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esenke
180
utils/utils_bbox.py
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180
utils/utils_bbox.py
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import numpy as np
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import torch
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from torchvision.ops import nms, boxes
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def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image):
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#-----------------------------------------------------------------#
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# 把y轴放前面是因为方便预测框和图像的宽高进行相乘
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#-----------------------------------------------------------------#
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box_yx = box_xy[..., ::-1]
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box_hw = box_wh[..., ::-1]
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input_shape = np.array(input_shape)
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image_shape = np.array(image_shape)
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if letterbox_image:
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#-----------------------------------------------------------------#
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# 这里求出来的offset是图像有效区域相对于图像左上角的偏移情况
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# new_shape指的是宽高缩放情况
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#-----------------------------------------------------------------#
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new_shape = np.round(image_shape * np.min(input_shape/image_shape))
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offset = (input_shape - new_shape)/2./input_shape
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scale = input_shape/new_shape
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box_yx = (box_yx - offset) * scale
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box_hw *= scale
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box_mins = box_yx - (box_hw / 2.)
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box_maxes = box_yx + (box_hw / 2.)
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boxes = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1)
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boxes *= np.concatenate([image_shape, image_shape], axis=-1)
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return boxes
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def decode_outputs(outputs, input_shape):
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grids = []
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strides = []
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hw = [x.shape[-2:] for x in outputs]
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#---------------------------------------------------#
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# outputs输入前代表每个特征层的预测结果
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# batch_size, 4 + 1 + num_classes, 80, 80 => batch_size, 4 + 1 + num_classes, 6400
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# batch_size, 5 + num_classes, 40, 40
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# batch_size, 5 + num_classes, 20, 20
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# batch_size, 4 + 1 + num_classes, 6400 + 1600 + 400 -> batch_size, 4 + 1 + num_classes, 8400
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# 堆叠后为batch_size, 8400, 5 + num_classes
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#---------------------------------------------------#
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outputs = torch.cat([x.flatten(start_dim=2) for x in outputs], dim=2).permute(0, 2, 1)
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#---------------------------------------------------#
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# 获得每一个特征点属于每一个种类的概率
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#---------------------------------------------------#
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outputs[:, :, 4:] = torch.sigmoid(outputs[:, :, 4:])
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for h, w in hw:
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#---------------------------#
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# 根据特征层的高宽生成网格点
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#---------------------------#
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grid_y, grid_x = torch.meshgrid([torch.arange(h), torch.arange(w)], indexing='ij')
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#---------------------------#
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# 1, 6400, 2
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# 1, 1600, 2
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# 1, 400, 2
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#---------------------------#
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grid = torch.stack((grid_x, grid_y), 2).view(1, -1, 2)
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shape = grid.shape[:2]
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grids.append(grid)
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strides.append(torch.full((shape[0], shape[1], 1), input_shape[0] / h))
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#---------------------------#
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# 将网格点堆叠到一起
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# 1, 6400, 2
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# 1, 1600, 2
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# 1, 400, 2
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#
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# 1, 8400, 2
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#---------------------------#
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grids = torch.cat(grids, dim=1).type(outputs.type())
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strides = torch.cat(strides, dim=1).type(outputs.type())
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#------------------------#
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# 根据网格点进行解码
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#------------------------#
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outputs[..., :2] = (outputs[..., :2] + grids) * strides
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outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides
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#-----------------#
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# 归一化
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#-----------------#
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outputs[..., [0,2]] = outputs[..., [0,2]] / input_shape[1]
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outputs[..., [1,3]] = outputs[..., [1,3]] / input_shape[0]
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return outputs
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def non_max_suppression(prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
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#----------------------------------------------------------#
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# 将预测结果的格式转换成左上角右下角的格式。
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# prediction [batch_size, num_anchors, 85]
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#----------------------------------------------------------#
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box_corner = prediction.new(prediction.shape)
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box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
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box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
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box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
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box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
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prediction[:, :, :4] = box_corner[:, :, :4]
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output = [None for _ in range(len(prediction))]
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#----------------------------------------------------------#
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# 对输入图片进行循环，一般只会进行一次
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#----------------------------------------------------------#
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for i, image_pred in enumerate(prediction):
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#----------------------------------------------------------#
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# 对种类预测部分取max。
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# class_conf [num_anchors, 1] 种类置信度
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# class_pred [num_anchors, 1] 种类
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#----------------------------------------------------------#
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class_conf, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True)
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#----------------------------------------------------------#
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# 利用置信度进行第一轮筛选
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#----------------------------------------------------------#
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conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze()
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if not image_pred.size(0):
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continue
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#-------------------------------------------------------------------------#
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# detections [num_anchors, 7]
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# 7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred
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#-------------------------------------------------------------------------#
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detections = torch.cat((image_pred[:, :5], class_conf, class_pred.float()), 1)
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detections = detections[conf_mask]
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nms_out_index = boxes.batched_nms(
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detections[:, :4],
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detections[:, 4] * detections[:, 5],
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detections[:, 6],
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nms_thres,
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)
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output[i] = detections[nms_out_index]
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# #------------------------------------------#
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# # 获得预测结果中包含的所有种类
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# #------------------------------------------#
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# unique_labels = detections[:, -1].cpu().unique()
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# if prediction.is_cuda:
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# unique_labels = unique_labels.cuda()
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# detections = detections.cuda()
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# for c in unique_labels:
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# #------------------------------------------#
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# # 获得某一类得分筛选后全部的预测结果
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# #------------------------------------------#
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# detections_class = detections[detections[:, -1] == c]
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# #------------------------------------------#
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# # 使用官方自带的非极大抑制会速度更快一些！
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# #------------------------------------------#
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# keep = nms(
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# detections_class[:, :4],
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# detections_class[:, 4] * detections_class[:, 5],
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# nms_thres
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# )
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# max_detections = detections_class[keep]
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# # # 按照存在物体的置信度排序
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# # _, conf_sort_index = torch.sort(detections_class[:, 4]*detections_class[:, 5], descending=True)
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# # detections_class = detections_class[conf_sort_index]
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# # # 进行非极大抑制
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# # max_detections = []
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# # while detections_class.size(0):
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# # # 取出这一类置信度最高的，一步一步往下判断，判断重合程度是否大于nms_thres，如果是则去除掉
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# # max_detections.append(detections_class[0].unsqueeze(0))
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# # if len(detections_class) == 1:
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# # break
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# # ious = bbox_iou(max_detections[-1], detections_class[1:])
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# # detections_class = detections_class[1:][ious < nms_thres]
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# # # 堆叠
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# # max_detections = torch.cat(max_detections).data
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# # Add max detections to outputs
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# output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))
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if output[i] is not None:
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output[i] = output[i].cpu().numpy()
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box_xy, box_wh = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]
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output[i][:, :4] = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
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return output
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