def DP_main_2(datas, interDict, sampleSize=0.5, n_iter=50, distError=0.005):
output = {"matching": [], "pending": []}
rests = copy.deepcopy(interDict)
# 境界が 0 番, 500番以外にないデータはここで除外する
temprests = {}
for r in rests:
if len(rests[r]) > 2:
temprests[r] = rests[r]
rests = temprests
for r in rests:
print(rests[r])
# 最初の stateDict を作る
stateDict = {"before": [], "after": [], "fname": [], "isadd": []}
for fname in rests:
fdata = datas[fname]
stateDict["before"].append(fdata[rests[fname].pop(0)])
stateDict["after"].append(fdata[rests[fname].pop(0)])
stateDict["fname"].append(fname)
stateDict["isadd"].append(False)
while True:
# rests が空なら終了
flg = True
for fname in rests:
if len(rests[fname]) != 0:
flg = False
break
if flg == True:
break
# サンプリングを用いて vp を取得
vp = manager.getViewPointwithSampling(stateDict, sampleSize, n_iter)
print("get vp:" + str(vp[0]["base"]) + "," + str(vp[0]["ref"]))
# これをもとに predicts を作成
predicts = {}
moved = manager.getMovedObject(stateDict)
for i in range(len(stateDict["before"])):
# step を使う場合は -1
predicts[stateDict["fname"][i]] = getAdditionalIntermediate(
datas[stateDict["fname"][i]],
stateDict["before"][i]["step"] - 1, vp)
# predicts と after の関係から，動作を決定する
matching = {"before": [], "after": [], "fname": [], "isadd": []}
pending = {}
for i in range(len(stateDict["before"])):
# predicts と after の差が score の一定倍程度なら
# matching として保存
matching["before"].append(stateDict["before"][i])
matching["after"].append(predicts[stateDict["fname"][i]])
matching["fname"].append(stateDict["fname"][i])
matching["isadd"].append(stateDict["isadd"][i])
# 新手法は matching すべてに対して predicts を適用していくという
# ものなので，ignored に対して特別な処理はしない
# pending であるもののみ，rests に返却するために保存する
print("aaa" + str(predicts[stateDict["fname"][i]]))
print("bbb" + str(stateDict["after"][i]))
if manager.calcDifference(
predicts[stateDict["fname"][i]],
stateDict["after"][i],
objs=moved) >= vp[0]["score"] * distError and predicts[
stateDict["fname"]
[i]]["step"] < stateDict["after"][i]["step"]:
pending[stateDict["fname"][i]] = stateDict["after"][i]["step"]
# rests に pending を返却
for fname in pending:
# インデックスのずれを修正するために -1
rests[fname] = [pending[fname] - 1] + rests[fname]
# output に matching と pending を追加
output["matching"].append(matching)
output["pending"].append(pending)
with open("tmp/log_MakerMain/dills/DP_main_2_temp.dill", "wb") as f:
dill.dump(output, f)
print("new Matching")
print([[matching["before"][i]["step"], matching["after"][i]["step"]]
for i in range(len(matching["after"]))])
print("new Pending")
print([pending[fname] for fname in pending])
# 次の stateDict を作る
stateDict = {"before": [], "after": [], "fname": [], "isadd": []}
for fname in rests:
fdata = datas[fname]
stateDict["before"].append(predicts[fname])
if len(rests[fname]) > 0:
stateDict["after"].append(fdata[rests[fname].pop(0)])
else:
print("EMPTY - " + fname)
stateDict["after"].append(fdata[-1])
stateDict["fname"].append(fname)
stateDict["isadd"].append(False)
return output
def DP_main(datas, interDict):
output = {"matching": [], "pending": []}
rests = copy.deepcopy(interDict)
# 境界が 0 番, 500番以外にないデータはここで除外する
temprests = {}
for r in rests:
if len(rests[r]) > 2:
temprests[r] = rests[r]
rests = temprests
# 最初の stateDict を作る
stateDict = {"before": [], "after": [], "fname": [], "isadd": []}
for fname in rests:
fdata = datas[fname]
stateDict["before"].append(fdata[rests[fname].pop(0)])
stateDict["after"].append(fdata[rests[fname].pop(0)])
stateDict["fname"].append(fname)
stateDict["isadd"].append(False)
while True:
# rests が空なら終了
flg = True
for fname in rests:
if len(rests[fname]) != 0:
flg = False
break
if flg == True:
break
# サンプリングを用いて vp を取得
vp = manager.getViewPointwothSampling(stateDict)
# これをもとに predicts を作成
predicts = {}
moved = manager.getMovedObject(stateDict)
for i in range(len(stateDict["before"])):
predicts[stateDict["fname"][i]] = getAdditionalIntermediate(
datas[pending["fname"][i]], pending["before"][i]["step"], vp)
# predicts と after の関係から，動作を決定する
matching = {"before": [], "after": [], "fname": [], "isadd": []}
# ignored = {"before":[], "after":[], "fname":[], "isadd":[]}
# pending = {"before":[], "after":[], "fname":[], "isadd":[]}
pending = {}
for i in range(len(stateDict["before"])):
# predicts と after の差が score の一定倍程度なら
# matching として保存
if manager.calcDifference(predicts[stateDict["fname"][i]],
stateDict["after"],
objs=moved) < vp["score"] * 0.8:
matching["before"].append(stateDict["before"][i])
matching["after"].append(predicts[stateDict["fname"][i]])
matching["fname"].append(stateDict["fname"])
matching["isadd"].append(stateDict["isadd"])
# 新手法は matching すべてに対して predicts を適用していくという
# ものなので，ignored に対して特別な処理はしない
# pending であるもののみ，rests に返却するために保存する
elif predicts[stateDict["fname"]
[i]]["step"] < stateDict["after"][i]["step"]:
pending[stateDict["fname"][i]] = stateDict["after"][i]["step"]
# rests に pending を返却
for fname in pending:
rests[fname] = [pending[fname]] + rests[fname]
# output に matching と pending を追加
output["matching"].append(matching)
output["pending"].append(pending)
# 次の stateDict を作る
stateDict = {"before": [], "after": [], "fname": [], "isadd": []}
for fname in rests:
fdata = datas[fname]
stateDict["before"].append(predicts[fname])
stateDict["after"].append(fdata[rests[fname].pop(0)])
stateDict["fname"].append(fname)
stateDict["isadd"].append(False)
for fname in stateDict["fname"]:
# すでに境界状態を使い果たした（本来そうならないでほしいが）fnameを表示して無視
if len(rests[fname]) == 0:
print("[predictMatching]DP_main:Empty - " + fname)
for i in range(len(stateDict["fname"])):
if stateDict["fname"][i] == fname:
stateDict["before"].pop(i)
stateDict["fname"].pop(i)
break
continue
return output
def DP_main(datas, interDict_, sampleSize=0.7, n_iter=50, distError=50):
interDict = pruningInterDict_2(datas, interDict_)
output = {"matching":[], "viewpoint":[]}
rests = copy.deepcopy(interDict)
# 最初の stateDict を作る
stateDict = {}
for fname in rests.keys():
fdata = datas[fname]
stateDict[fname] = fdata[rests[fname].pop(0)]
# matching に登録する
output["matching" ].append(stateDict)
output["viewpoint"].append(None)
while True:
# rests が空なら終了
flg = True
for fname in rests.keys():
if len(rests[fname]) != 0:
flg = False
break
if flg == True:
break
# 次の stateDict を作る
stateDict = {}
for fname in rests.keys():
fdata = datas[fname]
while True:
if len(rests[fname])>0 and rests[fname][0] < output["matching"][-1][fname]["step"]+10:
rests[fname].pop(0)
else:
break
if len(rests[fname]) > 0:
stateDict[fname] = fdata[rests[fname].pop(0)]
else:
# print("EMPTY - " + fname)
stateDict[fname] = fdata[-1]
# サンプリングを用いて vp を取得
fnameList = sorted(list(stateDict.keys()))
beforeDict = output["matching"][-1]
beforeList = [beforeDict[fn] for fn in fnameList]
afterList = [stateDict[fn] for fn in fnameList]
tempDict = {"before":beforeList, "after":afterList, "fname":fnameList}
vp = manager.getViewPointwithSampling(tempDict,sampleSize,n_iter)
print("get vp:" + str(vp["base"]) + "," + str(vp["ref"]))
matching = {}
moved = manager.getMovedObject(tempDict)
for fname in stateDict.keys():
# vp を用いて，境界の推定をやり直す
bStep = output["matching"][-1][fname]["step"]
addInter = getAdditionalIntermediate(datas[fname], bStep, vp)
# matching の更新
matching[fname] = addInter
# output に matching と pending と vp を追加
output["matching" ].append(matching)
output["viewpoint"].append(vp)
with open("tmp/log_MakerMain/dills/DP_main_temp.dill", "wb") as f:
dill.dump(output, f)
return output