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| import cv2 | |
| import numpy as np | |
| def nms(boxes, scores, nms_thr): | |
| """Single class NMS implemented in Numpy. | |
| Args: | |
| boxes (np.ndarray): shape=(N,4); N is number of boxes | |
| scores (np.ndarray): the score of bboxes | |
| nms_thr (float): the threshold in NMS | |
| Returns: | |
| List[int]: output bbox ids | |
| """ | |
| x1 = boxes[:, 0] | |
| y1 = boxes[:, 1] | |
| x2 = boxes[:, 2] | |
| y2 = boxes[:, 3] | |
| 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 <= nms_thr)[0] | |
| order = order[inds + 1] | |
| return keep | |
| def multiclass_nms(boxes, scores, nms_thr, score_thr): | |
| """Multiclass NMS implemented in Numpy. Class-aware version. | |
| Args: | |
| boxes (np.ndarray): shape=(N,4); N is number of boxes | |
| scores (np.ndarray): the score of bboxes | |
| nms_thr (float): the threshold in NMS | |
| score_thr (float): the threshold of cls score | |
| Returns: | |
| np.ndarray: outputs bboxes coordinate | |
| """ | |
| final_dets = [] | |
| num_classes = scores.shape[1] | |
| for cls_ind in range(num_classes): | |
| cls_scores = scores[:, cls_ind] | |
| valid_score_mask = cls_scores > score_thr | |
| if valid_score_mask.sum() == 0: | |
| continue | |
| else: | |
| valid_scores = cls_scores[valid_score_mask] | |
| valid_boxes = boxes[valid_score_mask] | |
| keep = nms(valid_boxes, valid_scores, nms_thr) | |
| if len(keep) > 0: | |
| cls_inds = np.ones((len(keep), 1)) * cls_ind | |
| dets = np.concatenate( | |
| [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 | |
| ) | |
| final_dets.append(dets) | |
| if len(final_dets) == 0: | |
| return None | |
| return np.concatenate(final_dets, 0) | |
| def demo_postprocess(outputs, img_size, p6=False): | |
| grids = [] | |
| expanded_strides = [] | |
| strides = [8, 16, 32] if not p6 else [8, 16, 32, 64] | |
| hsizes = [img_size[0] // stride for stride in strides] | |
| wsizes = [img_size[1] // stride for stride in strides] | |
| for hsize, wsize, stride in zip(hsizes, wsizes, strides): | |
| xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) | |
| grid = np.stack((xv, yv), 2).reshape(1, -1, 2) | |
| grids.append(grid) | |
| shape = grid.shape[:2] | |
| expanded_strides.append(np.full((*shape, 1), stride)) | |
| grids = np.concatenate(grids, 1) | |
| expanded_strides = np.concatenate(expanded_strides, 1) | |
| outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides | |
| outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides | |
| return outputs | |
| def preprocess(img, input_size, swap=(2, 0, 1)): | |
| if len(img.shape) == 3: | |
| padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114 | |
| else: | |
| padded_img = np.ones(input_size, dtype=np.uint8) * 114 | |
| r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) | |
| resized_img = cv2.resize( | |
| img, | |
| (int(img.shape[1] * r), int(img.shape[0] * r)), | |
| interpolation=cv2.INTER_LINEAR, | |
| ).astype(np.uint8) | |
| padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img | |
| padded_img = padded_img.transpose(swap) | |
| padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) | |
| return padded_img, r | |
| def inference_detector(session, oriImg): | |
| """run human detect | |
| """ | |
| input_shape = (640,640) | |
| img, ratio = preprocess(oriImg, input_shape) | |
| ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]} | |
| output = session.run(None, ort_inputs) | |
| predictions = demo_postprocess(output[0], input_shape)[0] | |
| boxes = predictions[:, :4] | |
| scores = predictions[:, 4:5] * predictions[:, 5:] | |
| boxes_xyxy = np.ones_like(boxes) | |
| boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2. | |
| boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2. | |
| boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2. | |
| boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2. | |
| boxes_xyxy /= ratio | |
| dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1) | |
| if dets is not None: | |
| final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] | |
| isscore = final_scores>0.3 | |
| iscat = final_cls_inds == 0 | |
| isbbox = [ i and j for (i, j) in zip(isscore, iscat)] | |
| final_boxes = final_boxes[isbbox] | |
| else: | |
| final_boxes = np.array([]) | |
| return final_boxes | |