107 lines
4.0 KiB
Python
107 lines
4.0 KiB
Python
"""Framed Ft and Zy measurements shared by interface 42 and the ratio preview."""
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import math
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FT_POINTS = ("hair_line_right_right", "hair_line_left_left")
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ZY_POINTS = ("face_edge_234", "face_edge_454")
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class PortraitQualityError(ValueError):
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"""An input that must be rejected before feature analysis."""
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def __init__(self, code, message):
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super().__init__(message)
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self.code = code
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def prepare_feature_portrait(image_bytes):
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"""Check the source image before applying interface 52 framing."""
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import cv2
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import numpy as np
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from face_analysis.pose import PITCH_THRESHOLD, estimate_head_pose
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from hairline.face_landmarks import get_landmarker, to_landmark_list
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from hairline.framing import decode_portrait, normalize_portrait
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image = decode_portrait(image_bytes)
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if image is None:
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raise PortraitQualityError(1008, "图片格式不支持（仅 JPG / PNG）")
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landmarks = get_landmarker().detect(
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cv2.cvtColor(image, cv2.COLOR_BGR2RGB), include_iris=True
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)
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if landmarks is None:
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raise PortraitQualityError(1001, "无法识别人像，请上传包含完整人脸的照片")
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h, w = image.shape[:2]
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face_xy = landmarks[:468, :2] * [w, h]
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if not np.isfinite(face_xy).all() or (
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(face_xy <= 0).any() or (face_xy >= [w - 1, h - 1]).any()
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):
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raise PortraitQualityError(1003, "人脸不完整，请保留完整的脸部轮廓")
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pose = estimate_head_pose(to_landmark_list(landmarks), w, h)
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# In the camera coordinate system, positive pitch points the face down.
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if pose is not None and pose[1] > PITCH_THRESHOLD:
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raise PortraitQualityError(1003, "俯拍角度过大，请将相机调整至与脸部平视")
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try:
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image, landmarks, _ = normalize_portrait(image, landmarks)
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except ValueError as exc:
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raise PortraitQualityError(1003, "人脸过于贴近边缘，无法扶正裁切") from exc
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return image, landmarks
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def detect_framed_ratio_points(image):
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"""Use the interface 52 crop and return the endpoints in its coordinate space."""
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from hairline.framing import prepare_portrait
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image, landmarks, framing = prepare_portrait(image)
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data = _detect_ratio_points(image, landmarks)
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data.update(framing=framing, coordinate_space="processed_image")
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return image, data
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def _detect_ratio_points(image, landmarks):
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from hairline.face_landmarks import to_landmark_list
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from .calibration import normalized_to_pixel
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from .special_points import detect_special_points
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override = to_landmark_list(landmarks)
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data = detect_special_points(image, landmarks_override=override)
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points = data["points"]
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height, width = image.shape[:2]
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x, y = normalized_to_pixel(override.landmark[454], width, height)
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points["face_edge_454"] = {"x": round(x), "y": round(y)}
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data["ft_original_points"] = {key: points[key] for key in FT_POINTS}
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a, b = (points[key] for key in FT_POINTS)
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if a is not None and b is not None:
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middle_y = (a["y"] + b["y"]) / 2
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points[FT_POINTS[0]] = {**a, "y": middle_y}
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points[FT_POINTS[1]] = {**b, "y": middle_y}
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data["ft_alignment"] = "horizontal_at_mean_y"
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return data
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def analyze_ratio_features(image, landmarks) -> dict[str, bool | None]:
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"""Return both labels from the same measurement of the processed image."""
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from .special_points import NoFaceError, NonFrontalError
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matched = None
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try:
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data = _detect_ratio_points(image, landmarks)
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except (NoFaceError, NonFrontalError):
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pass
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else:
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ft_left, ft_right, zy_left, zy_right = (
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data["points"].get(key) for key in (*FT_POINTS, *ZY_POINTS)
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)
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if all(point is not None for point in (ft_left, ft_right, zy_left, zy_right)):
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ft = abs(ft_left["x"] - ft_right["x"])
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zy = math.hypot(zy_left["x"] - zy_right["x"], zy_left["y"] - zy_right["y"])
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if ft > 0 and zy > 0:
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matched = ft / zy < 0.8
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return {"zygoma_expansion": matched, "temple_hollowing": matched}
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