|
|
end_point = (int(width), int((height/divider)*(i+1)))
|
|
draw.line([start_point, end_point], fill=color, width=thickness)
|
|
|
|
for i in range(divider-1):
|
|
start_point = (int((width/divider)*(i+1)), 0)
|
|
end_point = (int((width/divider)*(i+1)), int(height))
|
|
draw.line([start_point, end_point], fill=color, width=thickness)
|
|
|
|
image = composite(redImage, image, mask, visualizationOpacity)
|
|
|
|
elif "Vertical" in howSplit:
|
|
onlyHorizontal = False
|
|
onlyVertical = True
|
|
if len(allFiles) == 0:
|
|
image = Image.open("./extensions/batch-face-swap/images/exampleV.jpg")
|
|
# mask = Image.open("./extensions/batch-face-swap/images/exampleV_mask.jpg")
|
|
# mask = np.array(mask)
|
|
width, height = image.size
|
|
|
|
# if len(masks)==0 and path != '':
|
|
masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskSize=maskSize, skip=0)
|
|
|
|
mask = masks[0]
|
|
|
|
mask = maskResize(mask, maskSize, height)
|
|
|
|
mask = Image.fromarray(mask)
|
|
redImage = Image.new("RGB", (width, height), (255, 0, 0))
|
|
|
|
mask.convert("L")
|
|
draw = ImageDraw.Draw(mask, "L")
|
|
|
|
if divider > 1:
|
|
for i in range(divider-1):
|
|
start_point = (int((width/divider)*(i+1)), 0)
|
|
end_point = (int((width/divider)*(i+1)), int(height))
|
|
draw.line([start_point, end_point], fill=color, width=thickness)
|
|
|
|
image = composite(redImage, image, mask, visualizationOpacity)
|
|
|
|
else:
|
|
onlyHorizontal = True
|
|
onlyVertical = False
|
|
if len(allFiles) == 0:
|
|
image = Image.open("./extensions/batch-face-swap/images/exampleH.jpg")
|
|
width, height = image.size
|
|
|
|
# if len(masks)==0 and path != '':
|
|
masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskSize=maskSize, skip=0)
|
|
|
|
mask = masks[0]
|
|
|
|
mask = maskResize(mask, maskSize, height)
|
|
|
|
mask = Image.fromarray(mask)
|
|
redImage = Image.new("RGB", (width, height), (255, 0, 0))
|
|
|
|
mask.convert("L")
|
|
draw = ImageDraw.Draw(mask, "L")
|
|
|
|
if divider > 1:
|
|
for i in range(divider-1):
|
|
start_point = (0, int((height/divider)*(i+1)))
|
|
end_point = (int(width), int((height/divider)*(i+1)))
|
|
draw.line([start_point, end_point], fill=color, width=thickness)
|
|
|
|
image = composite(redImage, image, mask, visualizationOpacity)
|
|
|
|
update = gr.Image.update(value=image)
|
|
return update
|
|
|
|
def switchSaveMaskInteractivity(onlyMask: bool):
|
|
return gr.Checkbox.update(interactive=bool(onlyMask))
|
|
def switchSaveMask(onlyMask: bool):
|
|
if onlyMask == False:
|
|
return gr.Checkbox.update(value=bool(onlyMask))
|
|
def switchTipsVisibility(showTips: bool):
|
|
return gr.HTML.update(visible=bool(showTips))
|
|
def switchInvertMask(invertMask: bool):
|
|
return gr.Checkbox.update(value=bool(invertMask))
|
|
|
|
if is_img2img:
|
|
# Path to images
|
|
gr.HTML("<p style=\"margin-top:0.10em;font-size:1.5em\"><strong>Input:</strong></p>")
|
|
with gr.Column(variant='panel'):
|
|
htmlTip1 = gr.HTML("<p>'Load from subdirectories' will include all images in all subdirectories.</p>",visible=False)
|
|
with gr.Row():
|
|
input_path = gr.Textbox(label="Images directory",placeholder=r"C:\Users\dude\Desktop\images", visible=True)
|
|
output_path = gr.Textbox(label="Output directory (OPTIONAL)",placeholder=r"Leave empty to save to default directory")
|
|
with gr.Row():
|
|
searchSubdir = gr.Checkbox(value=False, label="Load from subdirectories")
|
|
saveToOriginalFolder = gr.Checkbox(value=False, label="Save to original folder")
|
|
keepOriginalName = gr.Checkbox(value=False, label="Keep original file name (OVERWRITES FILES WITH THE SAME NAME)")
|
|
loadGenParams = gr.Checkbox(value=False, label="Load generation parameters from images")
|
|
|
|
else:
|
|
htmlTip1 = gr.HTML("<p></p>",visible=False)
|
|
input_path = gr.Textbox(label="Images directory", visible=False)
|
|
output_path = gr.Textbox(label="Output directory (OPTIONAL)", visible=False)
|
|
searchSubdir = gr.Checkbox(value=False, label="Load from subdirectories", visible=False)
|
|
saveToOriginalFolder = gr.Checkbox(value=False, label="Save to original folder", visible=False)
|
|
keepOriginalName = gr.Checkbox(value=False, label="Keep original file name (OVERWRITES FILES WITH THE SAME NAME)", visible=False)
|
|
loadGenParams = gr.Checkbox(value=False, label="Load generation parameters from images", visible=False)
|
|
with gr.Box():
|
|
# Overrides
|
|
with gr.Column(variant='panel'):
|
|
gr.HTML("<p style=\"margin-top:0.75em;font-size:1.25em\">Overrides:</p>")
|
|
with gr.Row():
|
|
overrideDenoising = gr.Checkbox(value=True, label="""Override "Denoising strength" to 0.5""")
|
|
overrideMaskBlur = gr.Checkbox(value=True, label="""Override "Mask blur" to automatic""")
|
|
|
|
with gr.Column(variant='panel'):
|
|
with gr.Tab("Generate masks") as generateMasksTab:
|
|
# Face detection
|
|
with gr.Column(variant='compact'):
|
|
gr.HTML("<p style=\"margin-top:0.10em;margin-bottom:0.75em;font-size:1.5em\"><strong>Face detection:</strong></p>")
|
|
with gr.Row():
|
|
faceDetectMode = gr.Dropdown(label="Detector", choices=face_mode_names, value=face_mode_names[FaceMode.DEFAULT], type="index", elem_id="z_type")
|
|
minFace = gr.Slider(minimum=10, maximum=200, step=1 , value=30, label="Minimum face size in pixels")
|
|
|
|
with gr.Column(variant='panel'):
|
|
htmlTip2 = gr.HTML("<p>Activate the 'Masks only' checkbox to see how many faces do your current settings detect without generating SD image. (check console)</p><p>You can also save generated masks to disk. Only possible with 'Masks only' (if you leave path empty, it will save the masks to your default webui outputs directory)</p><p>'Single mask per image' is only recommended with 'Invert mask' or if you want to save one mask per image, not per face. If you activate it without inverting mask, and try to process an image with multiple faces, it will generate only one image for all faces, producing bad results.</p><p>'Rotation threshold', if the face is rotated at an angle higher than this value, it will be automatically rotated so it's upright before generating, producing much better results.</p>",visible=False)
|
|
# Settings
|
|
with gr.Column(variant='panel'):
|
|
gr.HTML("<p style=\"margin-top:0.10em;font-size:1.5em\">Settings:</p>")
|
|
with gr.Column(variant='compact'):
|
|
with gr.Row():
|
|
onlyMask = gr.Checkbox(value=False, label="Masks only", visible=True)
|
|
saveMask = gr.Checkbox(value=False, label="Save masks to disk", interactive=False)
|
|
with gr.Row():
|
|
invertMask = gr.Checkbox(value=False, label="Invert mask", visible=True)
|
|
singleMaskPerImage = gr.Checkbox(value=False, label="Single mask per image", visible=True)
|
|
with gr.Row(variant='panel'):
|
|
rotation_threshold = gr.Slider(minimum=0, maximum=180, step=1, value=20, label="Rotation threshold")
|
|
|
|
# Image splitter
|
|
with gr.Column(variant='panel'):
|
|
gr.HTML("<p style=\"margin-top:0.10em;font-size:1.5em\"><strong>Image splitter:</strong></p>")
|
|
with gr.Column(variant='panel'):
|
|
htmlTip3 = gr.HTML("<p>This divides image to smaller images and tries to find a face in the individual smaller images.</p><p>Useful when faces are small in relation to the size of the whole picture and are not being detected.</p><p>(may result in mask that only covers a part of a face or no detection if the division goes right through the face)</p><p>Open 'Split visualizer' to see how it works.</p>",visible=False)
|
|
with gr.Row():
|
|
divider = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="How many images to divide into")
|
|
maskSize = gr.Slider(minimum=-10, maximum=10, step=1, value=0, label="Mask size")
|
|
howSplit = gr.Radio(["Horizontal only ▤", "Vertical only ▥", "Both ▦"], value = "Both ▦", label = "How to divide")
|
|
with gr.Accordion(label="Visualizer", open=False):
|
|
exampleImage = gr.Image(value=Image.open("./extensions/batch-face-swap/images/exampleB.jpg"), label="Split visualizer", show_label=False, type="pil", visible=True).style(height=500)
|
|
with gr.Row(variant='compact'):
|
|
with gr.Column(variant='panel'):
|
|
gr.HTML("", visible=False)
|
|
with gr.Column(variant='compact'):
|
|
visualizationOpacity = gr.Slider(minimum=0, maximum=100, step=1, value=75, label="Opacity")
|
|
|
|
# Other
|
|
with gr.Column(variant='panel'):
|
|
gr.HTML("<p style=\"margin-top:0.10em;font-size:1.5em\">Other:</p>")
|
|
with gr.Column(variant='panel'):
|
|
htmlTip4 = gr.HTML("<p>'Count faces before generating' is required to see accurate progress bar (not recommended when processing a large number of images). Because without knowing the number of faces, the webui can't know how many images it will generate. Activating it means you will search for faces twice.</p>",visible=False)
|
|
saveNoFace = gr.Checkbox(value=True, label="Save image even if face was not found")
|
|
countFaces = gr.Checkbox(value=False, label="Count faces before generating (accurate progress bar but NOT recommended)")
|
|
|
|
with gr.Tab("Existing masks",) as existingMasksTab:
|
|
with gr.Column(variant='panel'):
|
|
htmlTip5 = gr.HTML("<p style=\"margin-bottom:0.75em\">Image name and it's corresponding mask must have exactly the same name (if image is called `abc.jpg` then it's mask must also be called `abc.jpg`)</p>",visible=False)
|
|
pathExisting = gr.Textbox(label="Images directory",placeholder=r"C:\Users\dude\Desktop\images")
|
|
pathMasksExisting = gr.Textbox(label="Masks directory",placeholder=r"C:\Users\dude\Desktop\masks")
|
|
output_pathExisting = gr.Textbox(label="Output directory (OPTIONAL)",placeholder=r"Leave empty to save to default directory")
|
|
|
|
# General
|
|
with gr.Box():
|
|
with gr.Column(variant='panel'):
|
|
gr.HTML("<p style=\"margin-top:0.10em;font-size:1.5em\">General:</p>")
|
|
htmlTip6 = gr.HTML("<p>Activate 'Show results in WebUI' checkbox to see results in the WebUI at the end (not recommended when processing a large number of images)</p>",visible=False)
|
|
with gr.Row():
|
|
viewResults = gr.Checkbox(value=True, label="Show results in WebUI")
|
|
showTips = gr.Checkbox(value=False, label="Show tips")
|
|
|
|
# Face detect internals
|
|
with gr.Column(variant='panel', visible = FaceDetectDevelopment):
|
|
gr.HTML("<p style=\"margin-top:0.75em;margin-bottom:0.5em;font-size:1.5em\"><strong>Debug internal config:</strong></p>")
|
|
with gr.Column(variant='panel'):
|
|
with gr.Row():
|
|
debugSave = gr.Checkbox(value=False, label="Save debug images")
|
|
optimizeDetect= gr.Checkbox(value=True, label="Used optimized detector")
|
|
face_x_scale = gr.Slider(minimum=1 , maximum= 6, step=0.1, value=4, label="Face x-scaleX")
|
|
face_y_scale = gr.Slider(minimum=1 , maximum= 6, step=0.1, value=2.5, label="Face y-scaleX")
|
|
|
|
multiScale = gr.Slider(minimum=1.0, maximum=200, step=0.001, value=1.03, label="Multiscale search stepsizess")
|
|
multiScale2 = gr.Slider(minimum=0.8, maximum=200, step=0.001, value=1.0 , label="Multiscale search secondary scalar")
|
|
multiScale3 = gr.Slider(minimum=0.8, maximum=2.0, step=0.001, value=1.0 , label="Multiscale search tertiary scale")
|
|
|
|
minNeighbors = gr.Slider(minimum=1 , maximum = 10, step=1 , value=5, label="minNeighbors")
|
|
mpconfidence = gr.Slider(minimum=0.01, maximum = 2.0, step=0.01, value=0.5, label="FaceMesh confidence threshold")
|
|
mpcount = gr.Slider(minimum=1, maximum = 20, step=1, value=5, label="FaceMesh maximum faces")
|
|
|
|
mainTab = gr.Textbox(value=f"""{"img2img" if is_img2img else "txt2img"}""", visible=False)
|
|
selectedTab = gr.Textbox(value="generateMasksTab", visible=False)
|
|
generateMasksTab.select(lambda: "generateMasksTab", inputs=None, outputs=selectedTab)
|
|
existingMasksTab.select(lambda: "existingMasksTab", inputs=None, outputs=selectedTab)
|
|
|
|
onlyMask.change(switchSaveMaskInteractivity, onlyMask, saveMask)
|
|
onlyMask.change(switchSaveMask, onlyMask, saveMask)
|
|
invertMask.change(switchInvertMask, invertMask, singleMaskPerImage)
|
|
|
|
faceDetectMode.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
minFace.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
visualizationOpacity.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
searchSubdir.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
howSplit.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
divider.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
maskSize.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
input_path.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, input_path, visualizationOpacity, faceDetectMode], exampleImage)
|
|
|
|
showTips.change(switchTipsVisibility, showTips, htmlTip1)
|
|
showTips.change(switchTipsVisibility, showTips, htmlTip2)
|
|
showTips.change(switchTipsVisibility, showTips, htmlTip3)
|
|
showTips.change(switchTipsVisibility, showTips, htmlTip4)
|
|
showTips.change(switchTipsVisibility, showTips, htmlTip5)
|
|
showTips.change(switchTipsVisibility, showTips, htmlTip6)
|
|
|
|
return [enabled, sd_model, mainTab, overrideDenoising, overrideMaskBlur, input_path, searchSubdir, divider, howSplit, saveMask, output_path, saveToOriginalFolder, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect, loadGenParams, rotation_threshold]
|
|
|
|
def process(self, p, enabled, sd_model, mainTab, overrideDenoising, overrideMaskBlur, input_path, searchSubdir, divider, howSplit, saveMask, output_path, saveToOriginalFolder, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect, loadGenParams, rotation_threshold):
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if enabled and mainTab == "img2img":
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global all_images
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wasGrid = p.do_not_save_grid
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p.do_not_save_grid = True
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wasInpaintFullRes = p.inpaint_full_res
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p.inpaint_full_res = 1
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all_images = []
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facecfg = FaceDetectConfig(faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect)
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if input_path == '':
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input_image = [ p.init_images[0] ]
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finishedImages = generateImages(p, facecfg, sd_model, input_image, input_path, searchSubdir, viewResults, int(divider), howSplit, saveMask, output_path, saveToOriginalFolder, onlyMask, saveNoFace, overrideDenoising, overrideMaskBlur, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, mainTab, loadGenParams, rotation_threshold)
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if not viewResults:
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finishedImages = []
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all_images += finishedImages
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proc = Processed(p, all_images)
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p.do_not_save_grid = wasGrid
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p.inpaint_full_res = wasInpaintFullRes
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p.batch_size = 0
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p.n_iter = 0
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return proc
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else:
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pass
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def postprocess(self, p, processed, enabled, sd_model, mainTab, overrideDenoising, overrideMaskBlur, input_path, searchSubdir, divider, howSplit, saveMask, output_path, saveToOriginalFolder, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect, loadGenParams, rotation_threshold):
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if enabled and mainTab == "txt2img":
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global all_images
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wasGrid = p.do_not_save_grid
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p.do_not_save_grid = True
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all_images = []
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facecfg = FaceDetectConfig(faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect)
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if input_path == '':
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input_image = []
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input_image += processed.images
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finishedImages = generateImages(p, facecfg, sd_model, input_image, input_path, searchSubdir, viewResults, int(divider), howSplit, saveMask, output_path, saveToOriginalFolder, onlyMask, saveNoFace, overrideDenoising, overrideMaskBlur, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, mainTab, loadGenParams, rotation_threshold)
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if not viewResults:
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finishedImages = []
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all_images += finishedImages
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p.do_not_save_grid = wasGrid
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processed.images = all_images
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elif enabled and mainTab == "img2img":
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processed.images = all_images
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else:
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pass
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