181 lines
8.5 KiB
Python
181 lines
8.5 KiB
Python
import numpy as np
|
||
import torch
|
||
from torchvision.ops import nms, boxes
|
||
|
||
def yolo_correct_boxes(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 decode_outputs(outputs, input_shape):
|
||
grids = []
|
||
strides = []
|
||
hw = [x.shape[-2:] for x in outputs]
|
||
#---------------------------------------------------#
|
||
# outputs输入前代表每个特征层的预测结果
|
||
# batch_size, 4 + 1 + num_classes, 80, 80 => batch_size, 4 + 1 + num_classes, 6400
|
||
# batch_size, 5 + num_classes, 40, 40
|
||
# batch_size, 5 + num_classes, 20, 20
|
||
# batch_size, 4 + 1 + num_classes, 6400 + 1600 + 400 -> batch_size, 4 + 1 + num_classes, 8400
|
||
# 堆叠后为batch_size, 8400, 5 + num_classes
|
||
#---------------------------------------------------#
|
||
outputs = torch.cat([x.flatten(start_dim=2) for x in outputs], dim=2).permute(0, 2, 1)
|
||
#---------------------------------------------------#
|
||
# 获得每一个特征点属于每一个种类的概率
|
||
#---------------------------------------------------#
|
||
outputs[:, :, 4:] = torch.sigmoid(outputs[:, :, 4:])
|
||
for h, w in hw:
|
||
#---------------------------#
|
||
# 根据特征层的高宽生成网格点
|
||
#---------------------------#
|
||
grid_y, grid_x = torch.meshgrid([torch.arange(h), torch.arange(w)])
|
||
#---------------------------#
|
||
# 1, 6400, 2
|
||
# 1, 1600, 2
|
||
# 1, 400, 2
|
||
#---------------------------#
|
||
grid = torch.stack((grid_x, grid_y), 2).view(1, -1, 2)
|
||
shape = grid.shape[:2]
|
||
|
||
grids.append(grid)
|
||
strides.append(torch.full((shape[0], shape[1], 1), input_shape[0] / h))
|
||
#---------------------------#
|
||
# 将网格点堆叠到一起
|
||
# 1, 6400, 2
|
||
# 1, 1600, 2
|
||
# 1, 400, 2
|
||
#
|
||
# 1, 8400, 2
|
||
#---------------------------#
|
||
grids = torch.cat(grids, dim=1).type(outputs.type())
|
||
strides = torch.cat(strides, dim=1).type(outputs.type())
|
||
#------------------------#
|
||
# 根据网格点进行解码
|
||
#------------------------#
|
||
outputs[..., :2] = (outputs[..., :2] + grids) * strides
|
||
outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides
|
||
#-----------------#
|
||
# 归一化
|
||
#-----------------#
|
||
outputs[..., [0,2]] = outputs[..., [0,2]] / input_shape[1]
|
||
outputs[..., [1,3]] = outputs[..., [1,3]] / input_shape[0]
|
||
return outputs
|
||
|
||
def non_max_suppression(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 = prediction.new(prediction.shape)
|
||
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))]
|
||
#----------------------------------------------------------#
|
||
# 对输入图片进行循环，一般只会进行一次
|
||
#----------------------------------------------------------#
|
||
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, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True)
|
||
|
||
#----------------------------------------------------------#
|
||
# 利用置信度进行第一轮筛选
|
||
#----------------------------------------------------------#
|
||
conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze()
|
||
|
||
if not image_pred.size(0):
|
||
continue
|
||
#-------------------------------------------------------------------------#
|
||
# detections [num_anchors, 7]
|
||
# 7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred
|
||
#-------------------------------------------------------------------------#
|
||
detections = torch.cat((image_pred[:, :5], class_conf, class_pred.float()), 1)
|
||
detections = detections[conf_mask]
|
||
|
||
nms_out_index = boxes.batched_nms(
|
||
detections[:, :4],
|
||
detections[:, 4] * detections[:, 5],
|
||
detections[:, 6],
|
||
nms_thres,
|
||
)
|
||
|
||
output[i] = detections[nms_out_index]
|
||
|
||
# #------------------------------------------#
|
||
# # 获得预测结果中包含的所有种类
|
||
# #------------------------------------------#
|
||
# unique_labels = detections[:, -1].cpu().unique()
|
||
|
||
# if prediction.is_cuda:
|
||
# unique_labels = unique_labels.cuda()
|
||
# detections = detections.cuda()
|
||
|
||
# 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]
|
||
|
||
# #------------------------------------------#
|
||
# # 使用官方自带的非极大抑制会速度更快一些！
|
||
# #------------------------------------------#
|
||
# keep = nms(
|
||
# detections_class[:, :4],
|
||
# detections_class[:, 4] * detections_class[:, 5],
|
||
# nms_thres
|
||
# )
|
||
# max_detections = detections_class[keep]
|
||
|
||
# # # 按照存在物体的置信度排序
|
||
# # _, conf_sort_index = torch.sort(detections_class[:, 4]*detections_class[:, 5], descending=True)
|
||
# # detections_class = detections_class[conf_sort_index]
|
||
# # # 进行非极大抑制
|
||
# # max_detections = []
|
||
# # while detections_class.size(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].unsqueeze(0))
|
||
# # if len(detections_class) == 1:
|
||
# # break
|
||
# # ious = bbox_iou(max_detections[-1], detections_class[1:])
|
||
# # detections_class = detections_class[1:][ious < nms_thres]
|
||
# # # 堆叠
|
||
# # max_detections = torch.cat(max_detections).data
|
||
|
||
# # Add max detections to outputs
|
||
# output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))
|
||
|
||
if output[i] is not None:
|
||
output[i] = output[i].cpu().numpy()
|
||
box_xy, box_wh = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]
|
||
output[i][:, :4] = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
|
||
return output
|