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Running
on
Zero
| import numpy as np | |
| import onnx, onnx2torch, cv2 | |
| import torch | |
| from insightface.utils import face_align | |
| class ArcFaceRecognizer: | |
| def __init__(self, model_file=None, device='cpu', dtype=torch.float32): | |
| assert model_file is not None | |
| self.model_file = model_file | |
| self.device = device | |
| self.dtype = dtype | |
| self.model = onnx2torch.convert(onnx.load(model_file)).to(device=device, dtype=dtype) | |
| for param in self.model.parameters(): | |
| param.requires_grad = False | |
| self.model.eval() | |
| self.input_mean = 127.5 | |
| self.input_std = 127.5 | |
| self.input_size = (112, 112) | |
| self.input_shape = ['None', 3, 112, 112] | |
| def get(self, img, face): | |
| aimg = face_align.norm_crop(img, landmark=face.kps, image_size=self.input_size[0]) | |
| face.embedding = self.get_feat(aimg).flatten() | |
| return face.embedding | |
| def compute_sim(self, feat1, feat2): | |
| from numpy.linalg import norm | |
| feat1 = feat1.ravel() | |
| feat2 = feat2.ravel() | |
| sim = np.dot(feat1, feat2) / (norm(feat1) * norm(feat2)) | |
| return sim | |
| def get_feat(self, imgs): | |
| if not isinstance(imgs, list): | |
| imgs = [imgs] | |
| input_size = self.input_size | |
| blob = cv2.dnn.blobFromImages(imgs, 1.0 / self.input_std, input_size, | |
| (self.input_mean, self.input_mean, self.input_mean), swapRB=True) | |
| blob_torch = torch.tensor(blob).to(device=self.device, dtype=self.dtype) | |
| net_out = self.model(blob_torch) | |
| return net_out[0].float().cpu() | |
| 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 FaceDetector: | |
| def __init__(self, model_file=None, dtype=torch.float32, device='cuda'): | |
| self.model_file = model_file | |
| self.taskname = 'detection' | |
| self.center_cache = {} | |
| self.nms_thresh = 0.4 | |
| self.det_thresh = 0.5 | |
| self.device = device | |
| self.dtype = dtype | |
| self.model = onnx2torch.convert(onnx.load(model_file)).to(device=device, dtype=dtype) | |
| for param in self.model.parameters(): | |
| param.requires_grad = False | |
| self.model.eval() | |
| input_shape = (320, 320) | |
| self.input_size = input_shape | |
| self.input_shape = input_shape | |
| self.input_mean = 127.5 | |
| self.input_std = 128.0 | |
| self._anchor_ratio = 1.0 | |
| self._num_anchors = 1 | |
| self.fmc = 3 | |
| self._feat_stride_fpn = [8, 16, 32] | |
| self._num_anchors = 2 | |
| self.use_kps = True | |
| self.det_thresh = 0.5 | |
| self.nms_thresh = 0.4 | |
| def forward(self, img, threshold): | |
| 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) | |
| blob_torch = torch.tensor(blob).to(device=self.device, dtype=self.dtype) | |
| net_outs_torch = self.model(blob_torch) | |
| # print(list(map(lambda x: x.shape, net_outs_torch))) | |
| net_outs = list(map(lambda x: x.float().cpu().numpy(), net_outs_torch)) | |
| 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-1, c style: | |
| # anchor_centers = np.zeros( (height, width, 2), dtype=np.float32 ) | |
| # for i in range(height): | |
| # anchor_centers[i, :, 1] = i | |
| # for i in range(width): | |
| # anchor_centers[:, i, 0] = i | |
| # solution-2: | |
| # ax = np.arange(width, dtype=np.float32) | |
| # ay = np.arange(height, dtype=np.float32) | |
| # xv, yv = np.meshgrid(np.arange(width), np.arange(height)) | |
| # anchor_centers = np.stack([xv, yv], axis=-1).astype(np.float32) | |
| # solution-3: | |
| anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) | |
| # print(anchor_centers.shape) | |
| 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 = 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'): | |
| assert input_size is not None or self.input_size is not None | |
| input_size = self.input_size if input_size is None else input_size | |
| 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, self.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 | |