def get_suggested_focal_point(self):
with self.get_willow_image() as willow:
faces = willow.detect_faces()
if faces:
# Create a bounding box around all faces
left = min(face[0] for face in faces)
top = min(face[1] for face in faces)
right = max(face[2] for face in faces)
bottom = max(face[3] for face in faces)
focal_point = Rect(left, top, right, bottom)
else:
features = willow.detect_features()
if features:
# Create a bounding box around all features
left = min(feature[0] for feature in features)
top = min(feature[1] for feature in features)
right = max(feature[0] for feature in features)
bottom = max(feature[1] for feature in features)
focal_point = Rect(left, top, right, bottom)
else:
return None
# Add 20% to width and height and give it a minimum size
x, y = focal_point.centroid
width, height = focal_point.size
width *= 1.20
height *= 1.20
width = max(width, 100)
height = max(height, 100)
return Rect.from_point(x, y, width, height)
def get_suggested_focal_point(self):
with self.get_willow_image() as willow:
faces = willow.detect_faces()
if faces:
# Create a bounding box around all faces
left = min(face[0] for face in faces)
top = min(face[1] for face in faces)
right = max(face[2] for face in faces)
bottom = max(face[3] for face in faces)
focal_point = Rect(left, top, right, bottom)
else:
features = willow.detect_features()
if features:
# Create a bounding box around all features
left = min(feature[0] for feature in features)
top = min(feature[1] for feature in features)
right = max(feature[0] for feature in features)
bottom = max(feature[1] for feature in features)
focal_point = Rect(left, top, right, bottom)
else:
return None
# Add 20% to width and height and give it a minimum size
x, y = focal_point.centroid
width, height = focal_point.size
width *= 1.20
height *= 1.20
width = max(width, 100)
height = max(height, 100)
return Rect.from_point(x, y, width, height)
def get_focal_point(self):
if (
self.focal_point_x is not None
and self.focal_point_y is not None
and self.focal_point_width is not None
and self.focal_point_height is not None
):
return Rect.from_point(
self.focal_point_x, self.focal_point_y, self.focal_point_width, self.focal_point_height
)
def get_focal_point(self):
if self.focal_point_x is not None and \
self.focal_point_y is not None and \
self.focal_point_width is not None and \
self.focal_point_height is not None:
return Rect.from_point(
self.focal_point_x,
self.focal_point_y,
self.focal_point_width,
self.focal_point_height,
)
def get_suggested_focal_point(self, backend_name='default'):
backend = get_image_backend(backend_name)
image_file = self.file.file
# Make sure image is open and seeked to the beginning
image_file.open('rb')
image_file.seek(0)
# Load the image
image = backend.open_image(self.file.file)
image_data = backend.image_data_as_rgb(image)
# Make sure we have image data
# If the image is animated, image_data_as_rgb will return None
if image_data is None:
return
# Use feature detection to find a focal point
feature_detector = FeatureDetector(image.size, image_data[0],
image_data[1])
faces = feature_detector.detect_faces()
if faces:
# Create a bounding box around all faces
left = min(face.left for face in faces)
top = min(face.top for face in faces)
right = max(face.right for face in faces)
bottom = max(face.bottom for face in faces)
focal_point = Rect(left, top, right, bottom)
else:
features = feature_detector.detect_features()
if features:
# Create a bounding box around all features
left = min(feature[0] for feature in features)
top = min(feature[1] for feature in features)
right = max(feature[0] for feature in features)
bottom = max(feature[1] for feature in features)
focal_point = Rect(left, top, right, bottom)
else:
return None
# Add 20% to width and height and give it a minimum size
x, y = focal_point.centroid
width, height = focal_point.size
width *= 1.20
height *= 1.20
width = max(width, 100)
height = max(height, 100)
return Rect.from_point(x, y, width, height)
def get_suggested_focal_point(self, backend_name='default'):
backend = get_image_backend(backend_name)
image_file = self.file.file
# Make sure image is open and seeked to the beginning
image_file.open('rb')
image_file.seek(0)
# Load the image
image = backend.open_image(self.file.file)
image_data = backend.image_data_as_rgb(image)
# Make sure we have image data
# If the image is animated, image_data_as_rgb will return None
if image_data is None:
return
# Use feature detection to find a focal point
feature_detector = FeatureDetector(image.size, image_data[0], image_data[1])
faces = feature_detector.detect_faces()
if faces:
# Create a bounding box around all faces
left = min(face.left for face in faces)
top = min(face.top for face in faces)
right = max(face.right for face in faces)
bottom = max(face.bottom for face in faces)
focal_point = Rect(left, top, right, bottom)
else:
features = feature_detector.detect_features()
if features:
# Create a bounding box around all features
left = min(feature[0] for feature in features)
top = min(feature[1] for feature in features)
right = max(feature[0] for feature in features)
bottom = max(feature[1] for feature in features)
focal_point = Rect(left, top, right, bottom)
else:
return None
# Add 20% to width and height and give it a minimum size
x, y = focal_point.centroid
width, height = focal_point.size
width *= 1.20
height *= 1.20
width = max(width, 100)
height = max(height, 100)
return Rect.from_point(x, y, width, height)
def run(self, willow, image):
image_width, image_height = willow.get_size()
focal_point = image.get_focal_point()
# Get crop aspect ratio
crop_aspect_ratio = self.width / self.height
# Get crop max
crop_max_scale = min(image_width, image_height * crop_aspect_ratio)
crop_max_width = crop_max_scale
crop_max_height = crop_max_scale / crop_aspect_ratio
# Initialise crop width and height to max
crop_width = crop_max_width
crop_height = crop_max_height
# Use crop closeness to zoom in
if focal_point is not None:
# Get crop min
crop_min_scale = max(focal_point.width, focal_point.height * crop_aspect_ratio)
crop_min_width = crop_min_scale
crop_min_height = crop_min_scale / crop_aspect_ratio
# Sometimes, the focal point may be bigger than the image...
if not crop_min_scale >= crop_max_scale:
# Calculate max crop closeness to prevent upscaling
max_crop_closeness = max(
1 - (self.width - crop_min_width) / (crop_max_width - crop_min_width),
1 - (self.height - crop_min_height) / (crop_max_height - crop_min_height)
)
# Apply max crop closeness
crop_closeness = min(self.crop_closeness, max_crop_closeness)
if 1 >= crop_closeness >= 0:
# Get crop width and height
crop_width = crop_max_width + (crop_min_width - crop_max_width) * crop_closeness
crop_height = crop_max_height + (crop_min_height - crop_max_height) * crop_closeness
# Find focal point UV
if focal_point is not None:
fp_x, fp_y = focal_point.centroid
else:
# Fall back to positioning in the centre
fp_x = image_width / 2
fp_y = image_height / 2
fp_u = fp_x / image_width
fp_v = fp_y / image_height
# Position crop box based on focal point UV
crop_x = fp_x - (fp_u - 0.5) * crop_width
crop_y = fp_y - (fp_v - 0.5) * crop_height
# Convert crop box into rect
rect = Rect.from_point(crop_x, crop_y, crop_width, crop_height)
# Make sure the entire focal point is in the crop box
if focal_point is not None:
rect = rect.move_to_cover(focal_point)
# Don't allow the crop box to go over the image boundary
rect = rect.move_to_clamp(Rect(0, 0, image_width, image_height))
# Crop!
willow = willow.crop(rect.round())
# Get scale for resizing
# The scale should be the same for both the horizontal and
# vertical axes
aftercrop_width, aftercrop_height = willow.get_size()
scale = self.width / aftercrop_width
# Only resize if the image is too big
if scale < 1.0:
# Resize!
willow = willow.resize((self.width, self.height))
return willow
def test_from_point(self):
rect = Rect.from_point(100, 200, 50, 20)
self.assertEqual(rect, Rect(75, 190, 125, 210))
def test_from_point(self):
rect = Rect.from_point(100, 200, 50, 20)
self.assertEqual(rect, Rect(75, 190, 125, 210))
def run(self, willow, image):
image_width, image_height = willow.get_size()
focal_point = image.get_focal_point()
# Get crop aspect ratio
crop_aspect_ratio = self.width / self.height
# Get crop max
crop_max_scale = min(image_width, image_height * crop_aspect_ratio)
crop_max_width = crop_max_scale
crop_max_height = crop_max_scale / crop_aspect_ratio
# Initialise crop width and height to max
crop_width = crop_max_width
crop_height = crop_max_height
# Use crop closeness to zoom in
if focal_point is not None:
# Get crop min
crop_min_scale = max(focal_point.width,
focal_point.height * crop_aspect_ratio)
crop_min_width = crop_min_scale
crop_min_height = crop_min_scale / crop_aspect_ratio
# Sometimes, the focal point may be bigger than the image...
if not crop_min_scale >= crop_max_scale:
# Calculate max crop closeness to prevent upscaling
max_crop_closeness = max(
1 - (self.width - crop_min_width) /
(crop_max_width - crop_min_width),
1 - (self.height - crop_min_height) /
(crop_max_height - crop_min_height))
# Apply max crop closeness
crop_closeness = min(self.crop_closeness, max_crop_closeness)
if 1 >= crop_closeness >= 0:
# Get crop width and height
crop_width = crop_max_width + (
crop_min_width - crop_max_width) * crop_closeness
crop_height = crop_max_height + (
crop_min_height - crop_max_height) * crop_closeness
# Find focal point UV
if focal_point is not None:
fp_x, fp_y = focal_point.centroid
else:
# Fall back to positioning in the centre
fp_x = image_width / 2
fp_y = image_height / 2
fp_u = fp_x / image_width
fp_v = fp_y / image_height
# Position crop box based on focal point UV
crop_x = fp_x - (fp_u - 0.5) * crop_width
crop_y = fp_y - (fp_v - 0.5) * crop_height
# Convert crop box into rect
rect = Rect.from_point(crop_x, crop_y, crop_width, crop_height)
# Make sure the entire focal point is in the crop box
if focal_point is not None:
rect = rect.move_to_cover(focal_point)
# Don't allow the crop box to go over the image boundary
rect = rect.move_to_clamp(Rect(0, 0, image_width, image_height))
# Crop!
willow.crop(rect.round())
# Get scale for resizing
# The scale should be the same for both the horizontal and
# vertical axes
aftercrop_width, aftercrop_height = willow.get_size()
scale = self.width / aftercrop_width
# Only resize if the image is too big
if scale < 1.0:
# Resize!
willow.resize((self.width, self.height))
