diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index b2f9241c..caaf18c8 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -1,4 +1,5 @@ import cv2 +import os from collections import defaultdict from math import log, sqrt import numpy as np @@ -26,19 +27,9 @@ def crop_image(im, settings): scale_by = settings.crop_height / im.height im = im.resize((int(im.width * scale_by), int(im.height * scale_by))) + im_debug = im.copy() - if im.width == settings.crop_width and im.height == settings.crop_height: - if settings.annotate_image: - d = ImageDraw.Draw(im) - rect = [0, 0, im.width, im.height] - rect[2] -= 1 - rect[3] -= 1 - d.rectangle(rect, outline=GREEN) - if settings.destop_view_image: - im.show() - return im - - focus = focal_point(im, settings) + 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 @@ -62,89 +53,143 @@ def crop_image(im, settings): crop = [x1, y1, x2, y2] + results = [] + + results.append(im.crop(tuple(crop))) + if settings.annotate_image: - d = ImageDraw.Draw(im) + 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.show() + im_debug.show() - return im.crop(tuple(crop)) + return results def focal_point(im, settings): corner_points = image_corner_points(im, settings) entropy_points = image_entropy_points(im, settings) face_points = image_face_points(im, settings) - total_points = len(corner_points) + len(entropy_points) + len(face_points) - - corner_weight = settings.corner_points_weight - entropy_weight = settings.entropy_points_weight - face_weight = settings.face_points_weight - - weight_pref_total = corner_weight + entropy_weight + face_weight - - # weight things pois = [] - if weight_pref_total == 0 or total_points == 0: - return pois - pois.extend( - [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ] - ) - pois.extend( - [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ] - ) - pois.extend( - [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ] - ) + weight_pref_total = 0 + if len(corner_points) > 0: + weight_pref_total += settings.corner_points_weight + if len(entropy_points) > 0: + weight_pref_total += settings.entropy_points_weight + if len(face_points) > 0: + weight_pref_total += settings.face_points_weight + + corner_centroid = None + if len(corner_points) > 0: + corner_centroid = centroid(corner_points) + corner_centroid.weight = settings.corner_points_weight / weight_pref_total + pois.append(corner_centroid) + + entropy_centroid = None + if len(entropy_points) > 0: + entropy_centroid = centroid(entropy_points) + entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total + pois.append(entropy_centroid) + + face_centroid = None + if len(face_points) > 0: + 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) - for f in face_points: - d.rectangle(f.bounding(f.size), outline=RED) - for f in entropy_points: - d.rectangle(f.bounding(30), outline=BLUE) - for poi in pois: - w = max(4, 4 * 0.5 * sqrt(poi.weight)) - d.ellipse(poi.bounding(w), fill=BLUE) - d.ellipse(average_point.bounding(25), outline=GREEN) + 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), "Edge: %.02f" % corner_centroid.weight, 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), "Entropy: %.02f" % entropy_centroid.weight, 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), "Face: %.02f" % face_centroid.weight, 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): - np_im = np.array(im) - gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY) + if settings.dnn_model_path is not None: + detector = cv2.FaceDetectorYN.create( + settings.dnn_model_path, + "", + (im.width, im.height), + 0.8, # 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)), # 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 ] - ] + 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: + continue - for t in tries: - # print(t[0]) - 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: - continue - - if len(faces) > 0: - 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])) for r in rects] + if len(faces) > 0: + 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 [] @@ -161,7 +206,7 @@ def image_corner_points(im, settings): np_im, maxCorners=100, qualityLevel=0.04, - minDistance=min(grayscale.width, grayscale.height)*0.07, + minDistance=min(grayscale.width, grayscale.height)*0.03, useHarrisDetector=False, ) @@ -171,7 +216,7 @@ def image_corner_points(im, settings): focal_points = [] for point in points: x, y = point.ravel() - focal_points.append(PointOfInterest(x, y, size=4)) + focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points))) return focal_points @@ -205,17 +250,22 @@ def image_entropy_points(im, settings): 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)] + 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) + 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 @@ -260,11 +310,12 @@ class PointOfInterest: 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): + 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 = entropy_points_weight + self.face_points_weight = face_points_weight self.annotate_image = annotate_image - self.destop_view_image = False \ No newline at end of file + self.destop_view_image = False + self.dnn_model_path = dnn_model_path \ No newline at end of file