Move code to calculate_eye_alignment_transform
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Arnaud_Cayrol
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1 changed files with 105 additions and 52 deletions
157
timelapse.py
157
timelapse.py
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@ -199,6 +199,7 @@ def download_asset(api_key, base_url, asset_id):
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def get_head_pose(landmarks, image):
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"""
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Estimates the head pose (pitch, yaw, roll) using facial landmarks.
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Based on https://learnopencv.com/head-pose-estimation-using-opencv-and-dlib/
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Args:
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landmarks (dict): Dictionary containing facial landmarks in numpy arrays.
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@ -363,6 +364,93 @@ def check_eye_visibility(left_eye, right_eye, ear_threshold=0.2) -> bool:
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return True
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def calculate_eye_alignment_transform(
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left_eye_center: np.ndarray,
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right_eye_center: np.ndarray,
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output_size: int,
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desired_left_eye_pos: Tuple[float, float]
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) -> np.ndarray:
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"""Calculate the transformation matrix to align eyes at desired positions.
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Args:
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left_eye_center: Center coordinates of the left eye
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right_eye_center: Center coordinates of the right eye
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output_size: Size of the output image (width and height)
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desired_left_eye_pos: Desired position of left eye as percentages (x, y)
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Returns:
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np.ndarray: 2x3 transformation matrix for cv2.warpAffine
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"""
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# Calculate the desired eye positions in the output image
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left_eye_target = np.array([
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output_size * desired_left_eye_pos[0],
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output_size * desired_left_eye_pos[1]
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])
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right_eye_target = np.array([
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output_size * (1.0 - desired_left_eye_pos[0]),
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output_size * desired_left_eye_pos[1]
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])
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# Calculate the angle between the current eye line and the target eye line
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current_angle = np.degrees(np.arctan2(
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right_eye_center[1] - left_eye_center[1],
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right_eye_center[0] - left_eye_center[0]
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))
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target_angle = np.degrees(np.arctan2(
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right_eye_target[1] - left_eye_target[1],
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right_eye_target[0] - left_eye_target[0]
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))
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rotation_angle = target_angle - current_angle
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# Calculate the scale factor to match the desired eye distance
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current_eye_distance = np.linalg.norm(right_eye_center - left_eye_center)
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target_eye_distance = np.linalg.norm(right_eye_target - left_eye_target)
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scale = target_eye_distance / current_eye_distance
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# Create the transformation matrix
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# First, translate to origin (center of eyes)
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center = np.array([
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(left_eye_center[0] + right_eye_center[0]) / 2,
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(left_eye_center[1] + right_eye_center[1]) / 2
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])
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M1 = np.array([
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[1, 0, -center[0]],
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[0, 1, -center[1]],
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[0, 0, 1]
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])
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# Then rotate
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angle_rad = np.radians(rotation_angle)
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M2 = np.array([
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[np.cos(angle_rad), -np.sin(angle_rad), 0],
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[np.sin(angle_rad), np.cos(angle_rad), 0],
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[0, 0, 1]
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])
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# Then scale
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M3 = np.array([
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[scale, 0, 0],
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[0, scale, 0],
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[0, 0, 1]
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])
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# Finally, translate to target position
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target_center = np.array([
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(left_eye_target[0] + right_eye_target[0]) / 2,
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(left_eye_target[1] + right_eye_target[1]) / 2
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])
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M4 = np.array([
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[1, 0, target_center[0]],
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[0, 1, target_center[1]],
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[0, 0, 1]
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])
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# Combine all transformations
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M = M4 @ M3 @ M2 @ M1
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# Convert to 2x3 matrix for OpenCV
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return M[:2, :]
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def crop_and_align_face(image, face_data, resize_size, face_resolution_threshold, pose_threshold, left_eye_pos):
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"""
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Aligns a face in an image by positioning the eyes at specified locations.
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@ -405,7 +493,7 @@ def crop_and_align_face(image, face_data, resize_size, face_resolution_threshold
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# Check if both eyes are visible
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if not check_eye_visibility(landmarks['left_eye'], landmarks['right_eye']):
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logger.info("Eyes are too closed")
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draw_landmarks(img_np, landmarks['all_landmarks'], (x1, y1, x2, y2), "discarded/side_face.jpg")
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draw_landmarks(img_np, landmarks['all_landmarks'], (x1, y1, x2, y2), "discarded/eyes_closed.jpg")
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return None
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# Get head pose
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@ -423,60 +511,25 @@ def crop_and_align_face(image, face_data, resize_size, face_resolution_threshold
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# return None
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# Get eye positions
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left_eye_center = np.array(np.mean(landmarks['left_eye'], axis=0))
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right_eye_center = np.array(np.mean(landmarks['right_eye'], axis=0))
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left_eye_center = np.mean(landmarks['left_eye'], axis=0)
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right_eye_center = np.mean(landmarks['right_eye'], axis=0)
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# Calculate the desired eye positions in the output image
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left_eye_target = np.array([resize_size * left_eye_pos[0], resize_size * left_eye_pos[1]])
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right_eye_target = np.array([resize_size * (1.0 - left_eye_pos[0]), resize_size * left_eye_pos[1]])
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# Calculate the angle between the current eye line and the target eye line
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current_angle = np.degrees(np.arctan2(right_eye_center[1] - left_eye_center[1],
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right_eye_center[0] - left_eye_center[0]))
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target_angle = np.degrees(np.arctan2(right_eye_target[1] - left_eye_target[1],
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right_eye_target[0] - left_eye_target[0]))
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rotation_angle = target_angle - current_angle
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# Calculate the scale factor to match the desired eye distance
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current_eye_distance = np.linalg.norm(right_eye_center - left_eye_center)
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target_eye_distance = np.linalg.norm(right_eye_target - left_eye_target)
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scale = target_eye_distance / current_eye_distance
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# Create the transformation matrix
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# First, translate to origin (center of eyes)
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center = np.array([(left_eye_center[0] + right_eye_center[0]) / 2,
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(left_eye_center[1] + right_eye_center[1]) / 2])
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M1 = np.array([[1, 0, -center[0]],
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[0, 1, -center[1]],
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[0, 0, 1]])
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# Then rotate
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angle_rad = np.radians(rotation_angle)
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M2 = np.array([[np.cos(angle_rad), -np.sin(angle_rad), 0],
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[np.sin(angle_rad), np.cos(angle_rad), 0],
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[0, 0, 1]])
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# Then scale
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M3 = np.array([[scale, 0, 0],
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[0, scale, 0],
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[0, 0, 1]])
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# Finally, translate to target position
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target_center = np.array([(left_eye_target[0] + right_eye_target[0]) / 2,
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(left_eye_target[1] + right_eye_target[1]) / 2])
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M4 = np.array([[1, 0, target_center[0]],
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[0, 1, target_center[1]],
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[0, 0, 1]])
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# Combine all transformations
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M = M4 @ M3 @ M2 @ M1
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# Convert to 2x3 matrix for OpenCV
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rotation_matrix = M[:2, :]
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# Calculate transformation matrix
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rotation_matrix = calculate_eye_alignment_transform(
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left_eye_center,
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right_eye_center,
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resize_size,
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left_eye_pos
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)
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# Apply transformation
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aligned_face = cv2.warpAffine(img_np, rotation_matrix, (resize_size, resize_size),
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flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
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aligned_face = cv2.warpAffine(
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img_np,
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rotation_matrix,
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(resize_size, resize_size),
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flags=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_REPLICATE
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)
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# Convert back to PIL Image
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aligned_face = cv2.cvtColor(aligned_face, cv2.COLOR_BGR2RGB)
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