Merge pull request #14121 from AUTOMATIC1111/fix-Auto-focal-point-crop-for-opencv-4.8.x (9eadc4f1) · Commits · github_fork / Stable Diffusion Webui

modules/textual_inversion/autocrop.py

+122 −117

Original line number	Original line	Diff line number	Diff line
	@@ -3,6 +3,8 @@ import requests
	import os		import os
	import numpy as np		import numpy as np
	from PIL import ImageDraw		from PIL import ImageDraw
			from modules import paths_internal
			from pkg_resources import parse_version

	GREEN = "#0F0"		GREEN = "#0F0"
	BLUE = "#00F"		BLUE = "#00F"
	@@ -25,7 +27,6 @@ def crop_image(im, settings):
	elif is_portrait(settings.crop_width, settings.crop_height):		elif is_portrait(settings.crop_width, settings.crop_height):
	scale_by = settings.crop_height / im.height		scale_by = settings.crop_height / im.height


	im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))		im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
	im_debug = im.copy()		im_debug = im.copy()

	@@ -69,6 +70,7 @@ def crop_image(im, settings):

	return results		return results


	def focal_point(im, settings):		def focal_point(im, settings):
	corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []		corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
	entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []		entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
	@@ -183,13 +185,15 @@ def image_face_points(im, settings):
	minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side		minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
	try:		try:
	faces = classifier.detectMultiScale(gray, scaleFactor=1.1,		faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
	minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)		minNeighbors=7, minSize=(minsize, minsize),
			flags=cv2.CASCADE_SCALE_IMAGE)
	except Exception:		except Exception:
	continue		continue

	if faces:		if faces:
	rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]		rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
	return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]		return [PointOfInterest((r[0] + r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0] - r[2]),
			weight=1 / len(rects)) for r in rects]
	return []		return []


	@@ -294,22 +298,23 @@ def is_square(w, h):
	return w == h		return w == h


	def download_and_cache_models(dirname):		model_dir_opencv = os.path.join(paths_internal.models_path, 'opencv')
	download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'		if parse_version(cv2.__version__) >= parse_version('4.8'):
	model_file_name = 'face_detection_yunet.onnx'		model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet_2023mar.onnx')
			model_url = 'https://github.com/opencv/opencv_zoo/blob/b6e370b10f641879a87890d44e42173077154a05/models/face_detection_yunet/face_detection_yunet_2023mar.onnx?raw=true'
			else:
			model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet.onnx')
			model_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'

	os.makedirs(dirname, exist_ok=True)

	cache_file = os.path.join(dirname, model_file_name)		def download_and_cache_models():
	if not os.path.exists(cache_file):		if not os.path.exists(model_file_path):
	print(f"downloading face detection model from '{download_url}' to '{cache_file}'")		os.makedirs(model_dir_opencv, exist_ok=True)
	response = requests.get(download_url)		print(f"downloading face detection model from '{model_url}' to '{model_file_path}'")
	with open(cache_file, "wb") as f:		response = requests.get(model_url)
			with open(model_file_path, "wb") as f:
	f.write(response.content)		f.write(response.content)
			return model_file_path
	if os.path.exists(cache_file):
	return cache_file
	return None


	class PointOfInterest:		class PointOfInterest:

modules/textual_inversion/preprocess.py

+2 −2

Original line number	Original line	Diff line number	Diff line
	@@ -3,7 +3,7 @@ from PIL import Image, ImageOps
	import math		import math
	import tqdm		import tqdm

	from modules import paths, shared, images, deepbooru		from modules import shared, images, deepbooru
	from modules.textual_inversion import autocrop		from modules.textual_inversion import autocrop


	@@ -196,7 +196,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre

	dnn_model_path = None		dnn_model_path = None
	try:		try:
	dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))		dnn_model_path = autocrop.download_and_cache_models()
	except Exception as e:		except Exception as e:
	print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)		print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)

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