From 0ddaf8d2028a7251e8c4ad93551a43b5d4700841 Mon Sep 17 00:00:00 2001 From: captin411 Date: Thu, 20 Oct 2022 00:34:55 -0700 Subject: [PATCH] improve face detection a lot --- modules/textual_inversion/autocrop.py | 95 +++++++++++++++++---------- 1 file changed, 60 insertions(+), 35 deletions(-) diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index f858a958..5a551c25 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -8,12 +8,18 @@ GREEN = "#0F0" BLUE = "#00F" RED = "#F00" + def crop_image(im, settings): """ Intelligently crop an image to the subject matter """ if im.height > im.width: im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width)) - else: + elif im.width > im.height: im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height)) + else: + im = im.resize((settings.crop_width, settings.crop_height)) + + if im.height == im.width: + return im focus = focal_point(im, settings) @@ -78,13 +84,18 @@ def focal_point(im, settings): [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ] ) + average_point = poi_average(pois, settings) + if settings.annotate_image: d = ImageDraw.Draw(im) - - average_point = poi_average(pois, settings, im=im) - - if settings.annotate_image: - d.ellipse([average_point.x - 25, average_point.y - 25, average_point.x + 25, average_point.y + 25], outline=GREEN) + 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) return average_point @@ -92,22 +103,32 @@ def focal_point(im, settings): def image_face_points(im, settings): np_im = np.array(im) gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY) - classifier = cv2.CascadeClassifier(f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml') - minsize = int(min(im.width, im.height) * 0.15) # at least N percent of the smallest side - faces = classifier.detectMultiScale(gray, scaleFactor=1.05, - minNeighbors=5, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE) + 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 ] + ] - if len(faces) == 0: - return [] + 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 - rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces] - if settings.annotate_image: - for f in rects: - d = ImageDraw.Draw(im) - d.rectangle(f, outline=RED) - - return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 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])) for r in rects] + return [] def image_corner_points(im, settings): @@ -132,8 +153,8 @@ def image_corner_points(im, settings): focal_points = [] for point in points: - x, y = point.ravel() - focal_points.append(PointOfInterest(x, y)) + x, y = point.ravel() + focal_points.append(PointOfInterest(x, y, size=4)) return focal_points @@ -167,31 +188,26 @@ 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)] + return [PointOfInterest(x_mid, y_mid, size=25)] def image_entropy(im): # greyscale image entropy - band = np.asarray(im.convert("1")) + # 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 poi_average(pois, settings, im=None): +def poi_average(pois, settings): weight = 0.0 x = 0.0 y = 0.0 - for pois in pois: - if settings.annotate_image and im is not None: - w = 4 * 0.5 * sqrt(pois.weight) - d = ImageDraw.Draw(im) - d.ellipse([ - pois.x - w, pois.y - w, - pois.x + w, pois.y + w ], fill=BLUE) - weight += pois.weight - x += pois.x * pois.weight - y += pois.y * pois.weight + for poi in pois: + weight += poi.weight + x += poi.x * poi.weight + y += poi.y * poi.weight avg_x = round(x / weight) avg_y = round(y / weight) @@ -199,10 +215,19 @@ def poi_average(pois, settings, im=None): class PointOfInterest: - def __init__(self, x, y, weight=1.0): + 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: