def batch_rgbtb(root):
df = get_trip(root)
in_dirs = [
osp.join(root, 'rgbc', 'origin'),
osp.join(root, 'rgbc', 'derived')
]
dfi = df.applymap(lambda x: osp.join(in_dirs[0], x + '.jpg'))
dfi[["fo",
"m"]] = df[["f", "ab"
]].applymap(lambda x: osp.join(in_dirs[1], x + '.jpg'))
dfi = dfi.to_numpy()
out_dirs = [
osp.join(root, 'rgbc', 'thumbnail', 'origin'),
osp.join(root, 'rgbc', 'thumbnail', 'derived')
]
for d in out_dirs:
mkdir(d)
dfo = df.applymap(lambda x: osp.join(out_dirs[0], x + '.jpg'))
dfo[["fo",
"m"]] = df[["f", "ab"
]].applymap(lambda x: osp.join(out_dirs[1], x + '.jpg'))
dfo = dfo.to_numpy()
with Pool(16, initialize_tbnail, (16, )) as p:
ares = p.starmap_async(tbnail_rgb, zip(dfi, dfo))
ares.wait()
def batch_crop_rgb(root):
df = get_trip(root)
in_dirs = [
osp.join(root, 'rgb', 'origin'),
osp.join(root, 'rgb', 'derived')
]
dfi = df.applymap(lambda x: osp.join(in_dirs[0], x + '.jpg'))
dfi[["fo",
"m"]] = df[["f", "ab"
]].applymap(lambda x: osp.join(in_dirs[1], x + '.jpg'))
dfi = dfi.to_numpy()
out_dirs = [
osp.join(root, 'rgbc', 'origin'),
osp.join(root, 'rgbc', 'derived')
]
for d in out_dirs:
mkdir(d)
dfo = df.applymap(lambda x: osp.join(out_dirs[0], x + '.jpg'))
dfo[["fo",
"m"]] = df[["f", "ab"
]].applymap(lambda x: osp.join(out_dirs[1], x + '.jpg'))
dfo = dfo.to_numpy()
dfbd = (pd.read_csv(osp.join(root,
root + '.csv'))[["w1", "h1", "w2",
"h2"]]).to_numpy()
# print(dfi[50])
# crop_rgb(dfi[50],dfo[50],dfbd[50])
# return
with Pool(16) as p:
ares = p.starmap_async(crop_rgb, zip(dfi, dfo, dfbd))
ares.wait()
def cvts(root, fm1='dng', fm2='jpg', num_worker=3):
dir1, dir2 = osp.join(root, fm1), osp.join(root, fm2)
ls1 = os.listdir(dir1)
mkdir(dir2)
outputs = bj(dir2, be(ls1, fm2))
inputs = bj(dir1, ls1)
with Pool(num_worker) as p:
p.starmap_async(convert, zip(inputs, outputs))
def save(self, filepath: str) -> None:
df = DataFrame(columns=("num", "rank", "num_batches", "cost"))
for epoch in self.epochs:
row = {
"num": epoch.num,
"rank": epoch.rank,
"num_batches": epoch.num_batches,
"cost": epoch.cost,
}
df = df.append(row, ignore_index=True)
mkdir(filepath)
df.to_csv(filepath)
def batch5rgb(root: str, processes=16):
df = get_trip(root)
dfi = df.applymap(lambda x: osp.join(root, 'raw', x + '.dng')).to_numpy()
out_dirs = [
osp.join(root, 'rgb', 'origin'),
osp.join(root, 'rgb', 'derived')
]
for d in out_dirs:
mkdir(d)
dfo = df.applymap(lambda x: osp.join(out_dirs[0], x + '.jpg'))
dfo[["fo",
"m"]] = df[["f", "ab"
]].applymap(lambda x: osp.join(out_dirs[1], x + '.jpg'))
dfo = dfo.to_numpy()
# get5rgb(dfi[0],dfo[0])
# return
with Pool(16) as p:
ares = p.starmap_async(get5rgb, zip(dfi, dfo))
ares.wait()
def batch5rawc(root: str, processes=16):
df = get_trip(root)
dfi = df.applymap(lambda x: osp.join(root, 'raw', x + '.dng')).to_numpy()
out_dirs = [
osp.join(root, 'rawc', 'origin'),
osp.join(root, 'rawc', 'derived')
]
for d in out_dirs:
mkdir(d)
dfo = df.applymap(lambda x: osp.join(out_dirs[0], x + '.png'))
dfo[["fo",
"m"]] = df[["f", "ab"
]].applymap(lambda x: osp.join(out_dirs[1], x + '.png'))
dfo = dfo.to_numpy()
dfbd = (pd.read_csv(osp.join(root,
root + '.csv'))[["w1", "h1", "w2",
"h2"]]).to_numpy()
# get5rawc(dfi[0],dfo[0],dfbd[0])
# return
with Pool(16) as p:
ares = p.starmap_async(get5rawc, zip(dfi, dfo, dfbd))
ares.wait()
