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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-01-17 11:50:18 +08:00
reformat file with uniform indentation
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w-e-w
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03ee297aa2
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d608926f81
@ -27,7 +27,6 @@ def crop_image(im, settings):
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elif is_portrait(settings.crop_width, settings.crop_height):
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scale_by = settings.crop_height / im.height
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im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
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im_debug = im.copy()
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@ -71,6 +70,7 @@ def crop_image(im, settings):
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return results
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def focal_point(im, settings):
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corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
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entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
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@ -80,118 +80,120 @@ def focal_point(im, settings):
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weight_pref_total = 0
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if corner_points:
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weight_pref_total += settings.corner_points_weight
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weight_pref_total += settings.corner_points_weight
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if entropy_points:
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weight_pref_total += settings.entropy_points_weight
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weight_pref_total += settings.entropy_points_weight
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if face_points:
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weight_pref_total += settings.face_points_weight
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weight_pref_total += settings.face_points_weight
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corner_centroid = None
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if corner_points:
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corner_centroid = centroid(corner_points)
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corner_centroid.weight = settings.corner_points_weight / weight_pref_total
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pois.append(corner_centroid)
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corner_centroid = centroid(corner_points)
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corner_centroid.weight = settings.corner_points_weight / weight_pref_total
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pois.append(corner_centroid)
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entropy_centroid = None
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if entropy_points:
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entropy_centroid = centroid(entropy_points)
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entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
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pois.append(entropy_centroid)
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entropy_centroid = centroid(entropy_points)
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entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
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pois.append(entropy_centroid)
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face_centroid = None
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if face_points:
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face_centroid = centroid(face_points)
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face_centroid.weight = settings.face_points_weight / weight_pref_total
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pois.append(face_centroid)
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face_centroid = centroid(face_points)
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face_centroid.weight = settings.face_points_weight / weight_pref_total
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pois.append(face_centroid)
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average_point = poi_average(pois, settings)
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if settings.annotate_image:
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d = ImageDraw.Draw(im)
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max_size = min(im.width, im.height) * 0.07
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if corner_centroid is not None:
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color = BLUE
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box = corner_centroid.bounding(max_size * corner_centroid.weight)
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d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
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d.ellipse(box, outline=color)
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if len(corner_points) > 1:
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for f in corner_points:
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d.rectangle(f.bounding(4), outline=color)
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if entropy_centroid is not None:
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color = "#ff0"
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box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
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d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
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d.ellipse(box, outline=color)
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if len(entropy_points) > 1:
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for f in entropy_points:
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d.rectangle(f.bounding(4), outline=color)
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if face_centroid is not None:
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color = RED
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box = face_centroid.bounding(max_size * face_centroid.weight)
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d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
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d.ellipse(box, outline=color)
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if len(face_points) > 1:
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for f in face_points:
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d.rectangle(f.bounding(4), outline=color)
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d = ImageDraw.Draw(im)
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max_size = min(im.width, im.height) * 0.07
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if corner_centroid is not None:
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color = BLUE
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box = corner_centroid.bounding(max_size * corner_centroid.weight)
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d.text((box[0], box[1] - 15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
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d.ellipse(box, outline=color)
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if len(corner_points) > 1:
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for f in corner_points:
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d.rectangle(f.bounding(4), outline=color)
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if entropy_centroid is not None:
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color = "#ff0"
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box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
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d.text((box[0], box[1] - 15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
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d.ellipse(box, outline=color)
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if len(entropy_points) > 1:
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for f in entropy_points:
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d.rectangle(f.bounding(4), outline=color)
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if face_centroid is not None:
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color = RED
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box = face_centroid.bounding(max_size * face_centroid.weight)
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d.text((box[0], box[1] - 15), f"Face: {face_centroid.weight:.02f}", fill=color)
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d.ellipse(box, outline=color)
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if len(face_points) > 1:
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for f in face_points:
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d.rectangle(f.bounding(4), outline=color)
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d.ellipse(average_point.bounding(max_size), outline=GREEN)
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d.ellipse(average_point.bounding(max_size), outline=GREEN)
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return average_point
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def image_face_points(im, settings):
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if settings.dnn_model_path is not None:
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detector = cv2.FaceDetectorYN.create(
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settings.dnn_model_path,
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"",
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(im.width, im.height),
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0.9, # score threshold
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0.3, # nms threshold
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5000 # keep top k before nms
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)
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faces = detector.detect(np.array(im))
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results = []
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if faces[1] is not None:
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for face in faces[1]:
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x = face[0]
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y = face[1]
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w = face[2]
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h = face[3]
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results.append(
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PointOfInterest(
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int(x + (w * 0.5)), # face focus left/right is center
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int(y + (h * 0.33)), # face focus up/down is close to the top of the head
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size = w,
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weight = 1/len(faces[1])
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)
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)
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return results
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detector = cv2.FaceDetectorYN.create(
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settings.dnn_model_path,
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"",
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(im.width, im.height),
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0.9, # score threshold
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0.3, # nms threshold
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5000 # keep top k before nms
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)
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faces = detector.detect(np.array(im))
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results = []
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if faces[1] is not None:
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for face in faces[1]:
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x = face[0]
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y = face[1]
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w = face[2]
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h = face[3]
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results.append(
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PointOfInterest(
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int(x + (w * 0.5)), # face focus left/right is center
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int(y + (h * 0.33)), # face focus up/down is close to the top of the head
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size=w,
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weight=1 / len(faces[1])
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)
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)
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return results
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else:
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np_im = np.array(im)
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gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
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np_im = np.array(im)
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gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
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tries = [
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[ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
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[ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
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[ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
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[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
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[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
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[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
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[ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
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[ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
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]
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for t in tries:
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classifier = cv2.CascadeClassifier(t[0])
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minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
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try:
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faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
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minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
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except Exception:
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continue
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tries = [
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[f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01],
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[f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05],
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[f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05],
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[f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05],
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[f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05],
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[f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05],
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[f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05],
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[f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05]
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]
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for t in tries:
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classifier = cv2.CascadeClassifier(t[0])
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minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
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try:
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faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
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minNeighbors=7, minSize=(minsize, minsize),
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flags=cv2.CASCADE_SCALE_IMAGE)
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except Exception:
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continue
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if faces:
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rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
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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]
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if faces:
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rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
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return [PointOfInterest((r[0] + r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0] - r[2]),
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weight=1 / len(rects)) for r in rects]
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return []
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@ -200,7 +202,7 @@ def image_corner_points(im, settings):
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# naive attempt at preventing focal points from collecting at watermarks near the bottom
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gd = ImageDraw.Draw(grayscale)
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gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
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gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")
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np_im = np.array(grayscale)
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@ -208,7 +210,7 @@ def image_corner_points(im, settings):
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np_im,
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maxCorners=100,
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qualityLevel=0.04,
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minDistance=min(grayscale.width, grayscale.height)*0.06,
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minDistance=min(grayscale.width, grayscale.height) * 0.06,
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useHarrisDetector=False,
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)
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@ -217,8 +219,8 @@ def image_corner_points(im, settings):
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focal_points = []
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for point in points:
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x, y = point.ravel()
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focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
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x, y = point.ravel()
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focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))
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return focal_points
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@ -227,13 +229,13 @@ def image_entropy_points(im, settings):
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landscape = im.height < im.width
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portrait = im.height > im.width
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if landscape:
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move_idx = [0, 2]
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move_max = im.size[0]
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move_idx = [0, 2]
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move_max = im.size[0]
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elif portrait:
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move_idx = [1, 3]
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move_max = im.size[1]
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move_idx = [1, 3]
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move_max = im.size[1]
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else:
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return []
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return []
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e_max = 0
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crop_current = [0, 0, settings.crop_width, settings.crop_height]
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@ -243,14 +245,14 @@ def image_entropy_points(im, settings):
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e = image_entropy(crop)
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if (e > e_max):
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e_max = e
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crop_best = list(crop_current)
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e_max = e
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crop_best = list(crop_current)
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crop_current[move_idx[0]] += 4
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crop_current[move_idx[1]] += 4
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x_mid = int(crop_best[0] + settings.crop_width/2)
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y_mid = int(crop_best[1] + settings.crop_height/2)
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x_mid = int(crop_best[0] + settings.crop_width / 2)
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y_mid = int(crop_best[1] + settings.crop_height / 2)
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return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
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