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637
utils/utils_bbox.py
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637
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
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class DecodeBox():
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def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):
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super(DecodeBox, self).__init__()
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self.anchors = anchors
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self.num_classes = num_classes
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self.bbox_attrs = 5 + num_classes
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self.input_shape = input_shape
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#-----------------------------------------------------------#
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# 20x20的特征层对应的anchor是[116,90],[156,198],[373,326]
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# 40x40的特征层对应的anchor是[30,61],[62,45],[59,119]
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# 80x80的特征层对应的anchor是[10,13],[16,30],[33,23]
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#-----------------------------------------------------------#
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self.anchors_mask = anchors_mask
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def decode_box(self, inputs):
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outputs = []
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for i, input in enumerate(inputs):
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#-----------------------------------------------#
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# 输入的input一共有三个，他们的shape分别是
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# batch_size = 1
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# batch_size, 3 * (4 + 1 + 80), 20, 20
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# batch_size, 255, 40, 40
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# batch_size, 255, 80, 80
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#-----------------------------------------------#
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batch_size = input.size(0)
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input_height = input.size(2)
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input_width = input.size(3)
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#-----------------------------------------------#
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# 输入为640x640时
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# stride_h = stride_w = 32、16、8
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#-----------------------------------------------#
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stride_h = self.input_shape[0] / input_height
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stride_w = self.input_shape[1] / input_width
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#-------------------------------------------------#
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# 此时获得的scaled_anchors大小是相对于特征层的
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#-------------------------------------------------#
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scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in self.anchors[self.anchors_mask[i]]]
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#-----------------------------------------------#
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# 输入的input一共有三个，他们的shape分别是
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# batch_size, 3, 20, 20, 85
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# batch_size, 3, 40, 40, 85
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# batch_size, 3, 80, 80, 85
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#-----------------------------------------------#
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prediction = input.view(batch_size, len(self.anchors_mask[i]),
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self.bbox_attrs, input_height, input_width).permute(0, 1, 3, 4, 2).contiguous()
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#-----------------------------------------------#
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# 先验框的中心位置的调整参数
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#-----------------------------------------------#
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x = torch.sigmoid(prediction[..., 0])
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y = torch.sigmoid(prediction[..., 1])
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#-----------------------------------------------#
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# 先验框的宽高调整参数
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#-----------------------------------------------#
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w = torch.sigmoid(prediction[..., 2])
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h = torch.sigmoid(prediction[..., 3])
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#-----------------------------------------------#
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# 获得置信度，是否有物体
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#-----------------------------------------------#
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conf = torch.sigmoid(prediction[..., 4])
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#-----------------------------------------------#
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# 种类置信度
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#-----------------------------------------------#
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pred_cls = torch.sigmoid(prediction[..., 5:])
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FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
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LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
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#----------------------------------------------------------#
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# 生成网格，先验框中心，网格左上角
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# batch_size,3,20,20
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#----------------------------------------------------------#
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grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat(
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batch_size * len(self.anchors_mask[i]), 1, 1).view(x.shape).type(FloatTensor)
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grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat(
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batch_size * len(self.anchors_mask[i]), 1, 1).view(y.shape).type(FloatTensor)
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#----------------------------------------------------------#
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# 按照网格格式生成先验框的宽高
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# batch_size,3,20,20
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#----------------------------------------------------------#
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anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))
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anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))
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anchor_w = anchor_w.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(w.shape)
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anchor_h = anchor_h.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(h.shape)
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#----------------------------------------------------------#
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# 利用预测结果对先验框进行调整
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# 首先调整先验框的中心，从先验框中心向右下角偏移
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# 再调整先验框的宽高。
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# x 0 ~ 1 => 0 ~ 2 => -0.5, 1.5 => 负责一定范围的目标的预测
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# y 0 ~ 1 => 0 ~ 2 => -0.5, 1.5 => 负责一定范围的目标的预测
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# w 0 ~ 1 => 0 ~ 2 => 0 ~ 4 => 先验框的宽高调节范围为0~4倍
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# h 0 ~ 1 => 0 ~ 2 => 0 ~ 4 => 先验框的宽高调节范围为0~4倍
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#----------------------------------------------------------#
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pred_boxes = FloatTensor(prediction[..., :4].shape)
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pred_boxes[..., 0] = x.data * 2. - 0.5 + grid_x
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pred_boxes[..., 1] = y.data * 2. - 0.5 + grid_y
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pred_boxes[..., 2] = (w.data * 2) ** 2 * anchor_w
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pred_boxes[..., 3] = (h.data * 2) ** 2 * anchor_h
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#----------------------------------------------------------#
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# 将输出结果归一化成小数的形式
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#----------------------------------------------------------#
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_scale = torch.Tensor([input_width, input_height, input_width, input_height]).type(FloatTensor)
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output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale,
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conf.view(batch_size, -1, 1), pred_cls.view(batch_size, -1, self.num_classes)), -1)
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outputs.append(output.data)
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return outputs
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def yolo_correct_boxes(self, 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 non_max_suppression(self, 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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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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#----------------------------------------------------------#
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# 根据置信度进行预测结果的筛选
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#----------------------------------------------------------#
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image_pred = image_pred[conf_mask]
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class_conf = class_conf[conf_mask]
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class_pred = class_pred[conf_mask]
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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.float(), class_pred.float()), 1)
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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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# 筛选出一定区域内，属于同一种类得分最大的框
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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] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
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return output
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class DecodeBoxNP():
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def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):
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super(DecodeBoxNP, self).__init__()
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self.anchors = anchors
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self.num_classes = num_classes
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self.bbox_attrs = 5 + num_classes
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self.input_shape = input_shape
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self.anchors_mask = anchors_mask
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def sigmoid(self, x):
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return 1 / (1 + np.exp(-x))
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def decode_box(self, inputs):
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outputs = []
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for i, input in enumerate(inputs):
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batch_size = np.shape(input)[0]
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input_height = np.shape(input)[2]
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input_width = np.shape(input)[3]
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#-----------------------------------------------#
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# 输入为640x640时
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# stride_h = stride_w = 32、16、8
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#-----------------------------------------------#
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stride_h = self.input_shape[0] / input_height
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stride_w = self.input_shape[1] / input_width
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#-------------------------------------------------#
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# 此时获得的scaled_anchors大小是相对于特征层的
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#-------------------------------------------------#
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scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in self.anchors[self.anchors_mask[i]]]
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#-----------------------------------------------#
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# 输入的input一共有三个，他们的shape分别是
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# batch_size, 3, 20, 20, 85
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# batch_size, 3, 40, 40, 85
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# batch_size, 3, 80, 80, 85
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#-----------------------------------------------#
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prediction = np.transpose(np.reshape(input, (batch_size, len(self.anchors_mask[i]), self.bbox_attrs, input_height, input_width)), (0, 1, 3, 4, 2))
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#-----------------------------------------------#
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# 先验框的中心位置的调整参数
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#-----------------------------------------------#
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x = self.sigmoid(prediction[..., 0])
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y = self.sigmoid(prediction[..., 1])
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#-----------------------------------------------#
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# 先验框的宽高调整参数
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#-----------------------------------------------#
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w = self.sigmoid(prediction[..., 2])
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h = self.sigmoid(prediction[..., 3])
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#-----------------------------------------------#
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# 获得置信度，是否有物体
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#-----------------------------------------------#
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conf = self.sigmoid(prediction[..., 4])
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#-----------------------------------------------#
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# 种类置信度
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#-----------------------------------------------#
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pred_cls = self.sigmoid(prediction[..., 5:])
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#----------------------------------------------------------#
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# 生成网格，先验框中心，网格左上角
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# batch_size,3,20,20
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#----------------------------------------------------------#
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grid_x = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.linspace(0, input_width - 1, input_width), 0), input_height, axis=0), 0), batch_size * len(self.anchors_mask[i]), axis=0)
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grid_x = np.reshape(grid_x, np.shape(x))
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grid_y = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.linspace(0, input_height - 1, input_height), 0), input_width, axis=0).T, 0), batch_size * len(self.anchors_mask[i]), axis=0)
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grid_y = np.reshape(grid_y, np.shape(y))
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#----------------------------------------------------------#
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# 按照网格格式生成先验框的宽高
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# batch_size,3,20,20
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#----------------------------------------------------------#
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anchor_w = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.array(scaled_anchors)[:, 0], 0), batch_size, axis=0), -1), input_height * input_width, axis=-1)
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anchor_h = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.array(scaled_anchors)[:, 1], 0), batch_size, axis=0), -1), input_height * input_width, axis=-1)
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anchor_w = np.reshape(anchor_w, np.shape(w))
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anchor_h = np.reshape(anchor_h, np.shape(h))
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#----------------------------------------------------------#
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# 利用预测结果对先验框进行调整
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# 首先调整先验框的中心，从先验框中心向右下角偏移
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# 再调整先验框的宽高。
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# x 0 ~ 1 => 0 ~ 2 => -0.5, 1.5 => 负责一定范围的目标的预测
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# y 0 ~ 1 => 0 ~ 2 => -0.5, 1.5 => 负责一定范围的目标的预测
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# w 0 ~ 1 => 0 ~ 2 => 0 ~ 4 => 先验框的宽高调节范围为0~4倍
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# h 0 ~ 1 => 0 ~ 2 => 0 ~ 4 => 先验框的宽高调节范围为0~4倍
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#----------------------------------------------------------#
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pred_boxes = np.zeros(np.shape(prediction[..., :4]))
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pred_boxes[..., 0] = x * 2. - 0.5 + grid_x
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pred_boxes[..., 1] = y * 2. - 0.5 + grid_y
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pred_boxes[..., 2] = (w * 2) ** 2 * anchor_w
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pred_boxes[..., 3] = (h * 2) ** 2 * anchor_h
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#----------------------------------------------------------#
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# 将输出结果归一化成小数的形式
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#----------------------------------------------------------#
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_scale = np.array([input_width, input_height, input_width, input_height])
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output = np.concatenate([np.reshape(pred_boxes, (batch_size, -1, 4)) / _scale,
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np.reshape(conf, (batch_size, -1, 1)), np.reshape(pred_cls, (batch_size, -1, self.num_classes))], -1)
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outputs.append(output)
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return outputs
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def bbox_iou(self, box1, box2, x1y1x2y2=True):
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"""
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计算IOU
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"""
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if not x1y1x2y2:
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b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
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b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
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b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
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b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
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else:
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
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inter_rect_x1 = np.maximum(b1_x1, b2_x1)
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inter_rect_y1 = np.maximum(b1_y1, b2_y1)
|
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inter_rect_x2 = np.minimum(b1_x2, b2_x2)
|
||||
inter_rect_y2 = np.minimum(b1_y2, b2_y2)
|
||||
|
||||
inter_area = np.maximum(inter_rect_x2 - inter_rect_x1, 0) * \
|
||||
np.maximum(inter_rect_y2 - inter_rect_y1, 0)
|
||||
|
||||
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
|
||||
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
|
||||
|
||||
iou = inter_area / np.maximum(b1_area + b2_area - inter_area, 1e-6)
|
||||
|
||||
return iou
|
||||
|
||||
def yolo_correct_boxes(self, box_xy, box_wh, input_shape, image_shape, letterbox_image):
|
||||
#-----------------------------------------------------------------#
|
||||
# 把y轴放前面是因为方便预测框和图像的宽高进行相乘
|
||||
#-----------------------------------------------------------------#
|
||||
box_yx = box_xy[..., ::-1]
|
||||
box_hw = box_wh[..., ::-1]
|
||||
input_shape = np.array(input_shape)
|
||||
image_shape = np.array(image_shape)
|
||||
|
||||
if letterbox_image:
|
||||
#-----------------------------------------------------------------#
|
||||
# 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
|
||||
# new_shape指的是宽高缩放情况
|
||||
#-----------------------------------------------------------------#
|
||||
new_shape = np.round(image_shape * np.min(input_shape/image_shape))
|
||||
offset = (input_shape - new_shape)/2./input_shape
|
||||
scale = input_shape/new_shape
|
||||
|
||||
box_yx = (box_yx - offset) * scale
|
||||
box_hw *= scale
|
||||
|
||||
box_mins = box_yx - (box_hw / 2.)
|
||||
box_maxes = box_yx + (box_hw / 2.)
|
||||
boxes = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1)
|
||||
boxes *= np.concatenate([image_shape, image_shape], axis=-1)
|
||||
return boxes
|
||||
|
||||
def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
|
||||
#----------------------------------------------------------#
|
||||
# 将预测结果的格式转换成左上角右下角的格式。
|
||||
# prediction [batch_size, num_anchors, 85]
|
||||
#----------------------------------------------------------#
|
||||
box_corner = np.zeros_like(prediction)
|
||||
box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
|
||||
box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
|
||||
box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
|
||||
box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
|
||||
prediction[:, :, :4] = box_corner[:, :, :4]
|
||||
|
||||
output = [None for _ in range(len(prediction))]
|
||||
for i, image_pred in enumerate(prediction):
|
||||
#----------------------------------------------------------#
|
||||
# 对种类预测部分取max。
|
||||
# class_conf [num_anchors, 1] 种类置信度
|
||||
# class_pred [num_anchors, 1] 种类
|
||||
#----------------------------------------------------------#
|
||||
class_conf = np.max(image_pred[:, 5:5 + num_classes], 1, keepdims=True)
|
||||
class_pred = np.expand_dims(np.argmax(image_pred[:, 5:5 + num_classes], 1), -1)
|
||||
|
||||
#----------------------------------------------------------#
|
||||
# 利用置信度进行第一轮筛选
|
||||
#----------------------------------------------------------#
|
||||
conf_mask = np.squeeze((image_pred[:, 4] * class_conf[:, 0] >= conf_thres))
|
||||
|
||||
#----------------------------------------------------------#
|
||||
# 根据置信度进行预测结果的筛选
|
||||
#----------------------------------------------------------#
|
||||
image_pred = image_pred[conf_mask]
|
||||
class_conf = class_conf[conf_mask]
|
||||
class_pred = class_pred[conf_mask]
|
||||
if not np.shape(image_pred)[0]:
|
||||
continue
|
||||
#-------------------------------------------------------------------------#
|
||||
# detections [num_anchors, 7]
|
||||
# 7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred
|
||||
#-------------------------------------------------------------------------#
|
||||
detections = np.concatenate((image_pred[:, :5], class_conf, class_pred), 1)
|
||||
|
||||
#------------------------------------------#
|
||||
# 获得预测结果中包含的所有种类
|
||||
#------------------------------------------#
|
||||
unique_labels = np.unique(detections[:, -1])
|
||||
|
||||
for c in unique_labels:
|
||||
#------------------------------------------#
|
||||
# 获得某一类得分筛选后全部的预测结果
|
||||
#------------------------------------------#
|
||||
detections_class = detections[detections[:, -1] == c]
|
||||
|
||||
# 按照存在物体的置信度排序
|
||||
conf_sort_index = np.argsort(detections_class[:, 4] * detections_class[:, 5])[::-1]
|
||||
detections_class = detections_class[conf_sort_index]
|
||||
# 进行非极大抑制
|
||||
max_detections = []
|
||||
while np.shape(detections_class)[0]:
|
||||
# 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
|
||||
max_detections.append(detections_class[0:1])
|
||||
if len(detections_class) == 1:
|
||||
break
|
||||
ious = self.bbox_iou(max_detections[-1], detections_class[1:])
|
||||
detections_class = detections_class[1:][ious < nms_thres]
|
||||
# 堆叠
|
||||
max_detections = np.concatenate(max_detections, 0)
|
||||
|
||||
# Add max detections to outputs
|
||||
output[i] = max_detections if output[i] is None else np.concatenate((output[i], max_detections))
|
||||
|
||||
if output[i] is not None:
|
||||
output[i] = output[i]
|
||||
box_xy, box_wh = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]
|
||||
output[i][:, :4] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
|
||||
return output
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
#---------------------------------------------------#
|
||||
# 将预测值的每个特征层调成真实值
|
||||
#---------------------------------------------------#
|
||||
def get_anchors_and_decode(input, input_shape, anchors, anchors_mask, num_classes):
|
||||
#-----------------------------------------------#
|
||||
# input batch_size, 3 * (4 + 1 + num_classes), 20, 20
|
||||
#-----------------------------------------------#
|
||||
batch_size = input.size(0)
|
||||
input_height = input.size(2)
|
||||
input_width = input.size(3)
|
||||
|
||||
#-----------------------------------------------#
|
||||
# 输入为640x640时 input_shape = [640, 640] input_height = 20, input_width = 20
|
||||
# 640 / 20 = 32
|
||||
# stride_h = stride_w = 32
|
||||
#-----------------------------------------------#
|
||||
stride_h = input_shape[0] / input_height
|
||||
stride_w = input_shape[1] / input_width
|
||||
#-------------------------------------------------#
|
||||
# 此时获得的scaled_anchors大小是相对于特征层的
|
||||
# anchor_width, anchor_height / stride_h, stride_w
|
||||
#-------------------------------------------------#
|
||||
scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in anchors[anchors_mask[2]]]
|
||||
|
||||
#-----------------------------------------------#
|
||||
# batch_size, 3 * (4 + 1 + num_classes), 20, 20 =>
|
||||
# batch_size, 3, 5 + num_classes, 20, 20 =>
|
||||
# batch_size, 3, 20, 20, 4 + 1 + num_classes
|
||||
#-----------------------------------------------#
|
||||
prediction = input.view(batch_size, len(anchors_mask[2]),
|
||||
num_classes + 5, input_height, input_width).permute(0, 1, 3, 4, 2).contiguous()
|
||||
|
||||
#-----------------------------------------------#
|
||||
# 先验框的中心位置的调整参数
|
||||
#-----------------------------------------------#
|
||||
x = torch.sigmoid(prediction[..., 0])
|
||||
y = torch.sigmoid(prediction[..., 1])
|
||||
#-----------------------------------------------#
|
||||
# 先验框的宽高调整参数
|
||||
#-----------------------------------------------#
|
||||
w = torch.sigmoid(prediction[..., 2])
|
||||
h = torch.sigmoid(prediction[..., 3])
|
||||
#-----------------------------------------------#
|
||||
# 获得置信度，是否有物体 0 - 1
|
||||
#-----------------------------------------------#
|
||||
conf = torch.sigmoid(prediction[..., 4])
|
||||
#-----------------------------------------------#
|
||||
# 种类置信度 0 - 1
|
||||
#-----------------------------------------------#
|
||||
pred_cls = torch.sigmoid(prediction[..., 5:])
|
||||
|
||||
FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
|
||||
LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
|
||||
|
||||
#----------------------------------------------------------#
|
||||
# 生成网格，先验框中心，网格左上角
|
||||
# batch_size,3,20,20
|
||||
# range(20)
|
||||
# [
|
||||
# [0, 1, 2, 3 ……, 19],
|
||||
# [0, 1, 2, 3 ……, 19],
|
||||
# …… （20次）
|
||||
# [0, 1, 2, 3 ……, 19]
|
||||
# ] * (batch_size * 3)
|
||||
# [batch_size, 3, 20, 20]
|
||||
#
|
||||
# [
|
||||
# [0, 1, 2, 3 ……, 19],
|
||||
# [0, 1, 2, 3 ……, 19],
|
||||
# …… （20次）
|
||||
# [0, 1, 2, 3 ……, 19]
|
||||
# ].T * (batch_size * 3)
|
||||
# [batch_size, 3, 20, 20]
|
||||
#----------------------------------------------------------#
|
||||
grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat(
|
||||
batch_size * len(anchors_mask[2]), 1, 1).view(x.shape).type(FloatTensor)
|
||||
grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat(
|
||||
batch_size * len(anchors_mask[2]), 1, 1).view(y.shape).type(FloatTensor)
|
||||
|
||||
#----------------------------------------------------------#
|
||||
# 按照网格格式生成先验框的宽高
|
||||
# batch_size, 3, 20 * 20 => batch_size, 3, 20, 20
|
||||
# batch_size, 3, 20 * 20 => batch_size, 3, 20, 20
|
||||
#----------------------------------------------------------#
|
||||
anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))
|
||||
anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))
|
||||
anchor_w = anchor_w.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(w.shape)
|
||||
anchor_h = anchor_h.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(h.shape)
|
||||
|
||||
#----------------------------------------------------------#
|
||||
# 利用预测结果对先验框进行调整
|
||||
# 首先调整先验框的中心，从先验框中心向右下角偏移
|
||||
# 再调整先验框的宽高。
|
||||
# x 0 ~ 1 => 0 ~ 2 => -0.5 ~ 1.5 + grid_x
|
||||
# y 0 ~ 1 => 0 ~ 2 => -0.5 ~ 1.5 + grid_y
|
||||
# w 0 ~ 1 => 0 ~ 2 => 0 ~ 4 * anchor_w
|
||||
# h 0 ~ 1 => 0 ~ 2 => 0 ~ 4 * anchor_h
|
||||
#----------------------------------------------------------#
|
||||
pred_boxes = FloatTensor(prediction[..., :4].shape)
|
||||
pred_boxes[..., 0] = x.data * 2. - 0.5 + grid_x
|
||||
pred_boxes[..., 1] = y.data * 2. - 0.5 + grid_y
|
||||
pred_boxes[..., 2] = (w.data * 2) ** 2 * anchor_w
|
||||
pred_boxes[..., 3] = (h.data * 2) ** 2 * anchor_h
|
||||
|
||||
point_h = 5
|
||||
point_w = 5
|
||||
|
||||
box_xy = pred_boxes[..., 0:2].cpu().numpy() * 32
|
||||
box_wh = pred_boxes[..., 2:4].cpu().numpy() * 32
|
||||
grid_x = grid_x.cpu().numpy() * 32
|
||||
grid_y = grid_y.cpu().numpy() * 32
|
||||
anchor_w = anchor_w.cpu().numpy() * 32
|
||||
anchor_h = anchor_h.cpu().numpy() * 32
|
||||
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(121)
|
||||
from PIL import Image
|
||||
img = Image.open("img/street.jpg").resize([640, 640])
|
||||
plt.imshow(img, alpha=0.5)
|
||||
plt.ylim(-30, 650)
|
||||
plt.xlim(-30, 650)
|
||||
plt.scatter(grid_x, grid_y)
|
||||
plt.scatter(point_h * 32, point_w * 32, c='black')
|
||||
plt.gca().invert_yaxis()
|
||||
|
||||
anchor_left = grid_x - anchor_w / 2
|
||||
anchor_top = grid_y - anchor_h / 2
|
||||
|
||||
rect1 = plt.Rectangle([anchor_left[0, 0, point_h, point_w],anchor_top[0, 0, point_h, point_w]], \
|
||||
anchor_w[0, 0, point_h, point_w],anchor_h[0, 0, point_h, point_w],color="r",fill=False)
|
||||
rect2 = plt.Rectangle([anchor_left[0, 1, point_h, point_w],anchor_top[0, 1, point_h, point_w]], \
|
||||
anchor_w[0, 1, point_h, point_w],anchor_h[0, 1, point_h, point_w],color="r",fill=False)
|
||||
rect3 = plt.Rectangle([anchor_left[0, 2, point_h, point_w],anchor_top[0, 2, point_h, point_w]], \
|
||||
anchor_w[0, 2, point_h, point_w],anchor_h[0, 2, point_h, point_w],color="r",fill=False)
|
||||
|
||||
ax.add_patch(rect1)
|
||||
ax.add_patch(rect2)
|
||||
ax.add_patch(rect3)
|
||||
|
||||
ax = fig.add_subplot(122)
|
||||
plt.imshow(img, alpha=0.5)
|
||||
plt.ylim(-30, 650)
|
||||
plt.xlim(-30, 650)
|
||||
plt.scatter(grid_x, grid_y)
|
||||
plt.scatter(point_h * 32, point_w * 32, c='black')
|
||||
plt.scatter(box_xy[0, :, point_h, point_w, 0], box_xy[0, :, point_h, point_w, 1], c='r')
|
||||
plt.gca().invert_yaxis()
|
||||
|
||||
pre_left = box_xy[...,0] - box_wh[...,0] / 2
|
||||
pre_top = box_xy[...,1] - box_wh[...,1] / 2
|
||||
|
||||
rect1 = plt.Rectangle([pre_left[0, 0, point_h, point_w], pre_top[0, 0, point_h, point_w]],\
|
||||
box_wh[0, 0, point_h, point_w,0], box_wh[0, 0, point_h, point_w,1],color="r",fill=False)
|
||||
rect2 = plt.Rectangle([pre_left[0, 1, point_h, point_w], pre_top[0, 1, point_h, point_w]],\
|
||||
box_wh[0, 1, point_h, point_w,0], box_wh[0, 1, point_h, point_w,1],color="r",fill=False)
|
||||
rect3 = plt.Rectangle([pre_left[0, 2, point_h, point_w], pre_top[0, 2, point_h, point_w]],\
|
||||
box_wh[0, 2, point_h, point_w,0], box_wh[0, 2, point_h, point_w,1],color="r",fill=False)
|
||||
|
||||
ax.add_patch(rect1)
|
||||
ax.add_patch(rect2)
|
||||
ax.add_patch(rect3)
|
||||
|
||||
plt.show()
|
||||
#
|
||||
feat = torch.from_numpy(np.random.normal(0.2, 0.5, [4, 255, 20, 20])).float()
|
||||
anchors = np.array([[116, 90], [156, 198], [373, 326], [30,61], [62,45], [59,119], [10,13], [16,30], [33,23]])
|
||||
anchors_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
|
||||
get_anchors_and_decode(feat, [640, 640], anchors, anchors_mask, 80)
|
||||
Reference in New Issue
Block a user