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utils320.lua
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require 'nn'
require 'cudnn'
require 'cutorch'
require 'torch'
require 'cunn'--]]
loc_loss_func = nn.SmoothL1Criterion():cuda()
loc_loss_func.sizeAverage = false
conf_loss_func = nn.CrossEntropyCriterion():cuda()
conf_loss_func.nll.sizeAverage = false
------------------
--po = trainGT['pcd1021r.png']
function pointsToLabel(prior_bboxes) --utils
local label = torch.Tensor(prior_bboxes:size(1),5)
label[{{}, {1}}] = torch.add(prior_bboxes[{{}, {1}}], prior_bboxes[{{}, {5}}]):div(2) -- center x
label[{{}, {2}}] = torch.add(prior_bboxes[{{}, {2}}], prior_bboxes[{{}, {6}}]):div(2) -- center y
label[{{}, {3}}] = torch.sqrt(torch.add(torch.pow(torch.csub(prior_bboxes[{{}, {3}}],prior_bboxes[{{}, {5}}]),2),torch.pow(torch.csub(prior_bboxes[{{}, {4}}],prior_bboxes[{{}, {6}}]),2)))--h
label[{{}, {4}}] = torch.sqrt(torch.add(torch.pow(torch.csub(prior_bboxes[{{}, {7}}],prior_bboxes[{{}, {5}}]),2),torch.pow(torch.csub(prior_bboxes[{{}, {8}}],prior_bboxes[{{}, {6}}]),2)))--w
label[{{}, {5}}] = -math.deg(torch.atan(torch.cdiv(torch.csub(prior_bboxes[{{}, {6}}],prior_bboxes[{{}, {8}}]),torch.csub(prior_bboxes[{{}, {5}}],prior_bboxes[{{}, {7}}]))))
return label
end
function pointCrop(prior_bboxes,Xran,Yran,cfg) --utils
local num_boxes = prior_bboxes:size(1)
local num_points = prior_bboxes:size(2)
local Croped = torch.Tensor(num_boxes,num_points):copy(prior_bboxes)
local XOriN=110+Xran
local YOriN=110+Yran
Croped[{{},{1}}]:csub(XOriN)
Croped[{{},{3}}]:csub(XOriN)
Croped[{{},{5}}]:csub(XOriN)
Croped[{{},{7}}]:csub(XOriN)
Croped[{{},{2}}]:csub(YOriN)
Croped[{{},{4}}]:csub(YOriN)
Croped[{{},{6}}]:csub(YOriN)
Croped[{{},{8}}]:csub(YOriN)
local row_mask = torch.gt(torch.lt(Croped,0)+torch.gt(Croped,cfg.imgshape),0):sum(2) -- the rows zero remain byteTensor
local remain_num = torch.squeeze(row_mask,2):eq(0):nonzero() -- longTensor of mask
--print(row_mask:eq(0))
--print(remain_num)
local inside_num = row_mask:eq(0):sum()
--print(inside_num)
if inside_num == 0 then
print('out of bound')
return false, prior_bboxes
else
local tmp_points = Croped:index(1,torch.squeeze(remain_num,2))
return true, tmp_points
end
end
function LabelRotateTensor(label, r, center)
local labelR = torch.Tensor(label:size()):copy(label)
local radius = torch.sqrt(torch.pow((label[{{},{1}}]-center.x), 2)+torch.pow((label[{{},{2}}]-center.y), 2) )
--print(radius)
cosor = torch.cdiv((label[{{},{1}}]-center.x), radius)
sinor = torch.cdiv((label[{{},{2}}]-center.y), radius)
cosr = math.cos(r)
sinr = math.sin(r)
labelR[{{},{5}}] = labelR[{{},{5}}] + r*180/math.pi
cosRot = cosor*cosr+sinor*sinr
sinRot = sinor*cosr-cosor*sinr
local lowerB = torch.lt(labelR[{{},{5}}], -90)
local higherB = torch.gt(labelR[{{},{5}}], 90)
labelR[{{},{5}}] = torch.csub(torch.add(labelR[{{},{5}}]:float(), 180*lowerB:float()), 180*higherB:float())
--print(cosRot,sinRot)
labelR[{{},{1}}]=torch.cmul(radius,cosRot)+center.x
labelR[{{},{2}}]=torch.cmul(radius,sinRot)+center.y
local points_rotated = labelToPointsTensor(labelR)
local row_mask = torch.gt(torch.lt(points_rotated, 0)+torch.gt(points_rotated, cfg.imgshape),0):sum(2) -- the rows zero remain byteTensor, if one of the coordinates across the boundary note 1
local remain_num = torch.squeeze(row_mask,2):eq(0):nonzero() -- longTensor of mask
--print(row_mask:eq(0))
--print(remain_num)
local inside_num = row_mask:eq(0):sum()
--print(inside_num)
if inside_num == 0 then
print('over rotated')
return false, label
else
local tmp_label = labelR:index(1,torch.squeeze(remain_num, 2))
return true, tmp_label
end
end
function labelToPoints(label)
local w = label[4]
local h = label[3]
local uw = {}
uw.x = math.cos(label[5])
uw.y = math.sin(label[5])
local uh = {}
uh.x = math.sin(label[5])
uh.y = -math.cos(label[5])
local w1 = {x=uw.x*w/2,y=uw.y*w/2}
local w2 = {x=-uw.x*w/2,y=-uw.y*w/2}
local h1 = {x=uh.x*h/2,y=uh.y*h/2}
local h2 = {x=-uh.x*h/2,y=-uh.y*h/2}
local center = {x=label[1],y=label[2]}
local points={}
points.x = torch.Tensor(4)
points.y = torch.Tensor(4)
points.x=torch.Tensor({center.x+w1.x+h1.x,center.x+w1.x+h2.x,center.x+w2.x+h1.x,center.x+w2.x+h2.x})
points.y=torch.Tensor({center.y+w1.y+h1.y,center.y+w1.y+h2.y,center.y+w2.y+h1.y,center.y+w2.y+h2.y})
return points
end
function labelToPointsTensor(label)
local points = torch.Tensor(label:size(1), 8)
local center_x = label[{{},{1}}]
local center_y = label[{{},{2}}]
local h = label[{{},{3}}]
local w = label[{{},{4}}]
local theta = label[{{},{5}}]*math.pi/180 -- degree to radian
-- x<0
points[{{},{1}}] = center_x + torch.cmul(torch.cos(theta), w)/2 + torch.cmul(torch.sin(theta), h)/2
points[{{},{2}}] = center_y - torch.cmul(torch.sin(theta), w)/2 + torch.cmul(torch.cos(theta), h)/2
points[{{},{3}}] = center_x - torch.cmul(torch.cos(theta), w)/2 + torch.cmul(torch.sin(theta), h)/2
points[{{},{4}}] = center_y + torch.cmul(torch.sin(theta), w)/2 + torch.cmul(torch.cos(theta), h)/2
points[{{},{5}}] = center_x - torch.cmul(torch.cos(theta), w)/2 - torch.cmul(torch.sin(theta), h)/2
points[{{},{6}}] = center_y + torch.cmul(torch.sin(theta), w)/2 - torch.cmul(torch.cos(theta), h)/2
points[{{},{7}}] = center_x + torch.cmul(torch.cos(theta), w)/2 - torch.cmul(torch.sin(theta), h)/2
points[{{},{8}}] = center_y - torch.cmul(torch.sin(theta), w)/2 - torch.cmul(torch.cos(theta), h)/2
return points
end
function TensorToPoints(tensor)
local tmp_tensor = tensor:view(4,2)
local points = {}
points.x = torch.Tensor(4):copy(torch.squeeze(tmp_tensor[{{},{1}}]))
points.y = torch.Tensor(4):copy(torch.squeeze(tmp_tensor[{{},{2}}]))
return points
end
function ImageCrop(img,Xran,Yran,cfg)
local XOriN=110+Xran
local YOriN=110+Yran
local ImageCroped=image.crop(img,XOriN,YOriN,XOriN+cfg.imgshape,YOriN+cfg.imgshape) --the box of crop should be lower than 350 pixels
return ImageCroped
end
---------------
function EncodeBBox(bbox, prior_bboxes, variance) --adapted to grasp
local gt_offset = torch.Tensor(prior_bboxes:size())
local prior_center_x = prior_bboxes[{{}, {1}}]
local prior_center_y = prior_bboxes[{{}, {2}}]
local prior_height = prior_bboxes[{{}, {3}}] --h
local prior_width = prior_bboxes[{{}, {4}}] --w
local prior_angle = prior_bboxes[{{}, {5}}]
local bbox_center_x = bbox[{{}, {1}}] --gt
local bbox_center_y = bbox[{{}, {2}}]
local bbox_height = bbox[{{}, {3}}]
local bbox_width = bbox[{{}, {4}}]
local bbox_angle = bbox[{{}, {5}}]
local encode_bbox_xmin, encode_bbox_ymin, encode_bbox_xmax, encode_bbox_ymax
if variance == nil then
gt_offset[{{},{1}}] = torch.cdiv(torch.csub(bbox_center_x, prior_center_x), prior_width)
gt_offset[{{},{2}}] = torch.cdiv(torch.csub(bbox_center_y, prior_center_y), prior_height)
gt_offset[{{},{3}}] = torch.log(torch.cdiv(bbox_height, prior_height))
gt_offset[{{},{4}}] = torch.log(torch.cdiv(bbox_width, prior_width))
gt_offset[{{},{5}}] = torch.div(torch.csub(bbox_angle, prior_angle), 15)
else
gt_offset[{{},{1}}] = torch.cdiv(torch.csub(bbox_center_x, prior_center_x), prior_width) / variance[1]
gt_offset[{{},{2}}] = torch.cdiv(torch.csub(bbox_center_y, prior_center_y), prior_height) / variance[2]
gt_offset[{{},{3}}] = torch.log(torch.cdiv(bbox_height, prior_height)) / variance[3]
gt_offset[{{},{4}}] = torch.log(torch.cdiv(bbox_width, prior_width)) / variance[4]
gt_offset[{{},{5}}] = torch.div(torch.csub(bbox_angle, prior_angle), 15) / variance[5]
end
return gt_offset
end
function DecodeBBox(bbox, prior_bboxes, variance) --adapted to grasp
local decode_xyhw = torch.Tensor(prior_bboxes:size())
local prior_center_x = prior_bboxes[{{}, {1}}]
local prior_center_y = prior_bboxes[{{}, {2}}]
local prior_height = prior_bboxes[{{}, {3}}]
local prior_width = prior_bboxes[{{}, {4}}]
local prior_angle = prior_bboxes[{{}, {5}}]
local decode_bbox_center_x, decode_bbox_center_y, decode_bbox_width, decode_bbox_height
if variance == nil then
decode_xyhw[{{},{1}}] = torch.add(torch.cmul(bbox[{{}, {1}}], prior_width), prior_center_x)
decode_xyhw[{{},{2}}] = torch.add(torch.cmul(bbox[{{}, {2}}], prior_height), prior_center_y)
decode_xyhw[{{},{3}}] = torch.cmul(torch.exp(bbox[{{}, {3}}]), prior_height)
decode_xyhw[{{},{4}}] = torch.cmul(torch.exp(bbox[{{}, {4}}]), prior_width)
decode_xyhw[{{},{5}}] = torch.add(torch.mul(bbox[{{}, {5}}], 15), prior_angle)
else
decode_xyhw[{{},{1}}] = torch.add(torch.cmul(bbox[{{}, {1}}], prior_width* variance[1]), prior_center_x)
decode_xyhw[{{},{2}}] = torch.add(torch.cmul(bbox[{{}, {2}}], prior_height* variance[2]), prior_center_y)
decode_xyhw[{{},{3}}] = torch.cmul(torch.exp(bbox[{{}, {3}}]* variance[3]), prior_height)
decode_xyhw[{{},{4}}] = torch.cmul(torch.exp(bbox[{{}, {4}}]* variance[4]), prior_width )
decode_xyhw[{{},{5}}] = torch.add(torch.mul(bbox[{{}, {5}}], 15)* variance[5], prior_angle)
end
return decode_xyhw
end
function GetPriorBBoxes(cfg)
local x_centers = torch.range(0, cfg.msize[1]-1)
x_centers:mul(cfg.steps[1]):add(cfg.steps[1]/2)
local y_centers = torch.range(0, cfg.msize[1]-1)
y_centers:mul(cfg.steps[1]):add(cfg.steps[1]/2)
local a_centers = torch.range(0, cfg.bpc[1]-1)
a_centers:mul(30):add(-75)
local output_view = torch.Tensor(cfg.bpc[1], 5, cfg.msize[1], cfg.msize[1])
local xc = output_view:select(2, 1)
local yc = output_view:select(2, 2)
local w = output_view:select(2, 3)
local h = output_view:select(2, 4)
local a = output_view:select(2, 5)
xc:copy(x_centers:view(1, 1, cfg.msize[1]):expand(cfg.bpc[1], cfg.msize[1], cfg.msize[1]))
yc:copy(y_centers:view(1, cfg.msize[1], 1):expand(cfg.bpc[1], cfg.msize[1], cfg.msize[1]))
w:fill(cfg.scale)
h:fill(cfg.scale)
a:copy(a_centers:view(cfg.bpc[1], 1, 1):expand(cfg.bpc[1], cfg.msize[1], cfg.msize[1]))
--print(output_view)
local tmp_prior_box = output_view:view(-1,cfg.msize[1], cfg.msize[1]):transpose(1,2):transpose(2,3)
tmp_prior_box = tmp_prior_box:contiguous()
--tmp_prior_box = tmp_prior_box
--print( tmp_prior_box:isContiguous() )
--local prior_box =
--print(tmp_prior_box:view(torch.LongStorage{600, 5}))
return tmp_prior_box:view(-1, 5)
end
--[[
function GetPriorBBoxes(cfg) --adapted to grasp
local scale = cfg.scale or 54
local map_num = cfg.nmap or 1
local map_size = cfg.msize or {21}
local img_size = cfg.imgshape or 336
local box_per_cell = cfg.bpc or {6}
local ar = cfg.aratio or {1, '1', 2, 1/2, 3, 1/3}
local steps = cfg.steps or {16}
local prior_bboxes = {}
for k = 1, map_num do
local step_w = steps[k]
local step_h = steps[k]
local tmp_prior_bboxes = torch.zeros(map_size[k], map_size[k], box_per_cell[k]*5)
for h = 1, map_size[k] do
for w = 1, map_size[k] do
local center_x = ((w-1) + 0.5) * step_w
local center_y = ((h-1) + 0.5) * step_h
for b = 1, box_per_cell[k] do
local box_width = scale * math.sqrt(ar[1])
local box_height = scale / math.sqrt(ar[1])
tmp_prior_bboxes[h][w][(b-1)*5+1] = center_x
tmp_prior_bboxes[h][w][(b-1)*5+2] = center_y
tmp_prior_bboxes[h][w][(b-1)*5+3] = box_height
tmp_prior_bboxes[h][w][(b-1)*5+4] = box_width
tmp_prior_bboxes[h][w][(b-1)*5+5] = -75+30*(b-1)
end
end
end
table.insert(prior_bboxes, tmp_prior_bboxes:view(-1, 5))
end
return nn.JoinTable(1):forward(prior_bboxes)
end -- output : torch.doubletensor (21*21*6)x5
--]]
function EncodeLocPrediction(loc_preds, prior_bboxes, gt_locs, match_indices, cfg)
local loc_gt_data = EncodeBBox(gt_locs:index(1, match_indices:nonzero():view(-1)), prior_bboxes:index(1, match_indices:nonzero():view(-1)), cfg.variance)
local loc_pred_data = loc_preds:index(1, match_indices:nonzero():view(-1))
-- if cfg.variance ~= nil then
-- loc_pred_data[{{}, {1}}]:div(cfg.variance[1])
-- loc_pred_data[{{}, {2}}]:div(cfg.variance[2])
-- loc_pred_data[{{}, {3}}]:div(cfg.variance[3])
-- loc_pred_data[{{}, {4}}]:div(cfg.variance[4])
-- end
return loc_gt_data, loc_pred_data
end
function EncodeConfPrediction(conf_preds, match_indices, neg_indices)
local num_matches = match_indices:nonzero():size(1)
local num_samples = num_matches + neg_indices:size(1)
local conf_gt_data = torch.zeros(num_samples)
conf_gt_data[{{1,num_matches}}] = match_indices[match_indices:ne(0)]
conf_gt_data[{{num_matches+1,-1}}] = 1
--print(conf_gt_data)
local match_preds = conf_preds:index(1, match_indices:nonzero():view(-1))
local neg_preds = conf_preds:index(1, match_indices:eq(0):nonzero():view(-1)):index(1, neg_indices)
local conf_pred_data = torch.cat(match_preds, neg_preds, 1)
return conf_gt_data, conf_pred_data
end
function BBoxSize(bbox)
local sizes = torch.zeros(bbox:size(1))
local idx = torch.cmul(bbox[{{}, {3}}]:gt(bbox[{{}, {1}}]), bbox[{{}, {4}}]:gt(bbox[{{}, {2}}])):view(-1)
local width = torch.csub(bbox[{{}, {3}}][idx], bbox[{{}, {1}}][idx])
local height = torch.csub(bbox[{{}, {4}}][idx], bbox[{{}, {2}}][idx])
sizes[idx] = torch.cmul(width, height):float()
return sizes
end
function MatchingBBoxes(prior_bboxes, gt_bboxes) --adapted to grasp
local match_indices = torch.zeros(prior_bboxes:size(1))
local match_overlaps = torch.zeros(prior_bboxes:size(1))
local gt_locs = torch.zeros(prior_bboxes:size())
--print(prior_bboxes:size())
for i = 1, gt_bboxes:size(1) do
local W
if gt_bboxes[i][1] <= 0 then
W = 1
elseif gt_bboxes[i][1] >= 320 then
W = 10
else
W = math.ceil(gt_bboxes[i][1]/32)
end
local H
if gt_bboxes[i][2] <= 0 then
H = 1
elseif gt_bboxes[i][2] >= 320 then
H = 10
else
H = math.ceil(gt_bboxes[i][2]/32)
end
local A
if gt_bboxes[i][5] == -90 then
A = 1
elseif gt_bboxes[i][5] == 90 then
A =6
else
A = math.ceil(gt_bboxes[i][5]/30)+3
end
--print(gt_bboxes[i][5],A)
--print(H,W,A)
local BT = torch.zeros(10,10,6)
if H == H and W == W and A == A then
BT[H][W][A] = 1
local idx =torch.gt(BT,0)
idx:view(prior_bboxes:size(1))
match_indices[idx] = 2
gt_locs[{{}, {1}}][idx] = gt_bboxes[i][1] --idx = (H-1)*21*6+ (W-1)*6 +A
gt_locs[{{}, {2}}][idx] = gt_bboxes[i][2]
gt_locs[{{}, {3}}][idx] = gt_bboxes[i][3]
gt_locs[{{}, {4}}][idx] = gt_bboxes[i][4]
gt_locs[{{}, {5}}][idx] = gt_bboxes[i][5]
end
end
return match_indices, gt_locs
end
function MineHardExamples(conf_preds, match_indices,cfg) -- adapted to grasp
local num_matches = match_indices:nonzero():size(1) -- number of prior boxes which has matched with gt
local num_sel = math.min(num_matches * cfg.NegRatio, match_indices:eq(0):sum()) -- set the num of selected
-- calc loss
local neg_loss = -torch.log(torch.cdiv(
torch.exp(conf_preds[{{},{1}}][match_indices:eq(0)]),
torch.exp(conf_preds):sum(2)[match_indices:eq(0)])) -- calculate negative log likelyhood for negative examples which are not matched with gt(the likelyhood of being background)
-- get topk
-- print(match_indices:eq(0))
--print(neg_loss:size())
local topk, neg_indices = neg_loss:topk(num_sel, true)
--print(conf_preds[{{},{1}}][match_indices:eq(0)])
return neg_indices
end
function GetGradient(loc_gt_data, loc_pred_data, conf_gt_data, conf_pred_data, match_indices, neg_indices, prior_bboxes_shape, cfg)
local num_matches = match_indices:nonzero():size(1)
local match_indices_tensor = match_indices:nonzero():view(-1)
local not_match_indices_tensor = match_indices:eq(0):nonzero():view(-1)
local loc_loss = loc_loss_func:forward(loc_pred_data:cuda(), loc_gt_data:cuda()) / num_matches
local loc_grad = (loc_loss_func:backward(loc_pred_data:cuda(), loc_gt_data:cuda()) / num_matches):float()
local conf_loss = conf_loss_func:forward(conf_pred_data:cuda(), conf_gt_data:cuda()) / num_matches
local conf_grad = (conf_loss_func:backward(conf_pred_data:cuda(), conf_gt_data:cuda()) / num_matches):float()
if conf_mat ~= nil then
conf_mat:batchAdd(conf_pred_data, conf_gt_data)
end
local loc_dE_do = torch.zeros(prior_bboxes_shape)
local conf_dE_do = torch.zeros(prior_bboxes_shape[1], cfg.classes)
for i = 1, num_matches do
loc_dE_do[match_indices_tensor[i]] = loc_grad[i]:float()
conf_dE_do[match_indices_tensor[i]] = conf_grad[i]:float()
end
for i = num_matches + 1, num_matches + neg_indices:size(1) do
local neg_index = not_match_indices_tensor[neg_indices[i-num_matches]]
conf_dE_do[neg_index] = conf_grad[i]
end
return loc_dE_do, conf_dE_do, loc_loss, conf_loss
end
function MultiBoxLoss(loc_preds, conf_preds, gt_bboxes, cfg)
local prior_bboxes = GetPriorBBoxes(cfg)
local match_indices, gt_locs = MatchingBBoxes(prior_bboxes, gt_bboxes, cfg)
local neg_indices = MineHardExamples(conf_preds:float(), match_indices:float(), cfg)
local loc_gt_data, loc_pred_data = EncodeLocPrediction(loc_preds:float(), prior_bboxes, gt_locs, match_indices, cfg)
local conf_gt_data, conf_pred_data = EncodeConfPrediction(conf_preds:float(), match_indices, neg_indices)
return GetGradient(loc_gt_data, loc_pred_data, conf_gt_data, conf_pred_data, match_indices, neg_indices, prior_bboxes:size(), cfg)
end
function NMS(original_bboxes, original_conf, original_classes, threshold)
local pick = {}
local bboxes = original_bboxes:clone()
local classes = original_classes:clone()
local sorted_score, i = original_conf:sort(true)
classes = classes:index(1, i)
bboxes = bboxes:index(1, i)
while i:dim() ~= 0 do
local idx = i[1]
table.insert(pick, idx)
local overlaps = JaccardOverlap(bboxes, original_bboxes[idx]):view(-1)
local diff_bboxes = torch.add(overlaps:lt(threshold), classes:ne(original_classes[idx])):ne(0)
i = i[diff_bboxes]
if i:dim() == 0 then
break
end
classes = classes[diff_bboxes]
local non_zero = diff_bboxes:nonzero()
bboxes = bboxes:index(1, non_zero:reshape(non_zero:size(1)))
end
pick = torch.LongTensor{pick}:reshape(#pick)
return original_bboxes:index(1, pick), original_classes:index(1, pick), original_conf:index(1, pick)
end
function Detect(model, imgs, nms_threshold, conf_threshold, cfg)
if imgs:dim() ~= 4 then
imgs = imgs:reshape(1, imgs:size(1), imgs:size(2), imgs:size(3))
end
local outputs = model:forward(imgs:cuda())
local conf_preds = outputs[2]:float()
local all_bboxes = {}
local all_classes = {}
local all_scores = {}
local prior_bboxes = GetPriorBBoxes(cfg)
for i = 1, imgs:size(1) do
local loc_preds = DecodeBBox(outputs[1][i]:float(), prior_bboxes, cfg.variance)
local softmax_conf = nn.SoftMax():forward(conf_preds[i]):view(-1,cfg.classes)
local conf, cls = softmax_conf:narrow(2,2,cfg.classes-1):max(2)
conf = conf:view(-1)
local idx = conf:ge(conf_threshold)
if idx:sum() ~= 0 then
cls = cls[idx]
conf = conf[idx]
local non_zero = idx:nonzero()
loc_preds = loc_preds:index(1, non_zero:reshape(non_zero:size(1)))
local bboxes, classes, score = NMS(loc_preds, conf:view(-1), cls:view(-1), nms_threshold)
bboxes[{{}, {1}}] = torch.cmax(bboxes[{{}, {1}}], 0)
bboxes[{{}, {2}}] = torch.cmax(bboxes[{{}, {2}}], 0)
bboxes[{{}, {3}}] = torch.cmin(bboxes[{{}, {3}}], 1)
bboxes[{{}, {4}}] = torch.cmin(bboxes[{{}, {4}}], 1)
table.insert(all_bboxes, bboxes)
table.insert(all_classes, classes)
table.insert(all_scores, score)
else
local dammy_tensor = torch.Tensor{0}
table.insert(all_bboxes, dammy_tensor)
table.insert(all_classes, dammy_tensor)
table.insert(all_scores, dammy_tensor)
end
end
return all_bboxes, all_classes, all_scores
end
function DrawRect(img, box, cls, index2class)
for i = 1, box:size(1) do
img = image.drawRect(img, box[i][1], box[i][2], box[i][3], box[i][4])
img = image.drawText(img, index2class[cls[i]], box[i][1], box[i][2], {color={255,255,255}, bg={0,0,255}, size=1})
end
return img
end
function VDetect(model, imgs, cfg)
if imgs:dim() ~= 4 then
imgs = imgs:reshape(1, imgs:size(1), imgs:size(2), imgs:size(3))
end
--local timer = torch.Timer()
local outputs = model:forward(imgs:cuda())
--print('Time elapsed for detection ' .. timer:time().real .. ' seconds')
local conf_preds = outputs[2]:float()
local all_bboxes = {}
local all_scores = {}
local prior_bboxes = GetPriorBBoxes(cfg)
local conf
local loc_preds
for i = 1, imgs:size(1) do
--print(outputs[1])
loc_preds = DecodeBBox(outputs[1][i]:float(), prior_bboxes, cfg.variance)
local softmax_conf = nn.SoftMax():forward(conf_preds[i]):view(-1,cfg.classes)
conf = softmax_conf[{{},{2}}]
--print(torch.squeeze(softmax_conf[{{},{2}}]:gt(0.5),2))-- -- the max confidence prediction in every bbox
--print(softmax_conf[{{},{2}}]:gt(0.5):nonzero())
local idx = torch.squeeze(softmax_conf[{{},{2}}]:gt(0.5),2):nonzero()
if idx:sum() ~= 0 then
conf = conf:index(1,torch.squeeze(idx,2))
local non_zero = idx:nonzero()
loc_preds = loc_preds:index(1,torch.squeeze(idx,2))
return conf,loc_preds
--print(loc_preds, conf)
else
--print('the match prediction does not exist')
return false, false
end
end -- print(conf,loc_preds)
end
function Top1Detect(model, imgs, cfg)
if imgs:dim() ~= 4 then
imgs = imgs:reshape(1, imgs:size(1), imgs:size(2), imgs:size(3))
end
-- local timer = torch.Timer()
local outputs = model:forward(imgs:cuda())
-- print('Time elapsed for detection ' .. timer:time().real .. ' seconds')
local conf_preds = outputs[2]:float()
local prior_bboxes = GetPriorBBoxes(cfg)
local conf
local loc_preds
--[[for i = 1, imgs:size(1) do
-- print(outputs[1])
loc_preds = DecodeBBox(outputs[1][i]:float(), prior_bboxes, cfg.variance)
print(loc_preds[1])
local softmax_conf = nn.SoftMax():forward(conf_preds[i]):view(-1,cfg.classes)
conf = softmax_conf[{{},{2}}]
local idx = torch.squeeze(softmax_conf[{{},{2}}]:gt(0.5),2):nonzero() --
local maxconf ,maxid = torch.max(torch.squeeze(softmax_conf[{{},{2}}], 2),1)
print(maxconf, maxid)
conf = conf:index(1,maxid)
loc_preds = loc_preds:index(1,maxid)
return conf, loc_preds
end--]]
for i = 1, imgs:size(1) do
-- print(outputs[1])
loc_preds = DecodeBBox(outputs[1][i]:float(), prior_bboxes, cfg.variance)
local softmax_conf = nn.SoftMax():forward(conf_preds[i]):view(-1,cfg.classes)
conf = softmax_conf[{{},{2}}]
--print(conf)
local idx = torch.squeeze(softmax_conf[{{},{2}}]:gt(0.5),2):nonzero() --
-- maxconf ,maxid = torch.squeeze(softmax_conf[{{},{2}}]:gt(0.5),2)
local maxconf ,maxid = torch.max(softmax_conf[{{},{2}}],1)
--print(maxconf, maxid)
conf = conf:index(1,torch.squeeze(maxid,2))
loc_preds = loc_preds:index(1,torch.squeeze(maxid,2))
return conf, loc_preds
end
end
-------------
function isInRect(point,vertex)
local isVertical = false
for t=1,3 do
if vertex.x[t] == vertex.x[t+1] then
isVertical = t
end
end
if not isVertical then -- if the box is not vertical
local k = {}
for t = 1,3 do
k[t] = (vertex.y[t+1]-vertex.y[t])/(vertex.x[t+1]-vertex.x[t]);
end
k[4] = (vertex.y[1]-vertex.y[4])/(vertex.x[1]-vertex.x[4]);
local isUp = {}
for t = 1,4 do
isUp[t] = point.y - vertex.y[t] -k[t]*(point.x - vertex.x[t]);
end
if isUp[1]*isUp[3]<0 and isUp[2]*isUp[4]<0 then
return true
else
return false
end
else
--print(isVertical,(isVertical+2)%4)
local next_idx
if isVertical == 2 then
next_idx = 4
else
next_idx = (isVertical+2)%4
end
if (point.x - vertex.x[isVertical])*(point.x - vertex.x[next_idx])<0 then
if (point.y - vertex.y[isVertical])*(point.y - vertex.y[next_idx])<0 then
return true
end
return false
end
return false
end
end
function computeIOU(rec1,rec2)
local xmin = math.min(rec1.x[1],rec1.x[2],rec1.x[3],rec1.x[4],rec2.x[1],rec2.x[2],rec2.x[3],rec2.x[4])
local ymin = math.min(rec1.y[1],rec1.y[2],rec1.y[3],rec1.y[4],rec2.y[1],rec2.y[2],rec2.y[3],rec2.y[4])
local xmax = math.max(rec1.x[1],rec1.x[2],rec1.x[3],rec1.x[4],rec2.x[1],rec2.x[2],rec2.x[3],rec2.x[4])
local ymax = math.max(rec1.y[1],rec1.y[2],rec1.y[3],rec1.y[4],rec2.y[1],rec2.y[2],rec2.y[3],rec2.y[4])
local mask = torch.Tensor(math.ceil(ymax)-math.ceil(ymin)+1,math.ceil(xmax)-math.ceil(xmin)+1):zero()
local countMerge = 0
local countInteraction = 0
for w = math.ceil(xmin),math.ceil(xmax) do
for h = math.ceil(ymin),math.ceil(ymax) do
local point = {x=w,y=h}
if isInRect(point,rec1) then
mask[{{h-math.ceil(ymin)+1},{w-math.ceil(xmin)+1}}] = mask[{{h-math.ceil(ymin)+1},{w-math.ceil(xmin)+1}}]+1
countMerge = countMerge+1
end
if isInRect(point,rec2) then
mask[{{h-math.ceil(ymin)+1},{w-math.ceil(xmin)+1}}] = mask[{{h-math.ceil(ymin)+1},{w-math.ceil(xmin)+1}}]+1
if mask[{h-math.ceil(ymin)+1,w-math.ceil(xmin)+1}] == 2 then
countInteraction = countInteraction+1
else
countMerge = countMerge+1
end
end
end
end
return countInteraction/countMerge
end
function evaluate(model, testpath, img_dir, epoch, cfg)
model:evaluate()
local per_img_acc = {}
local pred_num = 0
local succ_pred = 0
local pic_num_ND = 0 -- picture number of zero prediction
local run_time = 0
local timer = torch.Timer()
local PPPI = 0
local NO_MATCH = 0
for i,img_name in pairs(testpath) do
--img_path = paths.concat(img_dir,img_name) --
--img = image.load(img_path)
--[[local img = torch.FloatTensor(3,480,640)
local loadedimg = image.load(paths.concat(opt.root, 'cornell_dataset', 'image', img_name)) --load RGB img
img[{{1},{},{}}] = loadedimg[{{1},{},{}}]
img[{{2},{},{}}] = loadedimg[{{2},{},{}}]
--img[{{3},{},{}}] = loadedimg[{{3},{},{}}]
--print(shuffle[index])
--local img = train_RGBD[trainpath[shuffle[index]]--]
--[[local Dname = string.gsub(img_name,'r','d')
img[{{3},{},{}}] = image.load(paths.concat(opt.root, 'cornell_dataset', 'depth', Dname))
res = img:clone()
res = ImageCrop(res, 0, 0,cfg)
local scores, boxes = Top1Detect(model, res, cfg)
--timer:reset()--]]
img_path = paths.concat(img_dir,img_name)
local starttime = os.clock()
img = image.load(img_path)
--print(test_po - starttime)
res = img:clone()
res = ImageCrop(res, 0, 0,cfg)
--res = res
local scores, boxes = Top1Detect(model, res, cfg)
--print(boxes)
local endtime = os.clock()
local tmp_time = endtime - starttime
--print(scores, boxes)
if boxes == false then
pic_num_ND = 1 + pic_num_ND
--print(img_name,'does not have gt bbox to match with')
else
--print(boxes,scores)
local test_1 = os.clock()
gt = testgt[img_name] -- 8 colunm
goodC, gt = pointCrop(gt, 0, 0,cfg)
gt_label = pointsToLabel(gt)
--print(gt_label)
overlap_matrix = torch.zeros(boxes:size(1),gt:size(1))
pred_ang = torch.expand(boxes[{{},{5}}],boxes:size(1),gt_label:size(1))
--print(pred_ang)
gt_ang = torch.expand(gt_label[{{},{5}}],gt_label:size(1),boxes:size(1)):transpose(1, 2)
--print(gt_ang)
local ang_matched = torch.abs(pred_ang-gt_ang):le(30)
local ang_matched2 = torch.abs(pred_ang-gt_ang):gt(150)
local pred_Tensor = labelToPointsTensor(boxes)
--print(pred_Tensor)
for j =1 , boxes:size(1) do
--
--print(points)
for k=1, gt:size(1) do
local gt_points = TensorToPoints(gt[k])
local pred_points = TensorToPoints(pred_Tensor[j])
overlap_matrix[j][k] = computeIOU(pred_points,gt_points)
end
end
-- local test_2 = os.clock()
--print(test_2 - test_1)
--print(overlap_matrix:ge(0.20))
--print(ang_matched) -- eq(2) means statisfy IoU and angle
local correct_matches = (overlap_matrix:ge(0.25) + ang_matched + ang_matched2):eq(2) -- if one pred bbox match a gt bbox its corresponding index equal to 1
--print(correct_matches)
local gt_match_numbers = torch.sum(torch.sum(correct_matches,1):ge(1))
local match_numbers = torch.sum(torch.sum(correct_matches,2):ge(1)) -- how many pred bbox has its match gt bbox
pred_num = pred_num + boxes:size(1)
succ_pred = succ_pred + match_numbers
--print(img_name,match_numbers)
no_match_number = gt:size(1) - gt_match_numbers
per_img_acc[img_name] = match_numbers/boxes:size(1)
pppi = match_numbers/boxes:size(1)
end
NO_MATCH = NO_MATCH +no_match_number
PPPI = PPPI + pppi
run_time = run_time + tmp_time
end
--print('PPPI is', PPPI/177)
--print('Miss Rate is',NO_MATCH/177)
print('Total accuracy is',succ_pred/pred_num)
print('average run time:',run_time/#testpath)
print('amount of pictures that do not have prediction', pic_num_ND)
if pred_num ==0 then
per_img_acc['total'] = 0
else
per_img_acc['total'] = succ_pred/pred_num
end
per_img_acc['Avgtime'] = run_time/#testpath
per_img_acc['pic_num_ND'] = pic_num_ND
--torch.save(paths.concat('./output/', 'testresult'..epoch..'epoch.t7'), per_img_acc)
model:training()
return per_img_acc
end