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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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Reindent autocrop with 4 spaces
This commit is contained in:
Aarni Koskela
parent
483545252f
commit
098d2fda52
@ -10,63 +10,64 @@ RED = "#F00"
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def crop_image(im, settings):
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""" Intelligently crop an image to the subject matter """
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""" Intelligently crop an image to the subject matter """
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scale_by = 1
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if is_landscape(im.width, im.height):
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scale_by = settings.crop_height / im.height
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elif is_portrait(im.width, im.height):
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scale_by = settings.crop_width / im.width
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elif is_square(im.width, im.height):
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if is_square(settings.crop_width, settings.crop_height):
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scale_by = settings.crop_width / im.width
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elif is_landscape(settings.crop_width, settings.crop_height):
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scale_by = settings.crop_width / im.width
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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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scale_by = 1
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if is_landscape(im.width, im.height):
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scale_by = settings.crop_height / im.height
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elif is_portrait(im.width, im.height):
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scale_by = settings.crop_width / im.width
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elif is_square(im.width, im.height):
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if is_square(settings.crop_width, settings.crop_height):
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scale_by = settings.crop_width / im.width
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elif is_landscape(settings.crop_width, settings.crop_height):
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scale_by = settings.crop_width / im.width
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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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focus = focal_point(im_debug, settings)
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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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# take the focal point and turn it into crop coordinates that try to center over the focal
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# point but then get adjusted back into the frame
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y_half = int(settings.crop_height / 2)
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x_half = int(settings.crop_width / 2)
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focus = focal_point(im_debug, settings)
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x1 = focus.x - x_half
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if x1 < 0:
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x1 = 0
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elif x1 + settings.crop_width > im.width:
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x1 = im.width - settings.crop_width
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# take the focal point and turn it into crop coordinates that try to center over the focal
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# point but then get adjusted back into the frame
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y_half = int(settings.crop_height / 2)
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x_half = int(settings.crop_width / 2)
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y1 = focus.y - y_half
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if y1 < 0:
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y1 = 0
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elif y1 + settings.crop_height > im.height:
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y1 = im.height - settings.crop_height
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x1 = focus.x - x_half
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if x1 < 0:
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x1 = 0
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elif x1 + settings.crop_width > im.width:
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x1 = im.width - settings.crop_width
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x2 = x1 + settings.crop_width
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y2 = y1 + settings.crop_height
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y1 = focus.y - y_half
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if y1 < 0:
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y1 = 0
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elif y1 + settings.crop_height > im.height:
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y1 = im.height - settings.crop_height
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crop = [x1, y1, x2, y2]
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x2 = x1 + settings.crop_width
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y2 = y1 + settings.crop_height
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results = []
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crop = [x1, y1, x2, y2]
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results.append(im.crop(tuple(crop)))
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results = []
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if settings.annotate_image:
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d = ImageDraw.Draw(im_debug)
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rect = list(crop)
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rect[2] -= 1
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rect[3] -= 1
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d.rectangle(rect, outline=GREEN)
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results.append(im_debug)
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if settings.destop_view_image:
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im_debug.show()
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results.append(im.crop(tuple(crop)))
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return results
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if settings.annotate_image:
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d = ImageDraw.Draw(im_debug)
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rect = list(crop)
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rect[2] -= 1
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rect[3] -= 1
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d.rectangle(rect, outline=GREEN)
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results.append(im_debug)
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if settings.destop_view_image:
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im_debug.show()
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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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@ -86,7 +87,7 @@ def focal_point(im, settings):
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corner_centroid = None
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if len(corner_points) > 0:
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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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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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@ -98,7 +99,7 @@ def focal_point(im, settings):
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face_centroid = None
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if len(face_points) > 0:
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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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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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@ -132,7 +133,7 @@ def focal_point(im, settings):
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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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return average_point
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@ -260,10 +261,11 @@ def image_entropy(im):
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hist = hist[hist > 0]
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return -np.log2(hist / hist.sum()).sum()
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def centroid(pois):
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x = [poi.x for poi in pois]
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y = [poi.y for poi in pois]
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return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
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x = [poi.x for poi in pois]
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y = [poi.y for poi in pois]
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return PointOfInterest(sum(x) / len(pois), sum(y) / len(pois))
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def poi_average(pois, settings):
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@ -281,59 +283,59 @@ def poi_average(pois, settings):
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def is_landscape(w, h):
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return w > h
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return w > h
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def is_portrait(w, h):
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return h > w
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return h > w
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def is_square(w, h):
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return w == h
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return w == h
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def download_and_cache_models(dirname):
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download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
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model_file_name = 'face_detection_yunet.onnx'
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download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
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model_file_name = 'face_detection_yunet.onnx'
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if not os.path.exists(dirname):
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os.makedirs(dirname)
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if not os.path.exists(dirname):
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os.makedirs(dirname)
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cache_file = os.path.join(dirname, model_file_name)
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if not os.path.exists(cache_file):
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print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
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response = requests.get(download_url)
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with open(cache_file, "wb") as f:
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f.write(response.content)
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cache_file = os.path.join(dirname, model_file_name)
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if not os.path.exists(cache_file):
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print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
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response = requests.get(download_url)
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with open(cache_file, "wb") as f:
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f.write(response.content)
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if os.path.exists(cache_file):
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return cache_file
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return None
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if os.path.exists(cache_file):
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return cache_file
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return None
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class PointOfInterest:
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def __init__(self, x, y, weight=1.0, size=10):
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self.x = x
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self.y = y
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self.weight = weight
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self.size = size
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def __init__(self, x, y, weight=1.0, size=10):
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self.x = x
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self.y = y
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self.weight = weight
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self.size = size
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def bounding(self, size):
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return [
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self.x - size//2,
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self.y - size//2,
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self.x + size//2,
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self.y + size//2
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]
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def bounding(self, size):
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return [
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self.x - size // 2,
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self.y - size // 2,
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self.x + size // 2,
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self.y + size // 2
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]
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class Settings:
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def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
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self.crop_width = crop_width
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self.crop_height = crop_height
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self.corner_points_weight = corner_points_weight
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self.entropy_points_weight = entropy_points_weight
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self.face_points_weight = face_points_weight
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self.annotate_image = annotate_image
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self.destop_view_image = False
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self.dnn_model_path = dnn_model_path
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def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
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self.crop_width = crop_width
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self.crop_height = crop_height
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self.corner_points_weight = corner_points_weight
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self.entropy_points_weight = entropy_points_weight
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self.face_points_weight = face_points_weight
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self.annotate_image = annotate_image
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self.destop_view_image = False
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self.dnn_model_path = dnn_model_path
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