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reformat file with uniform indentation

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w-e-w 2023-11-28 12:12:27 +09:00
parent 03ee297aa2
commit d608926f81
1 changed files with 103 additions and 101 deletions
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204
modules/textual_inversion/autocrop.py
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@ -27,7 +27,6 @@ def crop_image(im, settings):
elif is_portrait(settings.crop_width, settings.crop_height): elif is_portrait(settings.crop_width, settings.crop_height):
scale_by = settings.crop_height / im.height scale_by = settings.crop_height / im.height
im = im.resize((int(im.width * scale_by), int(im.height * scale_by))) im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
im_debug = im.copy() im_debug = im.copy()
@ -71,6 +70,7 @@ def crop_image(im, settings):
return results return results
def focal_point(im, settings): def focal_point(im, settings):
corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else [] 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 [] entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
@ -80,118 +80,120 @@ def focal_point(im, settings):
weight_pref_total = 0 weight_pref_total = 0
if corner_points: if corner_points:
weight_pref_total += settings.corner_points_weight weight_pref_total += settings.corner_points_weight
if entropy_points: if entropy_points:
weight_pref_total += settings.entropy_points_weight weight_pref_total += settings.entropy_points_weight
if face_points: if face_points:
weight_pref_total += settings.face_points_weight weight_pref_total += settings.face_points_weight
corner_centroid = None corner_centroid = None
if corner_points: if corner_points:
corner_centroid = centroid(corner_points) 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) pois.append(corner_centroid)
entropy_centroid = None entropy_centroid = None
if entropy_points: if entropy_points:
entropy_centroid = centroid(entropy_points) entropy_centroid = centroid(entropy_points)
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
pois.append(entropy_centroid) pois.append(entropy_centroid)
face_centroid = None face_centroid = None
if face_points: if face_points:
face_centroid = centroid(face_points) 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) pois.append(face_centroid)
average_point = poi_average(pois, settings) average_point = poi_average(pois, settings)
if settings.annotate_image: if settings.annotate_image:
d = ImageDraw.Draw(im) d = ImageDraw.Draw(im)
max_size = min(im.width, im.height) * 0.07 max_size = min(im.width, im.height) * 0.07
if corner_centroid is not None: if corner_centroid is not None:
color = BLUE color = BLUE
box = corner_centroid.bounding(max_size * corner_centroid.weight) 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.text((box[0], box[1] - 15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color) d.ellipse(box, outline=color)
if len(corner_points) > 1: if len(corner_points) > 1:
for f in corner_points: for f in corner_points:
d.rectangle(f.bounding(4), outline=color) d.rectangle(f.bounding(4), outline=color)
if entropy_centroid is not None: if entropy_centroid is not None:
color = "#ff0" color = "#ff0"
box = entropy_centroid.bounding(max_size * entropy_centroid.weight) 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.text((box[0], box[1] - 15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color) d.ellipse(box, outline=color)
if len(entropy_points) > 1: if len(entropy_points) > 1:
for f in entropy_points: for f in entropy_points:
d.rectangle(f.bounding(4), outline=color) d.rectangle(f.bounding(4), outline=color)
if face_centroid is not None: if face_centroid is not None:
color = RED color = RED
box = face_centroid.bounding(max_size * face_centroid.weight) 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.text((box[0], box[1] - 15), f"Face: {face_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color) d.ellipse(box, outline=color)
if len(face_points) > 1: if len(face_points) > 1:
for f in face_points: for f in face_points:
d.rectangle(f.bounding(4), outline=color) d.rectangle(f.bounding(4), outline=color)
d.ellipse(average_point.bounding(max_size), outline=GREEN) d.ellipse(average_point.bounding(max_size), outline=GREEN)
return average_point return average_point
def image_face_points(im, settings): def image_face_points(im, settings):
if settings.dnn_model_path is not None: if settings.dnn_model_path is not None:
detector = cv2.FaceDetectorYN.create( detector = cv2.FaceDetectorYN.create(
settings.dnn_model_path, settings.dnn_model_path,
"", "",
(im.width, im.height), (im.width, im.height),
0.9, # score threshold 0.9, # score threshold
0.3, # nms threshold 0.3, # nms threshold
5000 # keep top k before nms 5000 # keep top k before nms
) )
faces = detector.detect(np.array(im)) faces = detector.detect(np.array(im))
results = [] results = []
if faces[1] is not None: if faces[1] is not None:
for face in faces[1]: for face in faces[1]:
x = face[0] x = face[0]
y = face[1] y = face[1]
w = face[2] w = face[2]
h = face[3] h = face[3]
results.append( results.append(
PointOfInterest( PointOfInterest(
int(x + (w * 0.5)), # face focus left/right is center 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 int(y + (h * 0.33)), # face focus up/down is close to the top of the head
size = w, size=w,
weight = 1/len(faces[1]) weight=1 / len(faces[1])
) )
) )
return results return results
else: else:
np_im = np.array(im) np_im = np.array(im)
gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY) gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
tries = [ tries = [
[ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ], [f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01],
[ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ], [f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05],
[ f'{cv2.data.haarcascades}haarcascade_profileface.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_alt.xml', 0.05],
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.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_frontalface_alt_tree.xml', 0.05],
[ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ], [f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05],
[ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ] [f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05]
] ]
for t in tries: for t in tries:
classifier = cv2.CascadeClassifier(t[0]) classifier = cv2.CascadeClassifier(t[0])
minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
try: try:
faces = classifier.detectMultiScale(gray, scaleFactor=1.1, faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE) minNeighbors=7, minSize=(minsize, minsize),
except Exception: flags=cv2.CASCADE_SCALE_IMAGE)
continue except Exception:
continue
if faces: if faces:
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in 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 []
@ -200,7 +202,7 @@ def image_corner_points(im, settings):
# naive attempt at preventing focal points from collecting at watermarks near the bottom # naive attempt at preventing focal points from collecting at watermarks near the bottom
gd = ImageDraw.Draw(grayscale) gd = ImageDraw.Draw(grayscale)
gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999") gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")
np_im = np.array(grayscale) np_im = np.array(grayscale)
@ -208,7 +210,7 @@ def image_corner_points(im, settings):
np_im, np_im,
maxCorners=100, maxCorners=100,
qualityLevel=0.04, qualityLevel=0.04,
minDistance=min(grayscale.width, grayscale.height)*0.06, minDistance=min(grayscale.width, grayscale.height) * 0.06,
useHarrisDetector=False, useHarrisDetector=False,
) )
@ -217,8 +219,8 @@ def image_corner_points(im, settings):
focal_points = [] focal_points = []
for point in points: for point in points:
x, y = point.ravel() x, y = point.ravel()
focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points))) focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))
return focal_points return focal_points
@ -227,13 +229,13 @@ def image_entropy_points(im, settings):
landscape = im.height < im.width landscape = im.height < im.width
portrait = im.height > im.width portrait = im.height > im.width
if landscape: if landscape:
move_idx = [0, 2] move_idx = [0, 2]
move_max = im.size[0] move_max = im.size[0]
elif portrait: elif portrait:
move_idx = [1, 3] move_idx = [1, 3]
move_max = im.size[1] move_max = im.size[1]
else: else:
return [] return []
e_max = 0 e_max = 0
crop_current = [0, 0, settings.crop_width, settings.crop_height] crop_current = [0, 0, settings.crop_width, settings.crop_height]
@ -243,14 +245,14 @@ def image_entropy_points(im, settings):
e = image_entropy(crop) e = image_entropy(crop)
if (e > e_max): if (e > e_max):
e_max = e e_max = e
crop_best = list(crop_current) crop_best = list(crop_current)
crop_current[move_idx[0]] += 4 crop_current[move_idx[0]] += 4
crop_current[move_idx[1]] += 4 crop_current[move_idx[1]] += 4
x_mid = int(crop_best[0] + settings.crop_width/2) x_mid = int(crop_best[0] + settings.crop_width / 2)
y_mid = int(crop_best[1] + settings.crop_height/2) y_mid = int(crop_best[1] + settings.crop_height / 2)
return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)] return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]