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Simplify and strengthen rotate_facial_area
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@ -123,10 +123,12 @@ def detect_faces(
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aligned_img, angle = detection.align_face(
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img=img, left_eye=left_eye, right_eye=right_eye
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)
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x1_new, y1_new, x2_new, y2_new = rotate_facial_area(
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facial_area=(x2, y2, x2 + w2, y2 + h2), angle=angle, direction=1, size=img.shape
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rotated_x1, rotated_y1, rotated_x2, rotated_y2 = rotate_facial_area(
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facial_area=(x, y, x + w, y + h),
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angle=angle,
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size=(img.shape[0], img.shape[1])
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)
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detected_face = aligned_img[int(y1_new) : int(y2_new), int(x1_new) : int(x2_new)]
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detected_face = aligned_img[int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)]
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result = DetectedFace(
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img=detected_face,
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@ -140,7 +142,9 @@ def detect_faces(
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def rotate_facial_area(
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facial_area: Tuple[int, int, int, int], angle: float, direction: int, size: Tuple[int, int]
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facial_area: Tuple[int, int, int, int],
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angle: float, # in degrees. The sign determines the direction of rotation
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size: Tuple[int, int] # (width, height)
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) -> Tuple[int, int, int, int]:
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"""
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Rotate the facial area around its center.
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@ -149,14 +153,22 @@ def rotate_facial_area(
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Args:
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facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
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x2 is equal to x1 + w1, and y2 is equal to y1 + h1
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angle (float): Angle of rotation in degrees.
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direction (int): Direction of rotation (-1 for clockwise, 1 for counterclockwise).
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angle (float): Angle of rotation in degrees. Its sign determines the direction of rotation.
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Note that angles > 360 degrees are normalized to the range [0, 360).
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size (tuple of int): Tuple representing the size of the image (width, height).
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Returns:
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rotated_coordinates (tuple of int): Representing the new coordinates
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(x1, y1, x2, y2) or (x1, y1, x1+w1, y1+h1) of the rotated facial area.
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"""
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# Normalize the witdh of the angle so we don't have to
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# worry about rotations greater than 360 degrees
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angle = angle % 360
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if angle == 0:
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return facial_area # No rotation needed
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direction = 1 if angle > 0 else -1
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# Angle in radians
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angle = angle * np.pi / 180
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