| import cv2 |
| import numpy as np |
|
|
| 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) |
|
|
| input = img[None, :, :, :] |
| outNames = session.getUnconnectedOutLayersNames() |
| session.setInput(input) |
| output = session.forward(outNames) |
|
|
| 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 None: |
| return 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 |
|
|
