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import os |
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import cv2 |
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import numpy as np |
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def mkdir(path): |
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if not os.path.exists(path): |
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os.makedirs(path) |
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def nms(boxes, scores, nms_thr): |
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"""Single class NMS implemented in Numpy.""" |
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x1 = boxes[:, 0] |
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y1 = boxes[:, 1] |
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x2 = boxes[:, 2] |
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y2 = boxes[:, 3] |
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areas = (x2 - x1 + 1) * (y2 - y1 + 1) |
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order = scores.argsort()[::-1] |
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keep = [] |
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while order.size > 0: |
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i = order[0] |
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keep.append(i) |
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xx1 = np.maximum(x1[i], x1[order[1:]]) |
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yy1 = np.maximum(y1[i], y1[order[1:]]) |
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xx2 = np.minimum(x2[i], x2[order[1:]]) |
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yy2 = np.minimum(y2[i], y2[order[1:]]) |
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w = np.maximum(0.0, xx2 - xx1 + 1) |
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h = np.maximum(0.0, yy2 - yy1 + 1) |
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inter = w * h |
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ovr = inter / (areas[i] + areas[order[1:]] - inter) |
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inds = np.where(ovr <= nms_thr)[0] |
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order = order[inds + 1] |
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return keep |
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def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True): |
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"""Multiclass NMS implemented in Numpy""" |
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if class_agnostic: |
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nms_method = multiclass_nms_class_agnostic |
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else: |
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nms_method = multiclass_nms_class_aware |
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return nms_method(boxes, scores, nms_thr, score_thr) |
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def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr): |
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"""Multiclass NMS implemented in Numpy. Class-aware version.""" |
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final_dets = [] |
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num_classes = scores.shape[1] |
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for cls_ind in range(num_classes): |
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cls_scores = scores[:, cls_ind] |
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valid_score_mask = cls_scores > score_thr |
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if valid_score_mask.sum() == 0: |
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continue |
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else: |
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valid_scores = cls_scores[valid_score_mask] |
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valid_boxes = boxes[valid_score_mask] |
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keep = nms(valid_boxes, valid_scores, nms_thr) |
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if len(keep) > 0: |
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cls_inds = np.ones((len(keep), 1)) * cls_ind |
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dets = np.concatenate( |
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[valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 |
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) |
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final_dets.append(dets) |
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if len(final_dets) == 0: |
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return None |
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return np.concatenate(final_dets, 0) |
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def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr): |
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"""Multiclass NMS implemented in Numpy. Class-agnostic version.""" |
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cls_inds = scores.argmax(1) |
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cls_scores = scores[np.arange(len(cls_inds)), cls_inds] |
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valid_score_mask = cls_scores > score_thr |
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if valid_score_mask.sum() == 0: |
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return None |
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valid_scores = cls_scores[valid_score_mask] |
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valid_boxes = boxes[valid_score_mask] |
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valid_cls_inds = cls_inds[valid_score_mask] |
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keep = nms(valid_boxes, valid_scores, nms_thr) |
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if keep: |
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dets = np.concatenate( |
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[valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]], 1 |
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) |
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return dets |
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def demo_postprocess(outputs, img_size, p6=False): |
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grids = [] |
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expanded_strides = [] |
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if not p6: |
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strides = [8, 16, 32] |
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else: |
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strides = [8, 16, 32, 64] |
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hsizes = [img_size[0] // stride for stride in strides] |
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wsizes = [img_size[1] // stride for stride in strides] |
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for hsize, wsize, stride in zip(hsizes, wsizes, strides): |
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xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) |
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2) |
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grids.append(grid) |
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shape = grid.shape[:2] |
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expanded_strides.append(np.full((*shape, 1), stride)) |
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grids = np.concatenate(grids, 1) |
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expanded_strides = np.concatenate(expanded_strides, 1) |
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outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides |
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outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides |
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return outputs |
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def vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None): |
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for i in range(len(boxes)): |
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box = boxes[i] |
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cls_id = int(cls_ids[i]) |
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score = scores[i] |
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if score < conf: |
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continue |
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x0 = int(box[0]) |
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y0 = int(box[1]) |
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x1 = int(box[2]) |
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y1 = int(box[3]) |
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color = (_COLORS[cls_id] * 255).astype(np.uint8).tolist() |
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text = '{}:{:.1f}%'.format(class_names[cls_id], score * 100) |
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txt_color = (0, 0, 0) if np.mean(_COLORS[cls_id]) > 0.5 else (255, 255, 255) |
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font = cv2.FONT_HERSHEY_SIMPLEX |
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txt_size = cv2.getTextSize(text, font, 0.4, 1)[0] |
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cv2.rectangle(img, (x0, y0), (x1, y1), color, 2) |
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txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist() |
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cv2.rectangle( |
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img, |
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(x0, y0 + 1), |
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(x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])), |
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txt_bk_color, |
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-1 |
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) |
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cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1) |
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return img |
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_COLORS = np.array( |
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[ |
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0.000, 0.447, 0.741, |
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0.850, 0.325, 0.098, |
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0.929, 0.694, 0.125, |
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0.494, 0.184, 0.556, |
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0.466, 0.674, 0.188, |
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0.301, 0.745, 0.933, |
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0.635, 0.078, 0.184, |
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0.300, 0.300, 0.300, |
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0.600, 0.600, 0.600, |
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1.000, 0.000, 0.000, |
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1.000, 0.500, 0.000, |
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0.749, 0.749, 0.000, |
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0.000, 1.000, 0.000, |
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0.000, 0.000, 1.000, |
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0.667, 0.000, 1.000, |
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0.333, 0.333, 0.000, |
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0.333, 0.667, 0.000, |
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0.333, 1.000, 0.000, |
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0.667, 0.333, 0.000, |
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0.667, 0.667, 0.000, |
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0.667, 1.000, 0.000, |
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1.000, 0.333, 0.000, |
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1.000, 0.667, 0.000, |
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1.000, 1.000, 0.000, |
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0.000, 0.333, 0.500, |
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0.000, 0.667, 0.500, |
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0.000, 1.000, 0.500, |
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0.333, 0.000, 0.500, |
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0.333, 0.333, 0.500, |
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0.333, 0.667, 0.500, |
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0.333, 1.000, 0.500, |
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0.667, 0.000, 0.500, |
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0.667, 0.333, 0.500, |
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0.667, 0.667, 0.500, |
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0.667, 1.000, 0.500, |
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1.000, 0.000, 0.500, |
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1.000, 0.333, 0.500, |
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1.000, 0.667, 0.500, |
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1.000, 1.000, 0.500, |
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0.000, 0.333, 1.000, |
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0.000, 0.667, 1.000, |
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0.000, 1.000, 1.000, |
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0.333, 0.000, 1.000, |
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0.333, 0.333, 1.000, |
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0.333, 0.667, 1.000, |
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0.333, 1.000, 1.000, |
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0.667, 0.000, 1.000, |
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0.667, 0.333, 1.000, |
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0.667, 0.667, 1.000, |
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0.667, 1.000, 1.000, |
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1.000, 0.000, 1.000, |
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1.000, 0.333, 1.000, |
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1.000, 0.667, 1.000, |
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0.333, 0.000, 0.000, |
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0.500, 0.000, 0.000, |
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0.667, 0.000, 0.000, |
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0.833, 0.000, 0.000, |
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1.000, 0.000, 0.000, |
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0.000, 0.167, 0.000, |
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0.000, 0.333, 0.000, |
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0.000, 0.500, 0.000, |
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0.000, 0.667, 0.000, |
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0.000, 0.833, 0.000, |
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0.000, 1.000, 0.000, |
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0.000, 0.000, 0.167, |
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0.000, 0.000, 0.333, |
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0.000, 0.000, 0.500, |
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0.000, 0.000, 0.667, |
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0.000, 0.000, 0.833, |
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0.000, 0.000, 1.000, |
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0.000, 0.000, 0.000, |
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0.143, 0.143, 0.143, |
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0.286, 0.286, 0.286, |
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0.429, 0.429, 0.429, |
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0.571, 0.571, 0.571, |
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0.714, 0.714, 0.714, |
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0.857, 0.857, 0.857, |
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0.000, 0.447, 0.741, |
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0.314, 0.717, 0.741, |
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0.50, 0.5, 0 |
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] |
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).astype(np.float32).reshape(-1, 3) |
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