def predictwithViewPoint(state, viewPoint):
moved = copy.deepcopy(state)
for m in viewPoint[0]["mean"]:
moved[m] = viewPoint[0]["mean"][m]
moved = encoder.retransformforViewPoint(moved, viewPoint[0]["base"],
viewPoint[0]["ref"])
output = {}
for s in state:
if s in viewPoint[0]["mean"]:
output[s] = moved[s]
else:
output[s] = state[s]
return output
def predictwithViewPoint(state, viewPoint):
moved = copy.deepcopy(state)
base = viewPoint["base"]
ref = viewPoint["ref"]
mean = viewPoint["mean"]
for m in mean:
moved[m] = viewPoint["mean"][m]
moved = encoder.retransformforViewPoint(moved, base, ref)
output = {}
for s in state:
if s in mean:
output[s] = moved[s]
else:
output[s] = state[s]
return output
def getStateswithViewPoint(filepathList, baseList, refList, detail=False):
output = {}
for fpath in filepathList:
if fpath[-4:] != ".csv":
if detail == True:
text = "[ViewPointManager]getSateswithViewPoint:"
print(text + fpath + " is not csv file")
continue
# パスをキーにして管理すると面倒なのでファイル名に変更
filename = os.path.basename(fpath)
# print("file:"+filename)
output[filename] = []
with open(fpath, "r", encoding="utf-8") as f:
while True:
line = f.readline()
if line == "":
break
bstate = encoder.encodeState(line)
astate = encoder.transformforViewPoint(bstate, baseList,
refList)
# data = encoder.serialize(astate)
data = astate
output[filename].append(data)
return output
def getDistribution(stateDict, baseList, refList, movedObject):
# データの数を取得しておく
size = len(stateDict["before"])
# 前後のデータ数が違えばそこでエラー終了
if size != len(stateDict["after"]):
print("[ViewPointManager]getDistribution:different size")
return []
# ゼロ個だったら何かおかしいのでエラー終了
if size == 0:
print("[ViewPointManager]getDistribution:size is zero")
return []
move = []
transafter = []
for i in range(size):
transafter.append( \
encoder.transformforViewPoint( \
stateDict["after"][i], baseList, refList \
) \
)
# 選択した物体の最終位置のブレをスコアとして返す
# 観点変換はここで使う
diffDict = {}
for o in movedObject:
diffDict[o] = []
for i in range(size):
for s in movedObject:
diffDict[s].append(np.array(transafter[i][s]))
meanDict = {}
for s in movedObject:
meanDict[s] = sum(diffDict[s]) / size
diff = 0
for i in range(size):
for s in movedObject:
diff += np.linalg.norm(np.array(transafter[i][s]) - meanDict[s])
return [meanDict, diff]
# coding = utf-8
# log データを可視化してチェックするだけ
import glob
import os
import HDP_HMM.MakerMain as makerMain
import DPM.ViewPointEncoder as encoder
import DPM.ViewPointManager as manager
fpaths = glob.glob("tmp/log_MakerMain/*")
for fpath in fpaths:
if os.path.basename(fpath)[:3] != "log" \
or fpath[-4:] != ".csv":
continue
if int(os.path.basename(fpath)[-6:-4]) > 2:
continue
with open(fpath, "r", encoding="utf-8") as f:
count = 0
while True:
line = f.readline()
if line == "":
break
if count%100 == 0:
manager.show(encoder.encodeState(line), title=fpath)
count += 1
# coding = utf-8
import DPM.ViewPointEncoder as encoder
import DPM.ViewPointManager as manager
interList = []
path="tmp/log_MakerMain/GettingIntermediated/3-2500-2500-9/inter000000000.csv"
with open(path) as f:
while True:
line = f.readline()
if line == "":
break
interList.append(int(line.split(",")[0]))
print("inters:"+str(interList))
with open("tmp/log_MakerMain/log000000000.csv", "r", encoding="utf-8") as f:
counter = 0
while True:
counter+=1
line = f.readline()
if line == "":
break
elif counter%6 != 0 and counter not in interList:
continue
state = encoder.encodeState(line)
name = "log000000000_"+str(counter)+".png"
inter = counter in interList
manager.savefig(state, name=name, log=True, inter=inter)