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from __future__ import division
import numpy as np
import cv2
from ..utils.load_model import load_model
def distance2bbox(points, distance, max_shape=None):
"""Decode distance prediction to bounding box.
Args:
points (Tensor): Shape (n, 2), [x, y].
distance (Tensor): Distance from the given point to 4
boundaries (left, top, right, bottom).
max_shape (tuple): Shape of the image.
Returns:
Tensor: Decoded bboxes.
"""
x1 = points[:, 0] - distance[:, 0]
y1 = points[:, 1] - distance[:, 1]
x2 = points[:, 0] + distance[:, 2]
y2 = points[:, 1] + distance[:, 3]
if max_shape is not None:
x1 = x1.clamp(min=0, max=max_shape[1])
y1 = y1.clamp(min=0, max=max_shape[0])
x2 = x2.clamp(min=0, max=max_shape[1])
y2 = y2.clamp(min=0, max=max_shape[0])
return np.stack([x1, y1, x2, y2], axis=-1)
def distance2kps(points, distance, max_shape=None):
"""Decode distance prediction to bounding box.
Args:
points (Tensor): Shape (n, 2), [x, y].
distance (Tensor): Distance from the given point to 4
boundaries (left, top, right, bottom).
max_shape (tuple): Shape of the image.
Returns:
Tensor: Decoded bboxes.
"""
preds = []
for i in range(0, distance.shape[1], 2):
px = points[:, i%2] + distance[:, i]
py = points[:, i%2+1] + distance[:, i+1]
if max_shape is not None:
px = px.clamp(min=0, max=max_shape[1])
py = py.clamp(min=0, max=max_shape[0])
preds.append(px)
preds.append(py)
return np.stack(preds, axis=-1)
class InsightFaceDet:
def __init__(self, model_path, device="cuda", **kwargs):
kwargs["model_file"] = model_path
kwargs["module_name"] = "RetinaFace"
kwargs["package_name"] = "..aux_models.modules"
self.model, self.model_type = load_model(model_path, device=device, **kwargs)
self.device = device
if self.model_type != "ori":
self._init_vars()
def _init_vars(self):
self.center_cache = {}
self.nms_thresh = 0.4
self.det_thresh = 0.5
self.input_size = (512, 512)
self.input_mean = 127.5
self.input_std = 128.0
self._anchor_ratio = 1.0
self.fmc = 3
self._feat_stride_fpn = [8, 16, 32]
self._num_anchors = 2
self.use_kps = True
self.output_names = [
"scores1",
"scores2",
"scores3",
"boxes1",
"boxes2",
"boxes3",
"kps1",
"kps2",
"kps3",
]
def _run_model(self, blob):
if self.model_type == "onnx":
net_outs = self.model.run(None, {"image": blob})
elif self.model_type == "tensorrt":
self.model.setup({"image": blob})
self.model.infer()
net_outs = [self.model.buffer[name][0] for name in self.output_names]
else:
raise ValueError(f"Unsupported model type: {self.model_type}")
return net_outs
def _forward(self, img, threshold):
"""
img: np.ndarray, shape (h, w, 3)
"""
scores_list = []
bboxes_list = []
kpss_list = []
input_size = tuple(img.shape[0:2][::-1])
blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
# (1, 3, 512, 512)
net_outs = self._run_model(blob)
input_height = blob.shape[2]
input_width = blob.shape[3]
fmc = self.fmc
for idx, stride in enumerate(self._feat_stride_fpn):
scores = net_outs[idx]
bbox_preds = net_outs[idx+fmc]
bbox_preds = bbox_preds * stride
if self.use_kps:
kps_preds = net_outs[idx+fmc*2] * stride
height = input_height // stride
width = input_width // stride
# K = height * width
key = (height, width, stride)
if key in self.center_cache:
anchor_centers = self.center_cache[key]
else:
#solution-3:
anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
anchor_centers = (anchor_centers * stride).reshape( (-1, 2) )
if self._num_anchors>1:
anchor_centers = np.stack([anchor_centers]*self._num_anchors, axis=1).reshape( (-1,2) )
if len(self.center_cache)<100:
self.center_cache[key] = anchor_centers
pos_inds = np.where(scores>=threshold)[0]
bboxes = distance2bbox(anchor_centers, bbox_preds)
pos_scores = scores[pos_inds]
pos_bboxes = bboxes[pos_inds]
scores_list.append(pos_scores)
bboxes_list.append(pos_bboxes)
if self.use_kps:
kpss = distance2kps(anchor_centers, kps_preds)
kpss = kpss.reshape( (kpss.shape[0], -1, 2) )
pos_kpss = kpss[pos_inds]
kpss_list.append(pos_kpss)
return scores_list, bboxes_list, kpss_list
def detect(self, img, input_size=None, max_num=0, metric='default', det_thresh=None):
input_size = self.input_size if input_size is None else input_size
det_thresh = self.det_thresh if det_thresh is None else det_thresh
im_ratio = float(img.shape[0]) / img.shape[1]
model_ratio = float(input_size[1]) / input_size[0]
if im_ratio>model_ratio:
new_height = input_size[1]
new_width = int(new_height / im_ratio)
else:
new_width = input_size[0]
new_height = int(new_width * im_ratio)
det_scale = float(new_height) / img.shape[0]
resized_img = cv2.resize(img, (new_width, new_height))
det_img = np.zeros( (input_size[1], input_size[0], 3), dtype=np.uint8 )
det_img[:new_height, :new_width, :] = resized_img
scores_list, bboxes_list, kpss_list = self._forward(det_img, det_thresh)
scores = np.vstack(scores_list)
scores_ravel = scores.ravel()
order = scores_ravel.argsort()[::-1]
bboxes = np.vstack(bboxes_list) / det_scale
if self.use_kps:
kpss = np.vstack(kpss_list) / det_scale
pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False)
pre_det = pre_det[order, :]
keep = self.nms(pre_det)
det = pre_det[keep, :]
if self.use_kps:
kpss = kpss[order,:,:]
kpss = kpss[keep,:,:]
else:
kpss = None
if max_num > 0 and det.shape[0] > max_num:
area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
img_center = img.shape[0] // 2, img.shape[1] // 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]
])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
if metric=='max':
values = area
else:
values = area - offset_dist_squared * 2.0 # some extra weight on the centering
bindex = np.argsort(values)[::-1] # some extra weight on the centering
bindex = bindex[0:max_num]
det = det[bindex, :]
if kpss is not None:
kpss = kpss[bindex, :]
return det, kpss
def nms(self, dets):
thresh = self.nms_thresh
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
def __call__(self, img, **kwargs):
if self.model_type == "ori":
det, kpss = self.model.detect(img, **kwargs)
else:
det, kpss = self.detect(img, **kwargs)
return det, kpss
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