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-rw-r--r--modules/textual_inversion/autocrop.py210
1 files changed, 106 insertions, 104 deletions
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 051be118..e223a2e0 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -27,7 +27,6 @@ def crop_image(im, settings):
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()
@@ -71,6 +70,7 @@ def crop_image(im, settings):
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 []
@@ -80,118 +80,120 @@ def focal_point(im, settings):
weight_pref_total = 0
if corner_points:
- weight_pref_total += settings.corner_points_weight
+ weight_pref_total += settings.corner_points_weight
if entropy_points:
- weight_pref_total += settings.entropy_points_weight
+ weight_pref_total += settings.entropy_points_weight
if face_points:
- weight_pref_total += settings.face_points_weight
+ 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)
+ 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)
+ 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)
+ 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)
+ 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
+ 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]
+ 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 []
@@ -200,7 +202,7 @@ def image_corner_points(im, settings):
# 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")
+ gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")
np_im = np.array(grayscale)
@@ -208,7 +210,7 @@ def image_corner_points(im, settings):
np_im,
maxCorners=100,
qualityLevel=0.04,
- minDistance=min(grayscale.width, grayscale.height)*0.06,
+ minDistance=min(grayscale.width, grayscale.height) * 0.06,
useHarrisDetector=False,
)
@@ -217,8 +219,8 @@ def image_corner_points(im, settings):
focal_points = []
for point in points:
- x, y = point.ravel()
- focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
+ x, y = point.ravel()
+ focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))
return focal_points
@@ -227,13 +229,13 @@ 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]
+ move_idx = [0, 2]
+ move_max = im.size[0]
elif portrait:
- move_idx = [1, 3]
- move_max = im.size[1]
+ move_idx = [1, 3]
+ move_max = im.size[1]
else:
- return []
+ return []
e_max = 0
crop_current = [0, 0, settings.crop_width, settings.crop_height]
@@ -243,14 +245,14 @@ def image_entropy_points(im, settings):
e = image_entropy(crop)
if (e > e_max):
- e_max = e
- crop_best = list(crop_current)
+ 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)
+ 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)]
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 06:44:46 +0000