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from __future__ import division
import numpy as np
import torch
import cv2
from skimage import transform as trans
from ..utils.load_model import load_model
def transform(data, center, output_size, scale, rotation):
scale_ratio = scale
rot = float(rotation) * np.pi / 180.0
t1 = trans.SimilarityTransform(scale=scale_ratio)
cx = center[0] * scale_ratio
cy = center[1] * scale_ratio
t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
t3 = trans.SimilarityTransform(rotation=rot)
t4 = trans.SimilarityTransform(translation=(output_size / 2,
output_size / 2))
t = t1 + t2 + t3 + t4
M = t.params[0:2]
cropped = cv2.warpAffine(data,
M, (output_size, output_size),
borderValue=0.0)
return cropped, M
def trans_points2d(pts, M):
new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
for i in range(pts.shape[0]):
pt = pts[i]
new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
new_pt = np.dot(M, new_pt)
new_pts[i] = new_pt[0:2]
return new_pts
class Landmark106:
def __init__(self, model_path, device="cuda", **kwargs):
kwargs["model_file"] = model_path
kwargs["module_name"] = "Landmark106"
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.input_mean = 0.0
self.input_std = 1.0
self.input_size = (192, 192)
self.lmk_num = 106
self.output_names = ["fc1"]
def _run_model(self, blob):
if self.model_type == "onnx":
pred = self.model.run(None, {"data": blob})[0]
elif self.model_type == "tensorrt":
self.model.setup({"data": blob})
self.model.infer()
pred = self.model.buffer[self.output_names[0]][0]
else:
raise ValueError(f"Unsupported model type: {self.model_type}")
return pred
def get(self, img, bbox):
w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
rotate = 0
_scale = self.input_size[0] / (max(w, h)*1.5)
aimg, M = transform(img, center, self.input_size[0], _scale, rotate)
input_size = tuple(aimg.shape[0:2][::-1])
blob = cv2.dnn.blobFromImage(aimg, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
pred = self._run_model(blob)
pred = pred.reshape((-1, 2))
if self.lmk_num < pred.shape[0]:
pred = pred[self.lmk_num*-1:,:]
pred[:, 0:2] += 1
pred[:, 0:2] *= (self.input_size[0] // 2)
IM = cv2.invertAffineTransform(M)
pred = trans_points2d(pred, IM)
return pred
def __call__(self, img, bbox):
if self.model_type == "ori":
pred = self.model.get(img, bbox)
else:
pred = self.get(img, bbox)
return pred
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