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Merge pull request #1047 from AndreaLanfranchi/al20220227-rotate-facial-area

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Simplify and strengthen rotate_facial_area
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Sefik Ilkin Serengil 2024-02-27 11:07:52 +00:00 committed by GitHub
commit 87f4893205
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1 changed files with 33 additions and 18 deletions
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deepface/detectors/DetectorWrapper.py
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@ -103,30 +103,33 @@ def detect_faces(
right_eye = facial_area.right_eye
confidence = facial_area.confidence
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * 2 * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * 2 * expand_percentage) / 100)) # expand bottom
if expand_percentage > 0:
# Expand the facial region height and width by the provided percentage
# ensuring that the expanded region stays within img.shape limits
expanded_w = w + int(w * expand_percentage / 100)
expanded_h = h + int(h * expand_percentage / 100)
x = max(0, x - int((expanded_w - w) / 2))
y = max(0, y - int((expanded_h - h) / 2))
w = min(img.shape[1] - x, expanded_w)
h = min(img.shape[0] - y, expanded_h)
# extract detected face unaligned
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
# aligning detected face causes a lot of black pixels
# if align is True:
# detected_face, _ = detection.align_face(
# img=detected_face, left_eye=left_eye, right_eye=right_eye
# )
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
# align original image, then find projection of detected face area after alignment
if align is True: # and left_eye is not None and right_eye is not None:
aligned_img, angle = detection.align_face(
img=img, left_eye=left_eye, right_eye=right_eye
)
x1_new, y1_new, x2_new, y2_new = rotate_facial_area(
facial_area=(x2, y2, x2 + w2, y2 + h2), angle=angle, direction=1, size=img.shape
rotated_x1, rotated_y1, rotated_x2, rotated_y2 = rotate_facial_area(
facial_area=(x, y, x + w, y + h),
angle=angle,
size=(img.shape[0], img.shape[1])
)
detected_face = aligned_img[int(y1_new) : int(y2_new), int(x1_new) : int(x2_new)]
detected_face = aligned_img[
int(rotated_y1) : int(rotated_y2),
int(rotated_x1) : int(rotated_x2)]
result = DetectedFace(
img=detected_face,
@ -140,7 +143,9 @@ def detect_faces(
def rotate_facial_area(
facial_area: Tuple[int, int, int, int], angle: float, direction: int, size: Tuple[int, int]
facial_area: Tuple[int, int, int, int],
angle: float,
size: Tuple[int, int]
) -> Tuple[int, int, int, int]:
"""
Rotate the facial area around its center.
@ -149,14 +154,24 @@ def rotate_facial_area(
Args:
facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
x2 is equal to x1 + w1, and y2 is equal to y1 + h1
angle (float): Angle of rotation in degrees.
direction (int): Direction of rotation (-1 for clockwise, 1 for counterclockwise).
angle (float): Angle of rotation in degrees. Its sign determines the direction of rotation.
Note that angles > 360 degrees are normalized to the range [0, 360).
size (tuple of int): Tuple representing the size of the image (width, height).
Returns:
rotated_coordinates (tuple of int): Representing the new coordinates
(x1, y1, x2, y2) or (x1, y1, x1+w1, y1+h1) of the rotated facial area.
"""
# Normalize the witdh of the angle so we don't have to
# worry about rotations greater than 360 degrees.
# We workaround the quirky behavior of the modulo operator
# for negative angle values.
direction = 1 if angle >= 0 else -1
angle = abs(angle) % 360
if angle == 0:
return facial_area
# Angle in radians
angle = angle * np.pi / 180