portrait-analyser

Extract quantitative facial and dental measurements from iOS Portrait Mode photos (.heic/.heif) captured with Apple's TrueDepth camera.

Why?

iPhone Portrait Mode photos (TrueDepth front camera) carry more than a picture: the HEIC container also stores a depth map and Apple's own semantic segmentation mattes (teeth, skin) used for the bokeh effect. That data is normally locked away — consumer apps only ever show you the blurred photo. portrait-analyser unpacks the container and turns those extra layers into physical, reproducible measurements (incisor distance, mouth opening, neck circumference, jaw/chin position) instead of just pixels, which is useful for orthodontic/clinical tracking, research, or any workflow that needs quantitative facial metrics from a phone photo instead of specialized 3D-scanning hardware.

Features

• HEIC/HEIF parsing — decode the primary photo, depth map, and Apple semantic segmentation mattes (teeth, skin) out of the TrueDepth container, with EXIF-based validation that the source is actually a TrueDepth capture
• Face & eye detection — OpenCV Haar-cascade based, with coordinate translation between image regions
• Teeth & incisor analysis — teeth bounding box, incisor centroid detection, and 3D incisor distance computed from depth data (not just pixel distance)
• Mouth opening measurement — MediaPipe FaceMesh-based fallback for patients without visible upper teeth
• Neck & chin detection — three independent strategies depending on what's available: MediaPipe Pose (shoulder/nose interpolation), MediaPipe Selfie Segmentation (silhouette width profile), or a dual-mask approach combining the skin matte, depth map, and hair mask
• 3D neck circumference — dense arc integration over the depth map to estimate physical neck circumference, not just a 2D collar-line width
• Pose-invariant local landmarks — robust local-plane removal finds anatomical peaks and valleys without letting mild patient rotation choose the camera-nearest side of a patch
• Thyromental distance — physical chin-to-neck-midpoint measurement, a standard airway/intubation-difficulty screening metric
• CLI diagnostic tool (analyse-portrait) — inspect a HEIC file's raw container, EXIF, depth metadata, and segmentation mattes from the command line

Requirements

• Python 3.12 (see Supported versions)
• iOS Portrait Mode photos in HEIC/HEIF format, taken with a TrueDepth camera (e.g. iPhone 12, 14)

Installation

Using uv (recommended)

uv add portrait-analyser

Using pip

pip install portrait-analyser

Platform-specific HEIF support

• macOS -- works out of the box (uses pyheif-iplweb)
• Linux -- requires system packages before installing:

  sudo apt install libheif-dev libde265-dev
  

Supported versions

Python

Quick start

from portrait_analyser import load_image, get_face_parameters, find_neck_measurement_point

# Load an iOS Portrait Mode photo
portrait = load_image("photo.heic")

# portrait.photo       -- PIL Image of the photo
# portrait.depthmap    -- PIL Image of the depth map
# portrait.teethmap    -- PIL Image of the teeth segmentation mask (or None)
# portrait.skinmap     -- PIL Image of the skin segmentation mask (or None)

# Detect face and eyes
face = get_face_parameters(portrait.photo)
print(f"Face at ({face.x}, {face.y}), size {face.width}x{face.height}")
print(f"Eyes detected: {len(face.eyes)}")

# Measure neck width using the skin map
if portrait.skinmap is not None:
    neck = find_neck_measurement_point(portrait.skinmap, face)
    # Returns (x1, y1, x2, y2) of the narrowest horizontal line below the face

API reference

load_image(fileName, use_exif=True) -> IOSPortrait

Parses a HEIC/HEIF file and returns an IOSPortrait containing the photo, depth map, and Apple semantic segmentation masks. Validates TrueDepth EXIF data by default.

IOSPortrait

Attributes:

• photo -- primary PIL Image
• depthmap -- depth map as PIL Image
• teethmap -- teeth segmentation mask (PIL Image or None)
• skinmap -- skin segmentation mask (PIL Image or None)
• teeth_bbox -- bounding box (x, y, width, height) of detected teeth, or None
• incisor_distance -- incisor measurement as (x, y1, x, y2), or None
• floatValueMin, floatValueMax -- depth map float range from Apple metadata

Methods:

• teeth_bbox_translated(max_wi, max_he) -- scale teeth bounding box to a target resolution

get_face_parameters(image, raise_opencv_exceptions=False) -> Face

Detects a single face in a PIL Image using OpenCV Haar cascades. Raises NoFacesDetected or MultipleFacesDetected if not exactly one face is found.

Face and Eye

Both extend Rectangle (attributes: x, y, width, height, center_x, center_y).

Face:

• image -- reference to the source PIL Image
• eyes -- list of Eye instances (detected automatically)
• translate_coordinates(new_max_width, new_max_height) -- scale face coordinates to a target resolution
• calculate_percentage_of_image() -- returns (percent_width, percent_height)

Eye:

• face -- reference to the parent Face
• translate_coordinates(max_wi, max_he) -- absolute coordinates in a target resolution

Teeth & incisor utility functions

• find_neck_measurement_point(skinmap, face_location, threshold=200) -- finds the narrowest horizontal line below the face in the skin map. Returns (x1, y1, x2, y2).
• find_bounding_box_teeth(teethmap, margin_x=100, margin_y=100, min_value=200) -- finds the bounding box of teeth in the teeth map. Returns (x, y, width, height) or None.
• find_incisor_distance_teeth(teethmap, bounding_box_teeth, threshold=200, margin_x=0.5) -- measures the vertical pixel distance between upper and lower incisors. Returns (x, y1, x, y2) or None.
• find_incisor_centroids(teethmap, bounding_box_teeth, threshold=200, margin_x=0.5, min_pixels=50, centroid_margin_x=0.5, ...) -- finds robust representative points on the facing upper and lower incisal edges. The historical function/field names still use “centroid”, but returned points are snapped to real paired teeth-mask pixels so they measure the inter-incisal gap and provide valid locations for depth sampling. Returns ((upper_x, upper_y), (lower_x, lower_y)) in teethmap coordinates, or None.
• sample_depth_at_point(depthmap, point_x, point_y, photo_width, photo_height, kernel_size=3, support_mask=None, support_threshold=200, inward_y=0) -> int | None -- samples the depth map at a photo-space coordinate using median filtering over a kernel_size x kernel_size region. An optional foreground mask restricts sampling to the intended surface; inward_y moves an edge sample inward in native depth-map pixels.

3D depth conversion (incisor module)

• depth_raw_to_distance_cm(value, float_min, float_max) -> float | None -- converts a raw depth pixel value (0-255) to physical distance in centimeters, using Apple's disparity-based depth encoding.
• pixel_to_mm(pixel_coord, distance_cm, image_dimension) -> float | None -- converts a pixel coordinate (original, full-resolution image space) to physical millimeters at a given camera distance, via a calibration polynomial fitted to TrueDepth camera data. image_dimension is the full image width (for an x coordinate) or height (for a y coordinate), used to centre the conversion on the principal point.
• vector_length_3d(x1, y1, z1, x2, y2, z2) -> float -- Euclidean distance between two 3D points.
• compute_incisor_distance_3d(upper_centroid, lower_centroid, upper_depth_raw, lower_depth_raw, float_min, float_max, image_width, image_height) -> tuple[float, float, float] | None -- converts two incisor centroids + their raw depth values into physical mm/cm and returns (distance_3d_mm, upper_distance_cm, lower_distance_cm).

Mouth opening (mouth module)

• compute_mouth_measurement_from_facemesh(landmarks, depthmap, photo_w, photo_h, float_min, float_max) -> MouthMeasurement | None -- fallback mouth-opening measurement using MediaPipe FaceMesh outer lip landmarks (indices 0 and 17) and the depth map, for cases where teethmap-based incisor detection fails (e.g. no visible upper teeth).
• MouthMeasurement -- dataclass with upper_point, lower_point (photo-space pixels), upper_depth_raw, lower_depth_raw, upper_distance_cm, lower_distance_cm, distance_3d_mm.

Neck & chin detection (pose module — MediaPipe Pose)

• detect_neck_midpoint(image, interpolation_ratio=0.35, min_detection_confidence=0.5, min_visibility=0.5) -> tuple[NeckMidpoint | None, MediaPipeDebug | None, FaceMeshDebug | None] -- locates shoulders and nose via MediaPipe PoseLandmarker, then interpolates between the shoulder midpoint (neck base, ~C7/T1) and the nose to approximate the mid-cervical level (~C3-C4). FaceMesh detection runs independently, so FaceMeshDebug may be populated even when pose detection fails.
• NeckMidpoint -- dataclass with nose, mouth_left, mouth_right, chin, neck_extended (True when the neck appears maximally extended, detected via face-flattening ratio), face_flatness_ratio, pose, mouth_open_ratio, plus shoulder-dependent fields (x, y, left_shoulder, right_shoulder, visibilities, interpolation_ratio) that are None when only FaceMesh (not Pose) detected the face.
• PortraitPose, MediaPipeDebug, FaceMeshDebug -- raw MediaPipe landmark containers, useful for debug visualization.

Neck & chin detection (extended_neck module — segmentation-based)

• detect_neck_midpoint_from_segmentation(image, threshold=0.5, jaw_flare_fraction=0.15, smoothing_window=15) -> tuple[NeckMidpoint | None, SegmentationDebug | None] -- uses MediaPipe Selfie Segmentation to build a person silhouette, then analyzes the width profile to find the narrowest point (neck) and where the jaw flares out above it (chin).
• detect_neck_midpoint_from_dual_mask(image, skinmap, depthmap, hairmap=None, threshold=0.5, skin_threshold=30, float_min=None, float_max=None) -> tuple[NeckMidpoint | None, SegmentationDebug | None] -- combines the iOS skin matte, depth map, and (optional) hair mask: the chin is found as the closest-to-camera skin pixel, neck/shoulders from the depth width profile with hair removed.
• compute_neck_width_3d(depthmap, neck_y, neck_left_x, neck_right_x, photo_width, photo_height, float_min, float_max, n_samples=25) -> tuple[float | None, float | None] -- samples N evenly-spaced points across the neck row and converts them to 3D coordinates, returning front-arc length and straight-line width.
• SegmentationDebug -- dataclass exposing the binary mask, width profile, and detected neck/chin/shoulder/ear rows for debug visualization.

Neck circumference (neck module — 3D arc integration)

• compute_neck_circumference(skinmap, depthmap, photo_width, photo_height, float_min, float_max, face_location=None, n_samples=25, skin_threshold=30, circumference_multiplier=2.7, arc_sag=None, face=None, eyes=None, image_width=None, scan_start_y=None, scan_end_y=None, neck_midpoint_y=None, hairmap=None, hair_threshold=30) -> NeckMeasurement | None -- computes neck circumference by densely sampling the front arc of the neck (using the skin matte and depth map together) and extrapolating to a full circumference. It denoises the skin matte, removes semantic hair, re-reads the contiguous skin boundary at the actual arc-edge Y, and walks inward only across allowed skin until the depth profile stabilizes.
• find_stable_depth_x_from_edge(depthmap, edge_x, y, direction, photo_width, photo_height, max_distance, stability_run=4, valid_mask=None) -> int | None -- walks from a left (direction=1) or right (direction=-1) skin edge in native-depth-pixel steps and returns the centre of the first locally stable depth run. An optional mask prevents stabilization on background or hair.
• neck_search_bounds_from_face_landmarks(chin=..., nose=..., image_height=..., face_mesh_landmarks=None, pose_neck_y=None) -> tuple[int, int] -- starts below the lowest FaceMesh row and caps the search using visible face height. A Pose neck estimate may shorten this band but cannot extend it toward the shoulders.
• estimate_face_from_skinmap(skinmap, threshold=1) -> tuple[int, int, int, int] | None -- estimates a synthetic face bounding box from the skin segmentation map alone, for when no OpenCV face detection is available.
• NeckMeasurement -- dataclass with stable left_x, right_x sampling coordinates, original mask_left_x, mask_right_x silhouette coordinates, neck_y, arc_points_3d (physical mm coordinates), arc_points_photo (pixel coordinates, for overlay painting), the surface-polyline front_arc_length_mm, and its direct Euclidean front_chord_length_mm.
• Within an explicit MediaPipe search band, neck-row selection median-smooths the skin-width profile and chooses the first prominent local minimum rather than a later global minimum caused by a collar or shoulder matte dropout.

Pose-invariant local surface landmarks (local_surface module)

• score_local_surface_feature(x, y, z, valid, feature, radial_fraction=None, smoothing_size=5, center_bias=0.15) -> LocalSurfaceScores -- robustly fits and removes the dominant local 3D plane, median-smooths the residual, then ranks a SurfaceFeature.PEAK or SurfaceFeature.VALLEY. Lower scores are always better. An optional normalized radial distance weakly favours the user's clicked area without overriding a strong off-centre feature.
• LocalSurfaceScores -- contains the ranking score, detrended residual, fitted baseline, and final valid mask as NumPy arrays.

Thyromental distance (tmd module)

• compute_tmd_3d(chin_coord, neck_coord, chin_depth_raw, neck_depth_raw, float_min, float_max, image_width, image_height) -> tuple[float, float, float] | None -- computes the 3D physical distance between chin (mentum) and neck midpoint (a standard airway/intubation-difficulty screening measure), returning (distance_3d_mm, chin_z_cm, neck_z_cm).

Robust surface-distance measurement (depth_sampling module)

• median_filter_depthmap(depthmap, size=3) -> Image -- returns a same-size, single-channel median-filtered copy of a depth map, to be sampled once and reused across many points.
• bilinear_sample(image, x, y, invalid_value=None) -> float | None -- samples an image at fractional coordinates using bilinear interpolation; returns None if a contributing pixel equals invalid_value, instead of interpolating across holes.
• sample_points_along_line(x1, y1, x2, y2, step) -> Iterator[tuple[float, float]] -- evenly spaced points along a 2D line, always including both endpoints, independent of point order.
• sample_filtered_depth(filtered_depthmap, photo_x, photo_y, photo_width, photo_height) -> int | None -- bilinearly samples a pre-filtered depth map at a photo-space point; None over invalid (zero) disparity.
• measure_filtered_surface_length(filtered_depthmap, points_photo, photo_width, photo_height, float_min, float_max) -> float | None -- sums 3D Euclidean distance across consecutive photo-space points, sampling depth via sample_filtered_depth. Prefiltering + bilinear sampling smooths TrueDepth sensor noise before it can accumulate across many points walked along a surface, which matters for curved or long paths (e.g. compute_neck_circumference's neck arc, or a straight line drawn across a cheek). Returns None if fewer than 2 points were given or any point falls on invalid depth.

Exceptions

• UnknownExtension -- file is not .heic or .heif
• ExifValidationFailed -- EXIF data does not indicate a TrueDepth camera
• NoDepthMapFound -- HEIF container has no depth data
• NoFacesDetected -- no face found in image
• MultipleFacesDetected -- more than one face found

Development

# Clone and set up
git clone https://github.com/fidmaa/portrait-analyser.git
cd portrait-analyser
uv sync

# Run tests
uv run pytest

# Build package
uv build

Changelog

See CHANGELOG.md for release notes.

License

MIT