diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index 7770d22f..8e667a4d 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -10,63 +10,64 @@ RED = "#F00" def crop_image(im, settings): - """ Intelligently crop an image to the subject matter """ + """ 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 + 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) + im = im.resize((int(im.width * scale_by), int(im.height * scale_by))) + im_debug = im.copy() - # 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) + focus = focal_point(im_debug, settings) - x1 = focus.x - x_half - if x1 < 0: - x1 = 0 - elif x1 + settings.crop_width > im.width: - x1 = im.width - settings.crop_width + # 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) - y1 = focus.y - y_half - if y1 < 0: - y1 = 0 - elif y1 + settings.crop_height > im.height: - y1 = im.height - settings.crop_height + x1 = focus.x - x_half + if x1 < 0: + x1 = 0 + elif x1 + settings.crop_width > im.width: + x1 = im.width - settings.crop_width - x2 = x1 + settings.crop_width - y2 = y1 + settings.crop_height + y1 = focus.y - y_half + if y1 < 0: + y1 = 0 + elif y1 + settings.crop_height > im.height: + y1 = im.height - settings.crop_height - crop = [x1, y1, x2, y2] + x2 = x1 + settings.crop_width + y2 = y1 + settings.crop_height - results = [] + crop = [x1, y1, x2, y2] - results.append(im.crop(tuple(crop))) + results = [] - 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.destop_view_image: - im_debug.show() + results.append(im.crop(tuple(crop))) - return results + 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.destop_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 [] @@ -86,7 +87,7 @@ def focal_point(im, settings): corner_centroid = None if len(corner_points) > 0: corner_centroid = centroid(corner_points) - corner_centroid.weight = settings.corner_points_weight / weight_pref_total + corner_centroid.weight = settings.corner_points_weight / weight_pref_total pois.append(corner_centroid) entropy_centroid = None @@ -98,7 +99,7 @@ def focal_point(im, settings): face_centroid = None if len(face_points) > 0: face_centroid = centroid(face_points) - face_centroid.weight = settings.face_points_weight / weight_pref_total + face_centroid.weight = settings.face_points_weight / weight_pref_total pois.append(face_centroid) average_point = poi_average(pois, settings) @@ -132,7 +133,7 @@ def focal_point(im, settings): d.rectangle(f.bounding(4), outline=color) d.ellipse(average_point.bounding(max_size), outline=GREEN) - + return average_point @@ -260,10 +261,11 @@ def image_entropy(im): 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)) + 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): @@ -281,59 +283,59 @@ def poi_average(pois, settings): def is_landscape(w, h): - return w > h + return w > h def is_portrait(w, h): - return h > w + return h > w def is_square(w, h): - return w == h + 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' + 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' - if not os.path.exists(dirname): - os.makedirs(dirname) + if not os.path.exists(dirname): + os.makedirs(dirname) - 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: - f.write(response.content) + 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: + f.write(response.content) - if os.path.exists(cache_file): - return cache_file - return None + if os.path.exists(cache_file): + return cache_file + return None 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 __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 - ] + 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.destop_view_image = False - self.dnn_model_path = dnn_model_path + 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.destop_view_image = False + self.dnn_model_path = dnn_model_path