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detect_face.py
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detect_face.py
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import torch
import numpy as np
from data import cfg
from postprocessing.prior_box import PriorBox
from utils.nms import nms
from utils.box_utils import decode
labels = np.array(['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'])
class FaceDetector:
def __init__(self, path, args, device='cuda'):
self.device = device
self.model = torch.jit.load(path)
self.model.to(device).eval()
self.args = args
def detect_face(self, img_raw):
img = np.float32(img_raw)
im_height, im_width, _ = img.shape
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.cuda()
scale = scale.cuda()
loc, conf = self.model(img) # forward pass
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
boxes = boxes * scale
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
# ignore low scores
inds = np.where(scores > self.args.confidence_threshold)[0]
boxes = boxes[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:self.args.top_k]
boxes = boxes[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
# keep = py_cpu_nms(dets, args.nms_threshold)
keep = nms(torch.tensor(boxes), torch.tensor(scores), overlap=self.args.nms_threshold)
dets = dets[keep, :]
# keep top-K faster NMS
dets = dets[:self.args.keep_top_k, :]
return dets