danvics/immich-automated-selfie-timelapse
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Use the initial immich face bounding box for landmark detection

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Arnaud_Cayrol 2025-04-11 17:55:19 +02:00
parent 248fb36e8d
commit 9a6b859f37
1 changed files with 55 additions and 21 deletions
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timelapse.py
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@ -252,14 +252,13 @@ def get_head_pose(shape, img_size):
return pitch, yaw, roll
def align_face(image, face_data, desired_face_width, desired_face_height, left_eye_pos, pose_threshold):
"""
Aligns the face in the image using facial landmarks and head pose estimation.
Args:
image (PIL.Image): The cropped image containing just the face.
face_data (dict): Metadata containing face bounding box info (not used for detection).
face_data (dict): Metadata containing face bounding box info.
desired_face_width (int): The desired output face width.
desired_face_height (int): The desired output face height.
left_eye_pos (tuple): The desired relative position of the left eye.
@ -271,20 +270,58 @@ def align_face(image, face_data, desired_face_width, desired_face_height, left_e
image_np = np.array(image)
gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
# Since we already have a cropped face image, create a rectangle for the whole image
img_height, img_width = gray.shape
rect = dlib.rectangle(0, 0, img_width, img_height)
# Get the original face bounding box from metadata and scale it to the cropped image
face_img_width = face_data.get("imageWidth")
face_img_height = face_data.get("imageHeight")
img_width, img_height = image.size
scale_x = img_width / face_img_width
scale_y = img_height / face_img_height
# Calculate the face rectangle in the cropped image coordinates
x1 = int(face_data.get("boundingBoxX1", 0) * scale_x)
x2 = int(face_data.get("boundingBoxX2", 0) * scale_x)
y1 = int(face_data.get("boundingBoxY1", 0) * scale_y)
y2 = int(face_data.get("boundingBoxY2", 0) * scale_y)
# Calculate optimal size for landmark detection (between 200-400px)
face_width = x2 - x1
face_height = y2 - y1
optimal_size = 300 # Target size for landmark detection
scale_factor = optimal_size / max(face_width, face_height)
# Resize the image for landmark detection
resized_width = int(img_width * scale_factor)
resized_height = int(img_height * scale_factor)
resized_gray = cv2.resize(gray, (resized_width, resized_height))
# Scale the face rectangle coordinates to the resized image
resized_x1 = int(x1 * scale_factor)
resized_x2 = int(x2 * scale_factor)
resized_y1 = int(y1 * scale_factor)
resized_y2 = int(y2 * scale_factor)
# Create dlib rectangle from the scaled face bounding box
rect = dlib.rectangle(resized_x1, resized_y1, resized_x2, resized_y2)
# Get facial landmarks
shape = face_predictor(gray, rect)
# Get facial landmarks on the resized image
shape = face_predictor(resized_gray, rect)
# Check if face landmarks were detected
if shape.num_parts != 68:
logger.info("Landmark detection failed - could not find all 68 facial landmarks.")
return None
# Get head pose
head_pose = get_head_pose(shape, (img_width, img_height))
# Scale the landmarks back to the original image coordinates
shape_np = np.array([(shape.part(i).x / scale_factor, shape.part(i).y / scale_factor)
for i in range(68)], dtype="float32")
# Get head pose using the original image size and scaled-back landmarks
# Create a new shape object with the scaled-back coordinates
scaled_shape = dlib.full_object_detection(
dlib.rectangle(0, 0, img_width, img_height),
[dlib.point(int(x), int(y)) for x, y in shape_np]
)
head_pose = get_head_pose(scaled_shape, (img_width, img_height))
if head_pose is None:
return None
@ -293,10 +330,7 @@ def align_face(image, face_data, desired_face_width, desired_face_height, left_e
logger.info(f"Face not frontal enough: pitch={pitch:.2f}, yaw={yaw:.2f}, roll={roll:.2f}. Discarding.")
# return None
# Convert landmarks to numpy array
shape_np = np.array([(shape.part(i).x, shape.part(i).y) for i in range(68)], dtype="int")
# Calculate eye centers
# Calculate eye centers using the scaled-back landmarks
left_eye_center = shape_np[36:42].mean(axis=0).astype("int")
right_eye_center = shape_np[42:48].mean(axis=0).astype("int")
@ -314,6 +348,7 @@ def align_face(image, face_data, desired_face_width, desired_face_height, left_e
eyes_center = ((left_eye_center[0] + right_eye_center[0]) / 2.0,
(left_eye_center[1] + right_eye_center[1]) / 2.0)
# Create transformation matrix
M = cv2.getRotationMatrix2D(eyes_center, angle, scale)
@ -323,6 +358,7 @@ def align_face(image, face_data, desired_face_width, desired_face_height, left_e
M[0, 2] += (tX - eyes_center[0])
M[1, 2] += (tY - eyes_center[1])
# Apply transformation
aligned_face_np = cv2.warpAffine(
image_np,
@ -362,14 +398,8 @@ def process_asset_worker(asset, config: ProcessConfig):
return None
matching_person = next((p for p in asset.get('people', []) if p.get('id') == config.person_id), None)
if not matching_person:
logger.info("Subject not in image.")
return None
faces = matching_person.get('faces', [])
if not faces:
logger.info("No face data available.")
return None
face_data = faces[0]
face_data = matching_person.get('faces', [])[0]
cropped_face = crop_face_from_metadata(image, face_data, config.padding_percent)
face_width, face_height = cropped_face.size
if face_width < config.min_face_width or face_height < config.min_face_height:
@ -395,6 +425,10 @@ def process_asset_worker(asset, config: ProcessConfig):
aligned_face.save(filename)
return filename
def draw_landmarks(image, shape_np):
for (x, y) in shape_np:
cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
return image
def process_faces(config: ProcessConfig, max_workers=1, progress_callback=None, date_from=None, date_to=None,
cancel_flag=None):