Merge pull request #14121 from AUTOMATIC1111/fix-Auto-focal-point-crop-for-opencv-4.8.x

import os

import numpy as np

from PIL import ImageDraw

from modules import paths_internal

from pkg_resources import parse_version

GREEN = "#0F0"

BLUE = "#00F"

        elif is_portrait(settings.crop_width, settings.crop_height):

            scale_by = settings.crop_height / im.height

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

    im_debug = im.copy()

    return results

def focal_point(im, settings):

    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 []

            minsize = int(min(im.width, im.height) * t[1])  # at least N percent of the smallest side

            try:

                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:

                continue

            if 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 w == h

def download_and_cache_models(dirname):

    download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'

    model_file_name = 'face_detection_yunet.onnx'

model_dir_opencv = os.path.join(paths_internal.models_path, 'opencv')

if parse_version(cv2.__version__) >= parse_version('4.8'):

    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)

    if not os.path.exists(cache_file):

        print(f"downloading face detection model from '{download_url}' to '{cache_file}'")

        response = requests.get(download_url)

        with open(cache_file, "wb") as f:

def download_and_cache_models():

    if not os.path.exists(model_file_path):

        os.makedirs(model_dir_opencv, exist_ok=True)

        print(f"downloading face detection model from '{model_url}' to '{model_file_path}'")

        response = requests.get(model_url)

        with open(model_file_path, "wb") as f:

            f.write(response.content)

    if os.path.exists(cache_file):

        return cache_file

    return None

    return model_file_path

class PointOfInterest:

import math

import tqdm

from modules import paths, shared, images, deepbooru

from modules import shared, images, deepbooru

from modules.textual_inversion import autocrop

            dnn_model_path = None

            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:

                print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)