Spaces:
Runtime error
Runtime error
File size: 4,076 Bytes
ac7cda5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
from enum import Enum
import numpy as np
from ..utils.load_model import load_model
from .blaze_face import BlazeFace
from .face_mesh import FaceMesh
class SizeMode(Enum):
DEFAULT = 0
SQUARE_LONG = 1
SQUARE_SHORT = 2
def _select_roi_size(
bbox: np.ndarray, image_size, size_mode: SizeMode # x1, y1, x2, y2 # w,h
):
"""Return the size of an ROI based on bounding box, image size and mode"""
width, height = bbox[2] - bbox[0], bbox[3] - bbox[1]
image_width, image_height = image_size
if size_mode == SizeMode.SQUARE_LONG:
long_size = max(width, height)
width, height = long_size, long_size
elif size_mode == SizeMode.SQUARE_SHORT:
short_side = min(width, height)
width, height = short_side, short_side
return width, height
def bbox_to_roi(
bbox: np.ndarray,
image_size, # w,h
rotation_keypoints=None,
scale=(1.0, 1.0), # w, h
size_mode: SizeMode = SizeMode.SQUARE_LONG,
):
PI = np.pi
TWO_PI = 2 * np.pi
# select ROI dimensions
width, height = _select_roi_size(bbox, image_size, size_mode)
scale_x, scale_y = scale
# calculate ROI size and -centre
width, height = width * scale_x, height * scale_y
cx = (bbox[0] + bbox[2]) / 2
cy = (bbox[1] + bbox[3]) / 2
# calculate rotation of required
if rotation_keypoints is None or len(rotation_keypoints) < 2:
return np.array([cx, cy, width, height, 0])
x0, y0 = rotation_keypoints[0]
x1, y1 = rotation_keypoints[1]
angle = -np.atan2(y0 - y1, x1 - x0)
# normalise to [0, 2*PI]
rotation = angle - TWO_PI * np.floor((angle + PI) / TWO_PI)
return np.array([cx, cy, width, height, rotation])
class Landmark478:
def __init__(self, blaze_face_model_path="", face_mesh_model_path="", device="cuda", **kwargs):
if kwargs.get("force_ori_type", False):
assert "task_path" in kwargs
kwargs["module_name"] = "Landmark478"
kwargs["package_name"] = "..aux_models.modules"
self.model, self.model_type = load_model("", device=device, **kwargs)
else:
self.blaze_face = BlazeFace(blaze_face_model_path, device)
self.face_mesh = FaceMesh(face_mesh_model_path, device)
self.model_type = ""
def get(self, image):
bboxes = self.blaze_face(image)
if len(bboxes) == 0:
return None
bbox = bboxes[0]
scale = (image.shape[1] / 128.0, image.shape[0] / 128.0)
# The first 4 numbers describe the bounding box corners:
#
# ymin, xmin, ymax, xmax
# These are normalized coordinates (between 0 and 1).
# The next 12 numbers are the x,y-coordinates of the 6 facial landmark keypoints:
#
# right_eye_x, right_eye_y
# left_eye_x, left_eye_y
# nose_x, nose_y
# mouth_x, mouth_y
# right_ear_x, right_ear_y
# left_ear_x, left_ear_y
# Tip: these labeled as seen from the perspective of the person, so their right is your left.
# The final number is the confidence score that this detection really is a face.
bbox[0] = bbox[0] * scale[1]
bbox[1] = bbox[1] * scale[0]
bbox[2] = bbox[2] * scale[1]
bbox[3] = bbox[3] * scale[0]
left_eye = (bbox[4], bbox[5])
right_eye = (bbox[6], bbox[7])
roi = bbox_to_roi(
bbox,
(image.shape[1], image.shape[0]),
rotation_keypoints=[left_eye, right_eye],
scale=(1.5, 1.5),
size_mode=SizeMode.SQUARE_LONG,
)
mesh = self.face_mesh(image, roi)
mesh = mesh / (image.shape[1], image.shape[0], image.shape[1])
return mesh
def __call__(self, image):
if self.model_type == "ori":
det = self.model.detect_from_npimage(image.copy())
lmk = self.model.mplmk_to_nplmk(det)
return lmk
else:
lmk = self.get(image)
lmk = lmk.reshape(1, -1, 3).astype(np.float32)
return lmk
|