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Reindent autocrop with 4 spaces

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This commit is contained in:
Aarni Koskela 2023-05-11 18:13:35 +03:00
parent 483545252f
commit 098d2fda52
1 changed files with 90 additions and 88 deletions
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178
modules/textual_inversion/autocrop.py
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@ -10,63 +10,64 @@ RED = "#F00"
def crop_image(im, settings): def crop_image(im, settings):
""" Intelligently crop an image to the subject matter """ """ Intelligently crop an image to the subject matter """
scale_by = 1 scale_by = 1
if is_landscape(im.width, im.height): if is_landscape(im.width, im.height):
scale_by = settings.crop_height / im.height scale_by = settings.crop_height / im.height
elif is_portrait(im.width, im.height): elif is_portrait(im.width, im.height):
scale_by = settings.crop_width / im.width scale_by = settings.crop_width / im.width
elif is_square(im.width, im.height): elif is_square(im.width, im.height):
if is_square(settings.crop_width, settings.crop_height): if is_square(settings.crop_width, settings.crop_height):
scale_by = settings.crop_width / im.width scale_by = settings.crop_width / im.width
elif is_landscape(settings.crop_width, settings.crop_height): elif is_landscape(settings.crop_width, settings.crop_height):
scale_by = settings.crop_width / im.width scale_by = settings.crop_width / im.width
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_debug = im.copy()
focus = focal_point(im_debug, settings) im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
im_debug = im.copy()
# take the focal point and turn it into crop coordinates that try to center over the focal focus = focal_point(im_debug, settings)
# point but then get adjusted back into the frame
y_half = int(settings.crop_height / 2)
x_half = int(settings.crop_width / 2)
x1 = focus.x - x_half # take the focal point and turn it into crop coordinates that try to center over the focal
if x1 < 0: # point but then get adjusted back into the frame
x1 = 0 y_half = int(settings.crop_height / 2)
elif x1 + settings.crop_width > im.width: x_half = int(settings.crop_width / 2)
x1 = im.width - settings.crop_width
y1 = focus.y - y_half x1 = focus.x - x_half
if y1 < 0: if x1 < 0:
y1 = 0 x1 = 0
elif y1 + settings.crop_height > im.height: elif x1 + settings.crop_width > im.width:
y1 = im.height - settings.crop_height x1 = im.width - settings.crop_width
x2 = x1 + settings.crop_width y1 = focus.y - y_half
y2 = y1 + settings.crop_height if y1 < 0:
y1 = 0
elif y1 + settings.crop_height > im.height:
y1 = im.height - settings.crop_height
crop = [x1, y1, x2, y2] x2 = x1 + settings.crop_width
y2 = y1 + settings.crop_height
results = [] crop = [x1, y1, x2, y2]
results.append(im.crop(tuple(crop))) results = []
if settings.annotate_image: results.append(im.crop(tuple(crop)))
d = ImageDraw.Draw(im_debug)
rect = list(crop)
rect[2] -= 1
rect[3] -= 1
d.rectangle(rect, outline=GREEN)
results.append(im_debug)
if settings.destop_view_image:
im_debug.show()
return results if settings.annotate_image:
d = ImageDraw.Draw(im_debug)
rect = list(crop)
rect[2] -= 1
rect[3] -= 1
d.rectangle(rect, outline=GREEN)
results.append(im_debug)
if settings.destop_view_image:
im_debug.show()
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 []
@ -86,7 +87,7 @@ def focal_point(im, settings):
corner_centroid = None corner_centroid = None
if len(corner_points) > 0: if len(corner_points) > 0:
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
@ -98,7 +99,7 @@ def focal_point(im, settings):
face_centroid = None face_centroid = None
if len(face_points) > 0: if len(face_points) > 0:
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)
@ -132,7 +133,7 @@ def focal_point(im, settings):
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
@ -260,10 +261,11 @@ def image_entropy(im):
hist = hist[hist > 0] hist = hist[hist > 0]
return -np.log2(hist / hist.sum()).sum() return -np.log2(hist / hist.sum()).sum()
def centroid(pois): def centroid(pois):
x = [poi.x for poi in pois] x = [poi.x for poi in pois]
y = [poi.y for poi in pois] y = [poi.y for poi in pois]
return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois)) return PointOfInterest(sum(x) / len(pois), sum(y) / len(pois))
def poi_average(pois, settings): def poi_average(pois, settings):
@ -281,59 +283,59 @@ def poi_average(pois, settings):
def is_landscape(w, h): def is_landscape(w, h):
return w > h return w > h
def is_portrait(w, h): def is_portrait(w, h):
return h > w return h > w
def is_square(w, h): def is_square(w, h):
return w == h return w == h
def download_and_cache_models(dirname): 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' 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_file_name = 'face_detection_yunet.onnx'
if not os.path.exists(dirname): if not os.path.exists(dirname):
os.makedirs(dirname) os.makedirs(dirname)
cache_file = os.path.join(dirname, model_file_name) cache_file = os.path.join(dirname, model_file_name)
if not os.path.exists(cache_file): if not os.path.exists(cache_file):
print(f"downloading face detection model from '{download_url}' to '{cache_file}'") print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
response = requests.get(download_url) response = requests.get(download_url)
with open(cache_file, "wb") as f: with open(cache_file, "wb") as f:
f.write(response.content) f.write(response.content)
if os.path.exists(cache_file): if os.path.exists(cache_file):
return cache_file return cache_file
return None return None
class PointOfInterest: class PointOfInterest:
def __init__(self, x, y, weight=1.0, size=10): def __init__(self, x, y, weight=1.0, size=10):
self.x = x self.x = x
self.y = y self.y = y
self.weight = weight self.weight = weight
self.size = size self.size = size
def bounding(self, size): def bounding(self, size):
return [ return [
self.x - size//2, self.x - size // 2,
self.y - size//2, self.y - size // 2,
self.x + size//2, self.x + size // 2,
self.y + size//2 self.y + size // 2
] ]
class Settings: class Settings:
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): 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):
self.crop_width = crop_width self.crop_width = crop_width
self.crop_height = crop_height self.crop_height = crop_height
self.corner_points_weight = corner_points_weight self.corner_points_weight = corner_points_weight
self.entropy_points_weight = entropy_points_weight self.entropy_points_weight = entropy_points_weight
self.face_points_weight = face_points_weight self.face_points_weight = face_points_weight
self.annotate_image = annotate_image self.annotate_image = annotate_image
self.destop_view_image = False self.destop_view_image = False
self.dnn_model_path = dnn_model_path self.dnn_model_path = dnn_model_path