人脸检测——FaceBoxes之解读与效果展示
本章对其论文进行简单解读及其效果的演示：
文章链接：《FaceBoxes: A CPU Real-time Face Detector with High Accuracy》
总体的流程图如下所示：
本质上并不复杂，熟悉fasterRcnn，或者熟悉rpn就可以很快搞清楚。
faceBox相当于在fasterRcnn的基础只保留了rpn结果，去掉了roi pooling，并且采用fpn，已经对anchor进行了改进，还有一点主干网络（不算什么亮点了）。
论文中的贡献有三个部分： （分别对应：主干网络、fpn、anchor）
（1）Rapidly Digested Convolutional Layers(RDCL)
（2）Multiple Scale Convolutional Layers(MSCL)
（3）Anchor densification strategy
为了anchor密度均衡，可以对密度不足的anchor以中心进行偏移加倍，如下图所示：
训练完成后测试效果：
后续会继续更新以及代码实现......
（1）数据准备与生成：（包含数据读取、扩增、anchor生成、所需训练数据格式）
dataset.py
#encoding:utf-8
import torch
import math
import itertools
import cv2
import numpy as np
class DataEncoder:
def __init__(self):
'''
compute default boxes
'''
# 模型训练图片得大小为1024.
scale = 1024.
# 锚点得平铺间隔。 可以抽象成anchors的密度。 这里的情况下：也相当于相对1024的缩放倍数。
steps = [s / scale for s in (32, 64, 128)]
# 每一层anchors的大小，包括：Inception3 layer; Conv3 2 layer; Conv4 2 layer;
sizes = [s / scale for s in (32, 256, 512)] # 当32改为64时，achor与label匹配的正样本数目更多
aspect_ratios = ((1,2,4), (1,), (1,))
# 对应Inception3 layer; Conv3 2 layer; Conv4 2 layer; 各层的featuremap大小。
feature_map_sizes = (32, 16, 8)
density = [[-3,-1,1,3],[-1,1],[0]] # density for output layer1
# density = [[0],[0],[0]] # density for output layer1
# 选用多用层， 用于最终的检测。
num_layers = len(feature_map_sizes)
boxes = []
# 遍历每一层
for i in range(num_layers):
# 选择该层的特征图大小。
fmsize = feature_map_sizes[i]
# print(len(boxes))
# 下面是计算各featuremap中，所有的box。 使用for循环进行统计，steps[i]可以抽象为第i层的anchors的密度。
for h,w in itertools.product(range(fmsize), repeat=2):
# 按照anchor在特征图上平铺：对应中心坐标。
cx = (w + 0.5)*steps[i]
cy = (h + 0.5)*steps[i]
# 这里可以理解为归一化的anchors的大小。
s = sizes[i]
# 每一层的anchor大小的缩放比例， Inception3 layer层为：(1,2,4) ； Conv3 2 layer层为(1,)； Conv4 2 layer层为(1,)。
for j,ar in enumerate(aspect_ratios[i]):
# Inception3 layer层除了使用了不同大小的anchor， 还加入了密集框，相当于对框做小的平移。
if i == 0:
for dx,dy in itertools.product(density[j], repeat=2):
boxes.append((cx+dx/8.*s*ar, cy+dy/8.*s*ar, s*ar, s*ar))
else:
boxes.append((cx, cy, s*ar, s*ar))
self.default_boxes = torch.Tensor(boxes)
def test_iou(self):
box1 = torch.Tensor([0,0,10,10])
box1 = box1[None,:]
box2 = torch.Tensor([[5,0,15,10],[5,0,15,10]])
print('iou', self.iou(box1, box2))
def iou(self, box1, box2):
'''Compute the intersection over union of two set of boxes, each box is [x1,y1,x2,y2].
Args:
box1: (tensor) bounding boxes, sized [N,4].
box2: (tensor) bounding boxes, sized [M,4].
Return:
(tensor) iou, sized [N,M].
'''
N = box1.size(0)
M = box2.size(0)
lt = torch.max( # left top
box1[:,:2].unsqueeze(1).expand(N,M,2), # [N,2] -> [N,1,2] -> [N,M,2]
box2[:,:2].unsqueeze(0).expand(N,M,2), # [M,2] -> [1,M,2] -> [N,M,2]
)
rb = torch.min( # right bottom
box1[:,2:].unsqueeze(1).expand(N,M,2), # [N,2] -> [N,1,2] -> [N,M,2]
box2[:,2:].unsqueeze(0).expand(N,M,2), # [M,2] -> [1,M,2] -> [N,M,2]
)
wh = rb - lt # [N,M,2]
wh[wh<0] = 0 # clip at 0
inter = wh[:,:,0] * wh[:,:,1] # [N,M]
area1 = (box1[:,2]-box1[:,0]) * (box1[:,3]-box1[:,1]) # [N,]
area2 = (box2[:,2]-box2[:,0]) * (box2[:,3]-box2[:,1]) # [M,]
area1 = area1.unsqueeze(1).expand_as(inter) # [N,] -> [N,1] -> [N,M]
area2 = area2.unsqueeze(0).expand_as(inter) # [M,] -> [1,M] -> [N,M]
iou = inter / (area1 + area2 - inter)
return iou
def test_encode(self, boxes, img, label):
# box = torch.Tensor([ 0.4003,0.0000,0.8409,0.4295])
# box = box[None,:]
# label = torch.LongTensor([1])
# label = label[None,:]
loc, conf = self.encode(boxes, label)
print('conf', type(conf), conf.size(), conf.long().sum())
print('loc', loc)
# img = cv2.imread('test1.jpg')
w,h,_ = img.shape
for box in boxes:
cv2.rectangle(img, (int(box[0]*w),int(box[1]*w)), (int(box[2]*w), int(box[3]*w)), (0,255,0))
print(type(conf))
for i in range(len(self.default_boxes)):
if conf[i] != 0:
print(i)
im = img.copy()
# for i in range(42):
# print(self.default_boxes[i]*w)
for i in range(32*32*21):
box_item = self.default_boxes[i]*w
centerx, centery = int(box_item[0]), int(box_item[1])
if conf[i] != 0:
cv2.circle(im, (centerx, centery), 4, (0,255,0))
else:
cv2.circle(im, (centerx, centery), 1, (0,0,255))
box = self.default_boxes[0]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
box = self.default_boxes[16]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
box = self.default_boxes[20]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
cv2.imwrite('test_encoder_0.jpg', im)
im = img.copy()
for i in range(32*32*21, 32*32*21+16*16):
box_item = self.default_boxes[i]*w
centerx, centery = int(box_item[0]), int(box_item[1])
if conf[i] != 0:
cv2.circle(im, (centerx, centery), 4, (0,255,0))
else:
cv2.circle(im, (centerx, centery), 2, (0,0,255))
box = self.default_boxes[32*32*21]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
cv2.imwrite('test_encoder_1.jpg', im)
im = img.copy()
for i in range(32*32*21+16*16, len(self.default_boxes)):
box_item = self.default_boxes[i]*w
centerx, centery = int(box_item[0]), int(box_item[1])
if conf[i] != 0:
cv2.circle(im, (centerx, centery), 4, (0,255,0))
else:
cv2.circle(im, (centerx, centery), 2, (0,0,255))
box = self.default_boxes[32*32*21+16*16]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
cv2.imwrite('test_encoder_2.jpg', im)
# for i in range(conf.size(0)):
# if conf[i].numpy != 0:
# print()
def encode(self,boxes,classes,threshold=0.35):
'''
boxes:[num_obj, 4]
default_box (x1,y1,x2,y2)
return:boxes: (tensor) [num_obj,21824,4]
classes:class label [obj,]
'''
# 做归一化后的boxes；
boxes_org = boxes
#print(boxes,classes)
# 得到所有的default_boxes。
default_boxes = self.default_boxes #[21824,4]
num_default_boxes = default_boxes.size(0)
# 图片中含有的人脸个数。
num_obj=boxes.size(0) #人脸个数
#print('num_faces {}'.format(num_obj))
# 计算真实box和预测box的iou，用于后面给每一个bounding box不管IOU大小，都设置一个与之IOU最大的default_box；并且每一个default_boxes对应到与之IOU最大的bounding box上
iou = self.iou(
boxes,
torch.cat([default_boxes[:,:2] - default_boxes[:,2:]/2,
default_boxes[:,:2] + default_boxes[:,2:]/2], 1))
# iou = self.iou(boxes, default_boxes)
#print('iou size {}'.format(iou.size()))
max_iou, max_iou_index = iou.max(1) #为每一个bounding box不管IOU大小，都设置一个与之IOU最大的default_box
iou, max_index= iou.max(0) #每一个default_boxes对应到与之IOU最大的bounding box上
#print(max(iou))
max_index.squeeze_(0) # torch.LongTensor 21824
iou.squeeze_(0)
# print('boxes', boxes.size(), boxes, 'max_index', max_index)
max_index[max_iou_index] = torch.LongTensor(range(num_obj))
'''
import numpy as np
a = np.array([1,2,3])
b = np.array([1,1,2,2,1,1])
print (a[b]) : [2 2 3 3 2 2]
'''
# 可以将box的维度 扩展到和default_boxes 一样，变成[21824,4]， 方便后边的批量计算。
boxes = boxes[max_index] # [21824,4] 是图像label
variances = [0.1, 0.2]
# 首先求box的中心坐标。 - default_boxes[:,:2]是为了不考虑具体坐标值，这里关注的是相对坐标，可以理解为一种平移预估。
cxcy = (boxes[:,:2] + boxes[:,2:])/2 - default_boxes[:,:2] # [21824,2]
# 除以default_boxes宽高，
cxcy /= variances[0] * default_boxes[:,2:]
# 首先得到bounding box宽高， 除以default_boxes宽高
wh = (boxes[:,2:] - boxes[:,:2]) / default_boxes[:,2:] # [21824,2] 为什么会出现0宽度？？
wh = torch.log(wh) / variances[1] # Variable
inf_flag = wh.abs() > 10000
# print ('>>>>>>>>>', inf_flag.long().sum())
if(inf_flag.long().sum() is not 0):
print('inf_flag has true', wh, boxes)
print('org_boxes', boxes_org)
print('max_iou', max_iou, 'max_iou_index', max_iou_index)
raise 'inf error'
loc = torch.cat([cxcy, wh], 1) # [21824,4]
conf = classes[max_index] #其实都是1 [21824,]
conf[iou < threshold] = 0 #iou小的设为背景
conf[max_iou_index] = 1 # 这么设置有问题，loc loss 会导致有inf loss，从而干扰训练，
# 去掉后，损失降的更稳定些，是因为widerFace数据集里有的label
# 做的宽度为0，但是没有被滤掉，是因为max(1)必须为每一个object选择一个
# 与之对应的default_box，需要修改数据集里的label。
# ('targets', Variable containing:
# 318.7500 -1.2500 -inf -inf
# org_boxes 0.1338 0.3801 0.1338 0.3801
return loc,conf
def nms(self,bboxes,scores,threshold=0.5):
'''
bboxes(tensor) [N,4]
scores(tensor) [N,]
'''
x1 = bboxes[:,0]
y1 = bboxes[:,1]
x2 = bboxes[:,2]
y2 = bboxes[:,3]
areas = (x2-x1) * (y2-y1)
_,order = scores.sort(0,descending=True)
keep = []
while order.numel() > 0:
i = order[0]
keep.append(i)
if order.numel() == 1:
break
xx1 = x1[order[1:]].clamp(min=x1[i])
yy1 = y1[order[1:]].clamp(min=y1[i])
xx2 = x2[order[1:]].clamp(max=x2[i])
yy2 = y2[order[1:]].clamp(max=y2[i])
w = (xx2-xx1).clamp(min=0)
h = (yy2-yy1).clamp(min=0)
inter = w*h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
ids = (ovr<=threshold).nonzero().squeeze()
if ids.numel() == 0:
break
order = order[ids+1]
return torch.LongTensor(keep)
def decode(self,loc,conf):
'''
將预测出的 loc/conf转换成真实的人脸框
loc [21842,4]
conf [21824,2]
'''
# encode 部分的返操作。
variances = [0.1, 0.2]
cxcy = loc[:,:2] * variances[0] * self.default_boxes[:,2:] + self.default_boxes[:,:2]
wh = torch.exp(loc[:,2:] * variances[1]) * self.default_boxes[:,2:]
boxes = torch.cat([cxcy-wh/2,cxcy+wh/2],1) #[21824,4]
conf[:,0] = 0.4
max_conf, labels = conf.max(1) #[21842,1]
# print(max_conf)
# print('labels', labels.long().sum())
if labels.long().sum() is 0:
sconf, slabel = conf.max(0)
max_conf[slabel[0:5]] = sconf[0:5]
labels[slabel[0:5]] = 1
ids = labels.nonzero().squeeze(1)
# print('ids', ids)
# print('boxes', boxes.size(), boxes[ids])
keep = self.nms(boxes[ids],max_conf[ids])#.squeeze(1))
# 返回的boxes是归一化后的结果
return boxes[ids][keep], labels[ids][keep], max_conf[ids][keep]
if __name__ == '__main__':
dataencoder = DataEncoder()
# dataencoder.test_iou()
#dataencoder.test_encode()
# print((dataencoder.default_boxes))
boxes = torch.Tensor([[-8,-8,24,24],[400,400,500,500]])/1024
dataencoder.encode(boxes,torch.Tensor([1,1]))encoderl.py
#encoding:utf-8
import torch
import math
import itertools
import cv2
import numpy as np
class DataEncoder:
def __init__(self):
'''
compute default boxes
'''
# 模型训练图片得大小为1024.
scale = 1024.
# 锚点得平铺间隔。 可以抽象成anchors的密度。 这里的情况下：也相当于相对1024的缩放倍数。
steps = [s / scale for s in (32, 64, 128)]
# 每一层anchors的大小，包括：Inception3 layer; Conv3 2 layer; Conv4 2 layer;
sizes = [s / scale for s in (32, 256, 512)] # 当32改为64时，achor与label匹配的正样本数目更多
aspect_ratios = ((1,2,4), (1,), (1,))
# 对应Inception3 layer; Conv3 2 layer; Conv4 2 layer; 各层的featuremap大小。
feature_map_sizes = (32, 16, 8)
density = [[-3,-1,1,3],[-1,1],[0]] # density for output layer1
# density = [[0],[0],[0]] # density for output layer1
# 选用多用层， 用于最终的检测。
num_layers = len(feature_map_sizes)
boxes = []
# 遍历每一层
for i in range(num_layers):
# 选择该层的特征图大小。
fmsize = feature_map_sizes[i]
# print(len(boxes))
# 下面是计算各featuremap中，所有的box。 使用for循环进行统计，steps[i]可以抽象为第i层的anchors的密度。
for h,w in itertools.product(range(fmsize), repeat=2):
# 按照anchor在特征图上平铺：对应中心坐标。
cx = (w + 0.5)*steps[i]
cy = (h + 0.5)*steps[i]
# 这里可以理解为归一化的anchors的大小。
s = sizes[i]
# 每一层的anchor大小的缩放比例， Inception3 layer层为：(1,2,4) ； Conv3 2 layer层为(1,)； Conv4 2 layer层为(1,)。
for j,ar in enumerate(aspect_ratios[i]):
# Inception3 layer层除了使用了不同大小的anchor， 还加入了密集框，相当于对框做小的平移。
if i == 0:
for dx,dy in itertools.product(density[j], repeat=2):
boxes.append((cx+dx/8.*s*ar, cy+dy/8.*s*ar, s*ar, s*ar))
else:
boxes.append((cx, cy, s*ar, s*ar))
self.default_boxes = torch.Tensor(boxes)
def test_iou(self):
box1 = torch.Tensor([0,0,10,10])
box1 = box1[None,:]
box2 = torch.Tensor([[5,0,15,10],[5,0,15,10]])
print('iou', self.iou(box1, box2))
def iou(self, box1, box2):
'''Compute the intersection over union of two set of boxes, each box is [x1,y1,x2,y2].
Args:
box1: (tensor) bounding boxes, sized [N,4].
box2: (tensor) bounding boxes, sized [M,4].
Return:
(tensor) iou, sized [N,M].
'''
N = box1.size(0)
M = box2.size(0)
lt = torch.max( # left top
box1[:,:2].unsqueeze(1).expand(N,M,2), # [N,2] -> [N,1,2] -> [N,M,2]
box2[:,:2].unsqueeze(0).expand(N,M,2), # [M,2] -> [1,M,2] -> [N,M,2]
)
rb = torch.min( # right bottom
box1[:,2:].unsqueeze(1).expand(N,M,2), # [N,2] -> [N,1,2] -> [N,M,2]
box2[:,2:].unsqueeze(0).expand(N,M,2), # [M,2] -> [1,M,2] -> [N,M,2]
)
wh = rb - lt # [N,M,2]
wh[wh<0] = 0 # clip at 0
inter = wh[:,:,0] * wh[:,:,1] # [N,M]
area1 = (box1[:,2]-box1[:,0]) * (box1[:,3]-box1[:,1]) # [N,]
area2 = (box2[:,2]-box2[:,0]) * (box2[:,3]-box2[:,1]) # [M,]
area1 = area1.unsqueeze(1).expand_as(inter) # [N,] -> [N,1] -> [N,M]
area2 = area2.unsqueeze(0).expand_as(inter) # [M,] -> [1,M] -> [N,M]
iou = inter / (area1 + area2 - inter)
return iou
def test_encode(self, boxes, img, label):
# box = torch.Tensor([ 0.4003,0.0000,0.8409,0.4295])
# box = box[None,:]
# label = torch.LongTensor([1])
# label = label[None,:]
loc, conf = self.encode(boxes, label)
print('conf', type(conf), conf.size(), conf.long().sum())
print('loc', loc)
# img = cv2.imread('test1.jpg')
w,h,_ = img.shape
for box in boxes:
cv2.rectangle(img, (int(box[0]*w),int(box[1]*w)), (int(box[2]*w), int(box[3]*w)), (0,255,0))
print(type(conf))
for i in range(len(self.default_boxes)):
if conf[i] != 0:
print(i)
im = img.copy()
# for i in range(42):
# print(self.default_boxes[i]*w)
for i in range(32*32*21):
box_item = self.default_boxes[i]*w
centerx, centery = int(box_item[0]), int(box_item[1])
if conf[i] != 0:
cv2.circle(im, (centerx, centery), 4, (0,255,0))
else:
cv2.circle(im, (centerx, centery), 1, (0,0,255))
box = self.default_boxes[0]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
box = self.default_boxes[16]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
box = self.default_boxes[20]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
cv2.imwrite('test_encoder_0.jpg', im)
im = img.copy()
for i in range(32*32*21, 32*32*21+16*16):
box_item = self.default_boxes[i]*w
centerx, centery = int(box_item[0]), int(box_item[1])
if conf[i] != 0:
cv2.circle(im, (centerx, centery), 4, (0,255,0))
else:
cv2.circle(im, (centerx, centery), 2, (0,0,255))
box = self.default_boxes[32*32*21]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
cv2.imwrite('test_encoder_1.jpg', im)
im = img.copy()
for i in range(32*32*21+16*16, len(self.default_boxes)):
box_item = self.default_boxes[i]*w
centerx, centery = int(box_item[0]), int(box_item[1])
if conf[i] != 0:
cv2.circle(im, (centerx, centery), 4, (0,255,0))
else:
cv2.circle(im, (centerx, centery), 2, (0,0,255))
box = self.default_boxes[32*32*21+16*16]
cv2.rectangle(im, (0,0), (int(box[2]*w), int(box[3]*w)), (0,255,0))
cv2.imwrite('test_encoder_2.jpg', im)
# for i in range(conf.size(0)):
# if conf[i].numpy != 0:
# print()
def encode(self,boxes,classes,threshold=0.35):
'''
boxes:[num_obj, 4]
default_box (x1,y1,x2,y2)
return:boxes: (tensor) [num_obj,21824,4]
classes:class label [obj,]
'''
# 做归一化后的boxes；
boxes_org = boxes
#print(boxes,classes)
# 得到所有的default_boxes。
default_boxes = self.default_boxes #[21824,4]
num_default_boxes = default_boxes.size(0)
# 图片中含有的人脸个数。
num_obj=boxes.size(0) #人脸个数
#print('num_faces {}'.format(num_obj))
# 计算真实box和预测box的iou，用于后面给每一个bounding box不管IOU大小，都设置一个与之IOU最大的default_box；并且每一个default_boxes对应到与之IOU最大的bounding box上
iou = self.iou(
boxes,
torch.cat([default_boxes[:,:2] - default_boxes[:,2:]/2,
default_boxes[:,:2] + default_boxes[:,2:]/2], 1))
# iou = self.iou(boxes, default_boxes)
#print('iou size {}'.format(iou.size()))
max_iou, max_iou_index = iou.max(1) #为每一个bounding box不管IOU大小，都设置一个与之IOU最大的default_box
iou, max_index= iou.max(0) #每一个default_boxes对应到与之IOU最大的bounding box上
#print(max(iou))
max_index.squeeze_(0) # torch.LongTensor 21824
iou.squeeze_(0)
# print('boxes', boxes.size(), boxes, 'max_index', max_index)
max_index[max_iou_index] = torch.LongTensor(range(num_obj))
'''
import numpy as np
a = np.array([1,2,3])
b = np.array([1,1,2,2,1,1])
print (a[b]) : [2 2 3 3 2 2]
'''
# 可以将box的维度 扩展到和default_boxes 一样，变成[21824,4]， 方便后边的批量计算。
boxes = boxes[max_index] # [21824,4] 是图像label
variances = [0.1, 0.2]
# 首先求box的中心坐标。 - default_boxes[:,:2]是为了不考虑具体坐标值，这里关注的是相对坐标，可以理解为一种平移预估。
cxcy = (boxes[:,:2] + boxes[:,2:])/2 - default_boxes[:,:2] # [21824,2]
# 除以default_boxes宽高，
cxcy /= variances[0] * default_boxes[:,2:]
# 首先得到bounding box宽高， 除以default_boxes宽高
wh = (boxes[:,2:] - boxes[:,:2]) / default_boxes[:,2:] # [21824,2] 为什么会出现0宽度？？
wh = torch.log(wh) / variances[1] # Variable
inf_flag = wh.abs() > 10000
# print ('>>>>>>>>>', inf_flag.long().sum())
if(inf_flag.long().sum() is not 0):
print('inf_flag has true', wh, boxes)
print('org_boxes', boxes_org)
print('max_iou', max_iou, 'max_iou_index', max_iou_index)
raise 'inf error'
loc = torch.cat([cxcy, wh], 1) # [21824,4]
conf = classes[max_index] #其实都是1 [21824,]
conf[iou < threshold] = 0 #iou小的设为背景
conf[max_iou_index] = 1 # 这么设置有问题，loc loss 会导致有inf loss，从而干扰训练，
# 去掉后，损失降的更稳定些，是因为widerFace数据集里有的label
# 做的宽度为0，但是没有被滤掉，是因为max(1)必须为每一个object选择一个
# 与之对应的default_box，需要修改数据集里的label。
# ('targets', Variable containing:
# 318.7500 -1.2500 -inf -inf
# org_boxes 0.1338 0.3801 0.1338 0.3801
return loc,conf
def nms(self,bboxes,scores,threshold=0.5):
'''
bboxes(tensor) [N,4]
scores(tensor) [N,]
'''
x1 = bboxes[:,0]
y1 = bboxes[:,1]
x2 = bboxes[:,2]
y2 = bboxes[:,3]
areas = (x2-x1) * (y2-y1)
_,order = scores.sort(0,descending=True)
keep = []
while order.numel() > 0:
i = order[0]
keep.append(i)
if order.numel() == 1:
break
xx1 = x1[order[1:]].clamp(min=x1[i])
yy1 = y1[order[1:]].clamp(min=y1[i])
xx2 = x2[order[1:]].clamp(max=x2[i])
yy2 = y2[order[1:]].clamp(max=y2[i])
w = (xx2-xx1).clamp(min=0)
h = (yy2-yy1).clamp(min=0)
inter = w*h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
ids = (ovr<=threshold).nonzero().squeeze()
if ids.numel() == 0:
break
order = order[ids+1]
return torch.LongTensor(keep)
def decode(self,loc,conf):
'''
將预测出的 loc/conf转换成真实的人脸框
loc [21842,4]
conf [21824,2]
'''
# encode 部分的返操作。
variances = [0.1, 0.2]
cxcy = loc[:,:2] * variances[0] * self.default_boxes[:,2:] + self.default_boxes[:,:2]
wh = torch.exp(loc[:,2:] * variances[1]) * self.default_boxes[:,2:]
boxes = torch.cat([cxcy-wh/2,cxcy+wh/2],1) #[21824,4]
conf[:,0] = 0.4
max_conf, labels = conf.max(1) #[21842,1]
# print(max_conf)
# print('labels', labels.long().sum())
if labels.long().sum() is 0:
sconf, slabel = conf.max(0)
max_conf[slabel[0:5]] = sconf[0:5]
labels[slabel[0:5]] = 1
ids = labels.nonzero().squeeze(1)
# print('ids', ids)
# print('boxes', boxes.size(), boxes[ids])
keep = self.nms(boxes[ids],max_conf[ids])#.squeeze(1))
# 返回的boxes是归一化后的结果
return boxes[ids][keep], labels[ids][keep], max_conf[ids][keep]
if __name__ == '__main__':
dataencoder = DataEncoder()
# dataencoder.test_iou()
#dataencoder.test_encode()
# print((dataencoder.default_boxes))
boxes = torch.Tensor([[-8,-8,24,24],[400,400,500,500]])/1024
dataencoder.encode(boxes,torch.Tensor([1,1]))本文参与 腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。
原始发表：2018年12月06日，如有侵权请联系 cloudcommunity@tencent.com 删除
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