modules/textual_inversion/autocrop.py

import cv2
import requests
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"
RED = "#F00"


def crop_image(im, settings):
    """ Intelligently crop an image to the subject matter """

    scale_by = 1
    if is_landscape(im.width, im.height):
        scale_by = settings.crop_height / im.height
    elif is_portrait(im.width, im.height):
        scale_by = settings.crop_width / im.width
    elif is_square(im.width, im.height):
        if is_square(settings.crop_width, settings.crop_height):
            scale_by = settings.crop_width / im.width
        elif is_landscape(settings.crop_width, settings.crop_height):
            scale_by = settings.crop_width / im.width
        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()

    focus = focal_point(im_debug, settings)

    # take the focal point and turn it into crop coordinates that try to center over the focal
    # point but then get adjusted back into the frame
    y_half = int(settings.crop_height / 2)
    x_half = int(settings.crop_width / 2)

    x1 = focus.x - x_half
    if x1 < 0:
        x1 = 0
    elif x1 + settings.crop_width > im.width:
        x1 = im.width - settings.crop_width

    y1 = focus.y - y_half
    if y1 < 0:
        y1 = 0
    elif y1 + settings.crop_height > im.height:
        y1 = im.height - settings.crop_height

    x2 = x1 + settings.crop_width
    y2 = y1 + settings.crop_height

    crop = [x1, y1, x2, y2]

    results = []

    results.append(im.crop(tuple(crop)))

    if settings.annotate_image:
        d = ImageDraw.Draw(im_debug)
        rect = list(crop)
        rect[2] -= 1
        rect[3] -= 1
        d.rectangle(rect, outline=GREEN)
        results.append(im_debug)
        if settings.desktop_view_image:
            im_debug.show()

    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 []
    face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []

    pois = []

    weight_pref_total = 0
    if corner_points:
        weight_pref_total += settings.corner_points_weight
    if entropy_points:
        weight_pref_total += settings.entropy_points_weight
    if face_points:
        weight_pref_total += settings.face_points_weight

    corner_centroid = None
    if corner_points:
        corner_centroid = centroid(corner_points)
        corner_centroid.weight = settings.corner_points_weight / weight_pref_total
        pois.append(corner_centroid)

    entropy_centroid = None
    if entropy_points:
        entropy_centroid = centroid(entropy_points)
        entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
        pois.append(entropy_centroid)

    face_centroid = None
    if face_points:
        face_centroid = centroid(face_points)
        face_centroid.weight = settings.face_points_weight / weight_pref_total
        pois.append(face_centroid)

    average_point = poi_average(pois, settings)

    if settings.annotate_image:
        d = ImageDraw.Draw(im)
        max_size = min(im.width, im.height) * 0.07
        if corner_centroid is not None:
            color = BLUE
            box = corner_centroid.bounding(max_size * corner_centroid.weight)
            d.text((box[0], box[1] - 15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
            d.ellipse(box, outline=color)
            if len(corner_points) > 1:
                for f in corner_points:
                    d.rectangle(f.bounding(4), outline=color)
        if entropy_centroid is not None:
            color = "#ff0"
            box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
            d.text((box[0], box[1] - 15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
            d.ellipse(box, outline=color)
            if len(entropy_points) > 1:
                for f in entropy_points:
                    d.rectangle(f.bounding(4), outline=color)
        if face_centroid is not None:
            color = RED
            box = face_centroid.bounding(max_size * face_centroid.weight)
            d.text((box[0], box[1] - 15), f"Face: {face_centroid.weight:.02f}", fill=color)
            d.ellipse(box, outline=color)
            if len(face_points) > 1:
                for f in face_points:
                    d.rectangle(f.bounding(4), outline=color)

        d.ellipse(average_point.bounding(max_size), outline=GREEN)

    return average_point


def image_face_points(im, settings):
    if settings.dnn_model_path is not None:
        detector = cv2.FaceDetectorYN.create(
            settings.dnn_model_path,
            "",
            (im.width, im.height),
            0.9,  # score threshold
            0.3,  # nms threshold
            5000  # keep top k before nms
        )
        faces = detector.detect(np.array(im))
        results = []
        if faces[1] is not None:
            for face in faces[1]:
                x = face[0]
                y = face[1]
                w = face[2]
                h = face[3]
                results.append(
                    PointOfInterest(
                        int(x + (w * 0.5)),  # face focus left/right is center
                        int(y + (h * 0.33)),  # face focus up/down is close to the top of the head
                        size=w,
                        weight=1 / len(faces[1])
                    )
                )
        return results
    else:
        np_im = np.array(im)
        gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)

        tries = [
            [f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01],
            [f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05],
            [f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05],
            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05],
            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05],
            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05],
            [f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05],
            [f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05]
        ]
        for t in tries:
            classifier = cv2.CascadeClassifier(t[0])
            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)
            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 []


def image_corner_points(im, settings):
    grayscale = im.convert("L")

    # naive attempt at preventing focal points from collecting at watermarks near the bottom
    gd = ImageDraw.Draw(grayscale)
    gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")

    np_im = np.array(grayscale)

    points = cv2.goodFeaturesToTrack(
        np_im,
        maxCorners=100,
        qualityLevel=0.04,
        minDistance=min(grayscale.width, grayscale.height) * 0.06,
        useHarrisDetector=False,
    )

    if points is None:
        return []

    focal_points = []
    for point in points:
        x, y = point.ravel()
        focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))

    return focal_points


def image_entropy_points(im, settings):
    landscape = im.height < im.width
    portrait = im.height > im.width
    if landscape:
        move_idx = [0, 2]
        move_max = im.size[0]
    elif portrait:
        move_idx = [1, 3]
        move_max = im.size[1]
    else:
        return []

    e_max = 0
    crop_current = [0, 0, settings.crop_width, settings.crop_height]
    crop_best = crop_current
    while crop_current[move_idx[1]] < move_max:
        crop = im.crop(tuple(crop_current))
        e = image_entropy(crop)

        if (e > e_max):
            e_max = e
            crop_best = list(crop_current)

        crop_current[move_idx[0]] += 4
        crop_current[move_idx[1]] += 4

    x_mid = int(crop_best[0] + settings.crop_width / 2)
    y_mid = int(crop_best[1] + settings.crop_height / 2)

    return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]


def image_entropy(im):
    # greyscale image entropy
    # band = np.asarray(im.convert("L"))
    band = np.asarray(im.convert("1"), dtype=np.uint8)
    hist, _ = np.histogram(band, bins=range(0, 256))
    hist = hist[hist > 0]
    return -np.log2(hist / hist.sum()).sum()


def centroid(pois):
    x = [poi.x for poi in pois]
    y = [poi.y for poi in pois]
    return PointOfInterest(sum(x) / len(pois), sum(y) / len(pois))


def poi_average(pois, settings):
    weight = 0.0
    x = 0.0
    y = 0.0
    for poi in pois:
        weight += poi.weight
        x += poi.x * poi.weight
        y += poi.y * poi.weight
    avg_x = round(weight and x / weight)
    avg_y = round(weight and y / weight)

    return PointOfInterest(avg_x, avg_y)


def is_landscape(w, h):
    return w > h


def is_portrait(w, h):
    return h > w


def is_square(w, h):
    return w == h


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'


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)
    return model_file_path


class PointOfInterest:
    def __init__(self, x, y, weight=1.0, size=10):
        self.x = x
        self.y = y
        self.weight = weight
        self.size = size

    def bounding(self, size):
        return [
            self.x - size // 2,
            self.y - size // 2,
            self.x + size // 2,
            self.y + size // 2
        ]


class Settings:
    def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
        self.crop_width = crop_width
        self.crop_height = crop_height
        self.corner_points_weight = corner_points_weight
        self.entropy_points_weight = entropy_points_weight
        self.face_points_weight = face_points_weight
        self.annotate_image = annotate_image
        self.desktop_view_image = False
        self.dnn_model_path = dnn_model_path