import cv2 import numpy as np import onnxruntime def nms(boxes, scores, nms_thr): """Single class NMS implemented in Numpy.""" 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.""" 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): 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] return final_boxes