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Merge pull request #14121 from AUTOMATIC1111/fix-Auto-focal-point-crop-for-opencv-4.8.x

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Fix auto focal point crop for opencv >= 4.8
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AUTOMATIC1111 2023-12-02 09:46:00 +03:00 committed by GitHub
commit 9eadc4f146
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2 changed files with 121 additions and 116 deletions
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233
modules/textual_inversion/autocrop.py
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@ -3,6 +3,8 @@ import requests
import os
import numpy as np
from PIL import ImageDraw
from modules import paths_internal
from pkg_resources import parse_version
GREEN = "#0F0"
BLUE = "#00F"
@ -25,7 +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()
@ -69,6 +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 []
@ -78,118 +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 = 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.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)
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
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]
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 []
@ -198,7 +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)
@ -206,7 +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,
)
@ -215,8 +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
@ -225,13 +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]
@ -241,14 +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)]
@ -294,22 +298,23 @@ def is_square(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'
model_dir_opencv = os.path.join(paths_internal.models_path, 'opencv')
if parse_version(cv2.__version__) >= parse_version('4.8'):
model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet_2023mar.onnx')
model_url = 'https://github.com/opencv/opencv_zoo/blob/b6e370b10f641879a87890d44e42173077154a05/models/face_detection_yunet/face_detection_yunet_2023mar.onnx?raw=true'
else:
model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet.onnx')
model_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
os.makedirs(dirname, exist_ok=True)
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:
def download_and_cache_models():
if not os.path.exists(model_file_path):
os.makedirs(model_dir_opencv, exist_ok=True)
print(f"downloading face detection model from '{model_url}' to '{model_file_path}'")
response = requests.get(model_url)
with open(model_file_path, "wb") as f:
f.write(response.content)
if os.path.exists(cache_file):
return cache_file
return None
return model_file_path
class PointOfInterest:

4
modules/textual_inversion/preprocess.py
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@ -3,7 +3,7 @@ from PIL import Image, ImageOps
import math
import tqdm
from modules import paths, shared, images, deepbooru
from modules import shared, images, deepbooru
from modules.textual_inversion import autocrop
@ -196,7 +196,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
dnn_model_path = None
try:
dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
dnn_model_path = autocrop.download_and_cache_models()
except Exception as e:
print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)