Example #1
0
def get_model():
    pretrain_model = "/Users/yangjiang/temp/gpu1/FaceBoxes.pth"
    num_classes = CONFIG["num_classes"]
    params = torch.load(pretrain_model, map_location="cpu")
    model = FaceBoxes(num_classes, "test")
    model.load_state_dict(params)
    model.eval()
    return model
 def __init__(self, **opt):
     net = FaceBoxes(phase='test', size=None, num_classes=2)  # initialize detector
     net = load_model(net, opt.get("weights", 'weights/FaceBoxes.pth'), True)
     net.eval()
     self.net = net
     self.top_k = opt.get('top_k', 5000)
     self.confidence_threshold = opt.get('confidence_threshold', 0.05)
     self.nms_threshold = opt.get('nms_threshold', 0.3)
     self.keep_top_k = opt.get('keep_top_k', 750)
 def load_face_model(self):
     torch.set_grad_enabled(False)
     # net and model
     net = FaceBoxes(phase='test', size=None,
                     num_classes=2)  # initialize detector
     net = load_model(net, self.args.trained_model, self.args.cpu)
     net.eval()
     cudnn.benchmark = True
     self.device = torch.device("cpu" if self.args.cpu else "cuda")
     self.net = net.to(self.device)
Example #4
0
class Detect(object):
    def __init__(self, path, device):
        self.net = FaceBoxes(phase='test', size=None, num_classes=2).to(device)
        self.net = load_model(self.net, path, False)
        self.net.eval()
        self.device = device

    def get_bbox(self, img_raw):
        img = torch.FloatTensor(img_raw).to(self.device)
        im_height, im_width, _ = img.size()
        scale = torch.FloatTensor([im_width, im_height, im_width,
                                   im_height]).to(self.device)
        img -= torch.FloatTensor((104, 117, 123)).to(self.device)
        img = img.permute(2, 0, 1).unsqueeze(0)

        loc, conf = self.net(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 > 0.05)[0]
        boxes = boxes[inds]
        scores = scores[inds]

        # keep top-K before NMS
        order = scores.argsort()[::-1][:5000]
        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(dets, 0.3, force_cpu=False)
        dets = dets[keep, :]

        # keep top-K faster NMS
        dets = dets[:750, :]
        bboxes = []
        for b in dets:
            if b[4] < 0.65:
                continue
            b = list(map(int, b))

            bboxes.append((b[0], b[1], b[2], b[3]))

        return bboxes
Example #5
0
def load_faceboxes():
    """
    Load FaceBoxes model and weight in pytorch
    """
    print('---------------------------------------------------')
    pretrained_path = 'weights/FaceBoxes.pth'
    net = FaceBoxes(phase='test', size=None,
                    num_classes=2)  # initialize detector
    net = load_model(net, pretrained_path)
    net.eval()
    print('Finished loading model')
    print('---------------------------------------------------')
    return net.cpu()
Example #6
0
        pretrained_dict = remove_prefix(pretrained_dict['state_dict'],
                                        'module.')
    else:
        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
    check_keys(model, pretrained_dict)
    model.load_state_dict(pretrained_dict, strict=False)
    return model


if __name__ == '__main__':
    torch.set_grad_enabled(False)
    # net and model
    net = FaceBoxes(phase='test', size=None,
                    num_classes=2)  # initialize detector
    net = load_model(net, args.trained_model, args.cpu)
    net.eval()
    print('Finished loading model!')
    print(net)
    cudnn.benchmark = True
    device = torch.device("cpu" if args.cpu else "cuda")
    net = net.to(device)

    # save file
    if not os.path.exists(args.save_folder):
        os.makedirs(args.save_folder)
    fw = open(os.path.join(args.save_folder, args.dataset + '_dets.txt'), 'w')

    # testing dataset
    testset_folder = os.path.join('data', args.dataset, 'images/')
    testset_list = os.path.join('data', args.dataset, 'img_list.txt')
    with open(testset_list, 'r') as fr:
Example #7
0
        chkpt = torch.load(ops.landmarks_model,
                           map_location=lambda storage, loc: storage)
        landmarks_model.load_state_dict(chkpt)
        landmarks_model.eval()  # 设置为前向推断模式
        print('load landmarks model : {}'.format(ops.landmarks_model))
        print(
            '\n/******************* landmarks model acc  ******************/')
        acc_model(ops, landmarks_model)
    landmarks_model = landmarks_model.to(device)

    #--------------------------------------------------------------------------- 构建人脸检测模型
    # detect_model
    detect_model = FaceBoxes(phase='test', size=None,
                             num_classes=2)  # initialize detector
    detect_model = load_model(detect_model, ops.detect_model, True)
    detect_model.eval()
    print('\n/******************* detect model acc  ******************/')
    acc_model(ops, detect_model)
    detect_model = detect_model.to(device)

    print('Finished loading model!')
    # print(detect_model)

    detect_model = detect_model.to(device)

    video_capture = cv2.VideoCapture(ops.test_path)

    resize = 1
    with torch.no_grad():
        idx = 0
        while True:
class FaceDetector(object):
    '''
    Class for face detection
    '''
    def __init__(self,
                 trained_model,
                 cpu=True,
                 nms_threshold=0.5,
                 top_k=1000,
                 confidence_threshold=0.8,
                 keep_top_k=10):
        super(FaceDetector, self).__init__()
        self.trained_model = trained_model
        self.net = FaceBoxes(phase='test', size=None, num_classes=2)
        self.net = load_model(self.net, trained_model, cpu)
        self.net.eval()
        print('Finished loading model', trained_model)

        self.device = torch.device("cpu" if cpu else "cuda")
        self.net = self.net.to(self.device)

        self.nms_threshold = nms_threshold
        self.top_k = top_k
        self.confidence_threshold = confidence_threshold
        self.keep_top_k = keep_top_k
        self.cpu = cpu

    def predict(self, img_name):
        img = np.float32(cv2.imread(img_name, cv2.IMREAD_COLOR))
        resize = 1
        if resize != 1:
            img = cv2.resize(img,
                             None,
                             None,
                             fx=resize,
                             fy=resize,
                             interpolation=cv2.INTER_LINEAR)
        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.to(self.device)
        scale = scale.to(self.device)

        _t = {'forward_pass': Timer(), 'misc': Timer()}
        _t['forward_pass'].tic()
        loc, conf = self.net(img)  # forward pass
        _t['forward_pass'].toc()
        _t['misc'].tic()
        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 / resize
        boxes = boxes.cpu().numpy()
        scores = conf.data.cpu().numpy()[:, 1]

        # ignore low scores
        inds = np.where(scores > self.confidence_threshold)[0]
        boxes = boxes[inds]
        scores = scores[inds]

        # keep top-K before NMS
        order = scores.argsort()[::-1][:self.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, self.nms_threshold)
        keep = nms(dets, self.nms_threshold, force_cpu=self.cpu)
        dets = dets[keep, :]

        # keep top-K faster NMS
        dets = dets[:self.keep_top_k, :]
        _t['misc'].toc()

        return dets
Example #9
0
    device = torch.cuda.current_device()
    pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
    if "state_dict" in pretrained_dict.keys():
        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
    else:
        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
    check_keys(model, pretrained_dict)
    model.load_state_dict(pretrained_dict, strict=False)
    return model


model = FaceBoxes(phase='test', size=None, num_classes=2)
device = torch.device("cuda")
model = load_model(model, trained_model_path)
model = model.to(device)
model.eval()

image_path = '/media/haoxue/WD/FaceBoxes.PyTorch/data/FDDB/images/2002/08/26/big/img_265.jpg'
img = np.float32(cv2.imread(image_path, cv2.IMREAD_COLOR))
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)
# if args.cuda:
img = img.cuda()
scale = scale.cuda()
out = model(img)
priorbox = PriorBox(cfg, out[2], (im_height, im_width), phase='test')
priors = priorbox.forward()
Example #10
0
def faceboxes(img_raw, cur_frame_counter):
    img = np.float32(img_raw)
    torch.set_grad_enabled(False)
    # net and model
    net = FaceBoxes(phase='test', size=None,
                    num_classes=2)  # initialize detector
    net = load_model(net, args.trained_model, args.cpu)
    net.eval()
    # print('Finished loading model!')
    # print(net)
    cudnn.benchmark = True
    device = torch.device("cpu" if args.cpu else "cuda")
    net = net.to(device)

    # testing scale

    resize = 2

    _t = {'forward_pass': Timer(), 'misc': Timer()}
    # testing begin
    if resize != 1:
        img = cv2.resize(img,
                         None,
                         None,
                         fx=resize,
                         fy=resize,
                         interpolation=cv2.INTER_LINEAR)
    im_height, im_width, _ = img.shape
    scale = torch.Tensor(
        [img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
    # print(img)
    img -= (104, 117, 123)
    img = img.transpose(2, 0, 1)
    img = torch.from_numpy(img).unsqueeze(0)
    img = img.to(device)
    scale = scale.to(device)

    loc, conf = net(img)  # forward pass
    # print(loc.size(),conf.size())
    priorbox = PriorBox(cfg, image_size=(im_height, im_width))
    priors = priorbox.forward()
    priors = priors.to(device)
    prior_data = priors.data
    boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
    boxes = boxes * scale / resize
    boxes = boxes.cpu().numpy()
    scores = conf.squeeze(0).data.cpu().numpy()[:, 1]

    # ignore low scores
    inds = np.where(scores > args.confidence_threshold)[0]
    boxes = boxes[inds]
    scores = scores[inds]

    # keep top-K before NMS
    order = scores.argsort()[::-1][: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(dets, args.nms_threshold, force_cpu=args.cpu)
    dets = dets[keep, :]

    # keep top-K faster NMS
    dets = dets[:args.keep_top_k, :]
    _t['misc'].toc()

    outputs_useful = []
    for b in dets:
        output_traffic = {}
        if b[4] < args.vis_thres:
            continue
        b = list(map(int, b))
        (left, right, top, bottom) = (b[0], b[2], b[1], b[3])
        label_str = 'face'
        output_traffic[label_str] = [left, right, top, bottom]
        outputs_useful.append(output_traffic)

    # show image

    if args.show_image:
        for b in dets:
            if b[4] < args.vis_thres:
                continue
            text = "{:.4f}".format(b[4])
            b = list(map(int, b))
            cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
            cx = b[0]
            cy = b[1] + 12
            # cv2.putText(img_raw, text, (cx, cy),
            #             cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))

            cv2.imwrite('boxes/' + str(cur_frame_counter) + '.jpg', img_raw)

    return outputs_useful
class FaceBoxDetector:
    def __init__(self, min_score=0.9, use_gpu=True):
        # Minimum score to consider as a detection.
        self.score_min = min_score

        self.net = FaceBoxes(phase='test', size=None, num_classes=2)

        self.use_gpu = use_gpu

        self.logger = Logger()

    def load_model(self, path_to_model):
        self.logger.field('Loading pretrained model from', path_to_model)
        device = torch.cuda.current_device()
        pretrained_dict = torch.load(path_to_model, map_location=lambda storage, loc: storage.cuda(device))
        if "state_dict" in pretrained_dict.keys():
            pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.')
        else:
            pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
        self.check_keys(self.net, pretrained_dict)
        self.net.load_state_dict(pretrained_dict, strict=False)
        self.net.eval()

        if self.use_gpu:
            self.net.cuda()

    def detect(self, images) -> List[List[TrackingRegion]]:
        frames = []
        for img in images:
            img = np.float32(img)
            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)
            if self.use_gpu:
                img = img.cuda()
                scale = scale.cuda()
            out = self.net(img)
            face_regions = self.nms_process(out, scale, im_height, im_width)
            frames.append(face_regions)

        return frames

    def nms_process(self, network_output, scale, im_height, im_width) -> List[TrackingRegion]:
        priorbox = PriorBox(cfg, network_output[2], (im_height, im_width), phase='test')
        priors = priorbox.forward()
        if self.use_gpu:
            priors = priors.cuda()
        loc, conf, _ = network_output
        prior_data = priors.data
        boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
        boxes = boxes * scale
        boxes = boxes.cpu().numpy()
        scores = conf.data.cpu().numpy()[:, 1]

        # ignore low scores
        inds = np.where(scores > self.score_min)[0]
        boxes = boxes[inds]
        scores = scores[inds]

        # keep top-K before NMS, top_k = 5
        order = scores.argsort()[::-1][:5000]
        boxes = boxes[order]
        scores = scores[order]

        # do NMS
        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
        keep = nms(dets, 0.3, force_cpu=False)
        dets = dets[keep, :]

        # keep top-K faster NMS
        dets = dets[:750, :]

        regions = []

        for i in range(dets.shape[0]):
            face_region = TrackingRegion()
            face_region.set_rect(left=dets[i, 0], top=dets[i, 1], right=dets[i, 2], bottom=dets[i, 3])
            face_region.confidence = dets[i, 4]
            face_region.data["class_id"] = "face"
            regions.append(face_region)

        return regions

    def check_keys(self, model, pretrained_state_dict):
        ckpt_keys = set(pretrained_state_dict.keys())
        model_keys = set(model.state_dict().keys())
        used_pretrained_keys = model_keys & ckpt_keys
        unused_pretrained_keys = ckpt_keys - model_keys
        missing_keys = model_keys - ckpt_keys
        self.logger.field('Missing keys', len(missing_keys))
        self.logger.field('Unused checkpoint keys', len(unused_pretrained_keys))
        self.logger.field('Used keys', len(used_pretrained_keys))
        assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
        return True

    @staticmethod
    def remove_prefix(state_dict, prefix):
        """ Old style model is stored with all names of parameters sharing common prefix 'module.' """
        print('remove prefix \'{}\''.format(prefix))
        f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
        return {f(key): value for key, value in state_dict.items()}