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face_detector.py
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face_detector.py
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import torch
import onnx
import onnxruntime
import numpy as np
from itertools import product as product
from math import ceil
import cv2
class FaceDetector(object):
def __init__(self):
self.model_path = r'model/FaceDetector.onnx'
self.onnx_model = onnx.load(self.model_path)
onnx.checker.check_model(self.onnx_model)
self.ort_session = onnxruntime.InferenceSession(self.model_path)
self.cfg = self.config()
self.conf_threshold = 0.5
self.top_k = 5000
self.nms_threshold = 0.4
self.keep_top_k = 750
self.vis_threshold = 0.6
self.image_size = (640, 640)
def run(self, image):
ori_height, ori_width = image.shape[:2]
processed_image, scale, img_height, img_width = self.preprocess(image)
ort_inputs = {self.ort_session.get_inputs()[0].name: self.to_numpy(processed_image)}
locations, confidences, landmarks = self.ort_session.run(None, ort_inputs)
detections, landmarks = self.postprocess(processed_image, locations, confidences, landmarks, scale, img_height,
img_width)
detections[:, 0] = detections[:, 0] * ori_width / self.image_size[0]
detections[:, 1] = detections[:, 1] * ori_height / self.image_size[1]
detections[:, 2] = detections[:, 2] * ori_width / self.image_size[0]
detections[:, 3] = detections[:, 3] * ori_height / self.image_size[1]
return detections, landmarks
def show_result(self, image, detections):
for d in detections:
if self.vis_threshold > d[4]:
continue
image = cv2.rectangle(image, (int(d[0]), int(d[1])), (int(d[2]), int(d[3])), (0, 0, 255), 2)
cv2.imshow('', image)
cv2.waitKey(1)
def preprocess(self, image):
image = cv2.resize(image, self.image_size)
img = np.float32(image)
img_height, img_width, _ = img.shape
scale = torch.Tensor([img_width, img_height, img_width, img_height])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.detach()
img = img.to('cuda')
scale = scale.to('cuda')
return img, scale, img_height, img_width
def postprocess(self, image, locations, confidences, landmarks, scale, img_height, img_width):
priorbox = PriorBox(self.cfg, image_size=self.image_size)
priors = priorbox.forward()
priors = priors.to('cuda')
resize = 1
prior_data = priors.data
locations = torch.from_numpy(locations).to('cuda')
confidences = torch.from_numpy(confidences).to('cuda')
landmarks = torch.from_numpy(landmarks).to('cuda')
boxes = self.decode(locations.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = confidences.squeeze(0).data.cpu().numpy()[:, 1]
landmarks = self.decode_landmarks(landmarks.data.squeeze(0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([image.shape[3], image.shape[2], image.shape[3], image.shape[2],
image.shape[3], image.shape[2], image.shape[3], image.shape[2],
image.shape[3], image.shape[2]])
scale1 = scale1.to('cuda')
landmarks = landmarks * scale1 / resize
landmarks = landmarks.cpu().numpy()
inds = np.where(scores > self.conf_threshold)[0]
boxes = boxes[inds]
landmarks = landmarks[inds]
scores = scores[inds]
order = scores.argsort()[::-1][:self.top_k]
boxes = boxes[order]
landmarks = landmarks[order]
scores = scores[order]
detections = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = self.py_cpu_nms(detections, self.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
detections = detections[keep, :]
landmarks = landmarks[keep]
# keep top-K faster NMS
detections = detections[:self.keep_top_k, :]
landmarks = landmarks[:self.keep_top_k, :]
# detections = np.concatenate((detections, landmarks), axis=1)
return detections, landmarks
def decode(self, loc, priors, variances):
boxes = torch.cat((
priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
boxes[:, :2] -= boxes[:, 2:] / 2
boxes[:, 2:] += boxes[:, :2]
return boxes
def decode_landmarks(self, pre, priors, variances):
landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
), dim=1)
return landms
def config(self):
cfg = {
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
}
return cfg
def to_numpy(self, tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
def py_cpu_nms(self, dets, thresh):
"""Pure Python NMS baseline."""
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
class PriorBox(object):
def __init__(self, cfg, image_size=None):
super(PriorBox, self).__init__()
self.min_sizes = cfg['min_sizes']
self.steps = cfg['steps']
self.clip = cfg['clip']
self.image_size = image_size
self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
self.name = "s"
def forward(self):
anchors = []
for k, f in enumerate(self.feature_maps):
min_sizes = self.min_sizes[k]
for i, j in product(range(f[0]), range(f[1])):
for min_size in min_sizes:
s_kx = min_size / self.image_size[1]
s_ky = min_size / self.image_size[0]
dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
for cy, cx in product(dense_cy, dense_cx):
anchors += [cx, cy, s_kx, s_ky]
# back to torch land
output = torch.Tensor(anchors).view(-1, 4)
if self.clip:
output.clamp_(max=1, min=0)
return output
if __name__ == '__main__':
image = cv2.imread('data/1.jpg')
handle = FaceDetector()
handle.run(image)