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@@ -0,0 +1,437 @@
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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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+#
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+# Licensed under the Apache License, Version 2.0 (the "License");
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+# you may not use this file except in compliance with the License.
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+# You may obtain a copy of the License at
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+#
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+# http://www.apache.org/licenses/LICENSE-2.0
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+#
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+# Unless required by applicable law or agreed to in writing, software
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+# distributed under the License is distributed on an "AS IS" BASIS,
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+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+# See the License for the specific language governing permissions and
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+# limitations under the License.
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+
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+import os
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+import os.path as osp
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+import math
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+from abc import ABCMeta, abstractmethod
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+from collections import Counter, defaultdict
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+
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+import numpy as np
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+from tqdm import tqdm
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+
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+import paddlers.utils.logging as logging
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+
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+
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+class Cache(metaclass=ABCMeta):
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+ @abstractmethod
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+ def get_block(self, i_st, j_st, h, w):
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+ pass
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+
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+
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+class SlowCache(Cache):
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+ def __init__(self):
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+ super(SlowCache, self).__init__()
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+ self.cache = defaultdict(Counter)
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+
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+ def push_pixel(self, i, j, l):
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+ self.cache[(i, j)][l] += 1
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+
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+ def push_block(self, i_st, j_st, h, w, data):
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+ for i in range(0, h):
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+ for j in range(0, w):
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+ self.push_pixel(i_st + i, j_st + j, data[i, j])
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+
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+ def pop_pixel(self, i, j):
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+ self.cache.pop((i, j))
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+
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+ def pop_block(self, i_st, j_st, h, w):
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+ for i in range(0, h):
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+ for j in range(0, w):
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+ self.pop_pixel(i_st + i, j_st + j)
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+
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+ def get_pixel(self, i, j):
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+ winners = self.cache[(i, j)].most_common(1)
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+ winner = winners[0]
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+ return winner[0]
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+
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+ def get_block(self, i_st, j_st, h, w):
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+ block = []
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+ for i in range(i_st, i_st + h):
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+ row = []
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+ for j in range(j_st, j_st + w):
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+ row.append(self.get_pixel(i, j))
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+ block.append(row)
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+ return np.asarray(block)
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+
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+
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+class ProbCache(Cache):
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+ def __init__(self, h, w, ch, cw, sh, sw, dtype=np.float32, order='c'):
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+ super(ProbCache, self).__init__()
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+ self.cache = None
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+ self.h = h
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+ self.w = w
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+ self.ch = ch
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+ self.cw = cw
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+ self.sh = sh
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+ self.sw = sw
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+ if not issubclass(dtype, np.floating):
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+ raise TypeError("`dtype` must be one of the floating types.")
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+ self.dtype = dtype
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+ order = order.lower()
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+ if order not in ('c', 'f'):
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+ raise ValueError("`order` other than 'c' and 'f' is not supported.")
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+ self.order = order
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+
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+ def _alloc_memory(self, nc):
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+ if self.order == 'c':
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+ # Colomn-first order (C-style)
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+ #
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+ # <-- cw -->
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+ # |--------|---------------------|^ ^
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+ # | || | sh
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+ # |--------|---------------------|| ch v
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+ # | ||
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+ # |--------|---------------------|v
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+ # <------------ w --------------->
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+ self.cache = np.zeros((self.ch, self.w, nc), dtype=self.dtype)
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+ elif self.order == 'f':
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+ # Row-first order (Fortran-style)
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+ #
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+ # <-- sw -->
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+ # <---- cw ---->
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+ # |--------|---|^ ^
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+ # | | || |
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+ # | | || ch
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+ # | | || |
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+ # |--------|---|| h v
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+ # | | ||
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+ # | | ||
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+ # | | ||
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+ # |--------|---|v
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+ self.cache = np.zeros((self.h, self.cw, nc), dtype=self.dtype)
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+
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+ def update_block(self, i_st, j_st, h, w, prob_map):
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+ if self.cache is None:
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+ nc = prob_map.shape[2]
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+ # Lazy allocation of memory
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+ self._alloc_memory(nc)
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+ self.cache[i_st:i_st + h, j_st:j_st + w] += prob_map
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+
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+ def roll_cache(self, shift):
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+ if self.order == 'c':
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+ self.cache[:-shift] = self.cache[shift:]
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+ self.cache[-shift:, :] = 0
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+ elif self.order == 'f':
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+ self.cache[:, :-shift] = self.cache[:, shift:]
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+ self.cache[:, -shift:] = 0
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+
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+ def get_block(self, i_st, j_st, h, w):
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+ return np.argmax(self.cache[i_st:i_st + h, j_st:j_st + w], axis=2)
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+
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+
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+class OverlapProcessor(metaclass=ABCMeta):
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+ def __init__(self, h, w, ch, cw, sh, sw):
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+ super(OverlapProcessor, self).__init__()
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+ self.h = h
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+ self.w = w
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+ self.ch = ch
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+ self.cw = cw
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+ self.sh = sh
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+ self.sw = sw
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+
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+ @abstractmethod
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+ def process_pred(self, out, xoff, yoff):
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+ pass
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+
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+
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+class KeepFirstProcessor(OverlapProcessor):
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+ def __init__(self, h, w, ch, cw, sh, sw, ds, inval=255):
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+ super(KeepFirstProcessor, self).__init__(h, w, ch, cw, sh, sw)
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+ self.ds = ds
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+ self.inval = inval
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+
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+ def process_pred(self, out, xoff, yoff):
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+ pred = out['label_map']
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+ pred = pred[:self.ch, :self.cw]
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+ rd_block = self.ds.ReadAsArray(xoff, yoff, self.cw, self.ch)
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+ mask = rd_block != self.inval
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+ pred = np.where(mask, rd_block, pred)
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+ return pred
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+
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+
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+class KeepLastProcessor(OverlapProcessor):
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+ def process_pred(self, out, xoff, yoff):
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+ pred = out['label_map']
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+ pred = pred[:self.ch, :self.cw]
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+ return pred
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+
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+
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+class AccumProcessor(OverlapProcessor):
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+ def __init__(self,
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+ h,
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+ w,
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+ ch,
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+ cw,
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+ sh,
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+ sw,
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+ dtype=np.float16,
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+ assign_weight=True):
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+ super(AccumProcessor, self).__init__(h, w, ch, cw, sh, sw)
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+ self.cache = ProbCache(h, w, ch, cw, sh, sw, dtype=dtype, order='c')
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+ self.prev_yoff = None
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+ self.assign_weight = assign_weight
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+
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+ def process_pred(self, out, xoff, yoff):
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+ if self.prev_yoff is not None and yoff != self.prev_yoff:
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+ if yoff < self.prev_yoff:
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+ raise RuntimeError
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+ self.cache.roll_cache(yoff - self.prev_yoff)
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+ pred = out['label_map']
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+ pred = pred[:self.ch, :self.cw]
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+ prob = out['score_map']
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+ prob = prob[:self.ch, :self.cw]
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+ if self.assign_weight:
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+ prob = assign_border_weights(prob, border_ratio=0.25, inplace=True)
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+ self.cache.update_block(0, xoff, self.ch, self.cw, prob)
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+ pred = self.cache.get_block(0, xoff, self.ch, self.cw)
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+ self.prev_yoff = yoff
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+ return pred
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+
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+
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+def assign_border_weights(array, weight=0.5, border_ratio=0.25, inplace=True):
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+ if not inplace:
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+ array = array.copy()
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+ h, w = array.shape[:2]
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+ hm, wm = int(h * border_ratio), int(w * border_ratio)
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+ array[:hm] *= weight
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+ array[-hm:] *= weight
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+ array[:, :wm] *= weight
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+ array[:, -wm:] *= weight
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+ return array
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+
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+
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+def read_block(ds,
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+ xoff,
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+ yoff,
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+ xsize,
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+ ysize,
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+ tar_xsize=None,
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+ tar_ysize=None,
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+ pad_val=0):
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+ if tar_xsize is None:
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+ tar_xsize = xsize
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+ if tar_ysize is None:
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+ tar_ysize = ysize
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+ # Read data from dataset
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+ block = ds.ReadAsArray(xoff, yoff, xsize, ysize)
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+ c, real_ysize, real_xsize = block.shape
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+ assert real_ysize == ysize and real_xsize == xsize
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+ # [c, h, w] -> [h, w, c]
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+ block = block.transpose((1, 2, 0))
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+ if (real_ysize, real_xsize) != (tar_ysize, tar_xsize):
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+ if real_ysize >= tar_ysize or real_xsize >= tar_xsize:
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+ raise ValueError
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+ padded_block = np.full(
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+ (tar_ysize, tar_xsize, c), fill_value=pad_val, dtype=block.dtype)
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+ # Fill
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+ padded_block[:real_ysize, :real_xsize] = block
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+ return padded_block
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+ else:
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+ return block
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+
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+
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+def slider_predict(predict_func,
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+ img_file,
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+ save_dir,
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+ block_size,
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+ overlap,
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+ transforms,
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+ invalid_value,
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+ merge_strategy,
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+ batch_size,
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+ show_progress=False):
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+ """
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+ Do inference using sliding windows.
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+
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+ Args:
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+ predict_func (callable): A callable object that makes the prediction.
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+ img_file (str|tuple[str]): Image path(s).
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+ save_dir (str): Directory that contains saved geotiff file.
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+ block_size (list[int] | tuple[int] | int):
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+ Size of block. If `block_size` is list or tuple, it should be in
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+ (W, H) format.
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+ overlap (list[int] | tuple[int] | int):
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+ Overlap between two blocks. If `overlap` is list or tuple, it should
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+ be in (W, H) format.
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+ transforms (paddlers.transforms.Compose|None): Transforms for inputs. If
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+ None, the transforms for evaluation process will be used.
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+ invalid_value (int): Value that marks invalid pixels in output image.
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+ Defaults to 255.
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+ merge_strategy (str): Strategy to merge overlapping blocks. Choices are
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+ {'keep_first', 'keep_last', 'accum'}. 'keep_first' and 'keep_last'
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+ means keeping the values of the first and the last block in
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+ traversal order, respectively. 'accum' means determining the class
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+ of an overlapping pixel according to accumulated probabilities.
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+ batch_size (int): Batch size used in inference.
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+ show_progress (bool, optional): Whether to show prediction progress with a
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+ progress bar. Defaults to True.
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+ """
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+
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+ try:
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+ from osgeo import gdal
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+ except:
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+ import gdal
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+
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+ if isinstance(block_size, int):
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+ block_size = (block_size, block_size)
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+ elif isinstance(block_size, (tuple, list)) and len(block_size) == 2:
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+ block_size = tuple(block_size)
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+ else:
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+ raise ValueError(
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+ "`block_size` must be a tuple/list of length 2 or an integer.")
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+ if isinstance(overlap, int):
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+ overlap = (overlap, overlap)
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+ elif isinstance(overlap, (tuple, list)) and len(overlap) == 2:
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+ overlap = tuple(overlap)
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+ else:
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+ raise ValueError(
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+ "`overlap` must be a tuple/list of length 2 or an integer.")
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+
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+ step = np.array(
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+ block_size, dtype=np.int32) - np.array(
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+ overlap, dtype=np.int32)
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+ if step[0] == 0 or step[1] == 0:
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+ raise ValueError("`block_size` and `overlap` should not be equal.")
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+
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+ if isinstance(img_file, tuple):
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+ if len(img_file) != 2:
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+ raise ValueError("Tuple `img_file` must have the length of two.")
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+ # Assume that two input images have the same size
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+ src_data = gdal.Open(img_file[0])
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+ src2_data = gdal.Open(img_file[1])
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+ # Output name is the same as the name of the first image
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+ file_name = osp.basename(osp.normpath(img_file[0]))
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+ else:
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+ src_data = gdal.Open(img_file)
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+ file_name = osp.basename(osp.normpath(img_file))
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+
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+ # Get size of original raster
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+ width = src_data.RasterXSize
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+ height = src_data.RasterYSize
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+ bands = src_data.RasterCount
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+
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+ # XXX: GDAL read behavior conforms to paddlers.transforms.decode_image(read_raw=True)
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+ # except for SAR images.
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+ if bands == 1:
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+ logging.warning(
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+ f"Detected `bands=1`. Please note that currently `slider_predict()` does not properly handle SAR images."
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+ )
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+
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+ if block_size[0] > width or block_size[1] > height:
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+ raise ValueError("`block_size` should not be larger than image size.")
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+
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+ driver = gdal.GetDriverByName("GTiff")
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+ if not osp.exists(save_dir):
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+ os.makedirs(save_dir)
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+ # Replace extension name with '.tif'
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+ file_name = osp.splitext(file_name)[0] + ".tif"
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+ save_file = osp.join(save_dir, file_name)
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+ dst_data = driver.Create(save_file, width, height, 1, gdal.GDT_Byte)
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+
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+ # Set meta-information
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+ dst_data.SetGeoTransform(src_data.GetGeoTransform())
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+ dst_data.SetProjection(src_data.GetProjection())
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+
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+ # Initialize raster with `invalid_value`
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+ band = dst_data.GetRasterBand(1)
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+ band.WriteArray(
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+ np.full(
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+ (height, width), fill_value=invalid_value, dtype="uint8"))
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+
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+ if overlap == (0, 0) or block_size == (width, height):
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+ # When there is no overlap or the whole image is used as input,
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+ # use 'keep_last' strategy as it introduces least overheads
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+ merge_strategy = 'keep_last'
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+
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+ if merge_strategy == 'keep_first':
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+ overlap_processor = KeepFirstProcessor(
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+ height,
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+ width,
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+ *block_size[::-1],
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+ *step[::-1],
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+ band,
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+ inval=invalid_value)
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+ elif merge_strategy == 'keep_last':
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+ overlap_processor = KeepLastProcessor(height, width, *block_size[::-1],
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+ *step[::-1])
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+ elif merge_strategy == 'accum':
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+ overlap_processor = AccumProcessor(height, width, *block_size[::-1],
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+ *step[::-1])
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+ else:
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+ raise ValueError("{} is not a supported stragegy for block merging.".
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+ format(merge_strategy))
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+
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+ xsize, ysize = block_size
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+ num_blocks = math.ceil(height / step[1]) * math.ceil(width / step[0])
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+ cnt = 0
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+ if show_progress:
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+ pb = tqdm(total=num_blocks)
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+ batch_data = []
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+ batch_offsets = []
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+ for yoff in range(0, height, step[1]):
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+ for xoff in range(0, width, step[0]):
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+ if xoff + xsize > width:
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+ xoff = width - xsize
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+ is_end_of_row = True
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+ else:
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+ is_end_of_row = False
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+ if yoff + ysize > height:
|
|
|
+ yoff = height - ysize
|
|
|
+ is_end_of_col = True
|
|
|
+ else:
|
|
|
+ is_end_of_col = False
|
|
|
+
|
|
|
+ # Read
|
|
|
+ im = read_block(src_data, xoff, yoff, xsize, ysize)
|
|
|
+
|
|
|
+ if isinstance(img_file, tuple):
|
|
|
+ im2 = read_block(src2_data, xoff, yoff, xsize, ysize)
|
|
|
+ batch_data.append((im, im2))
|
|
|
+ else:
|
|
|
+ batch_data.append(im)
|
|
|
+
|
|
|
+ batch_offsets.append((xoff, yoff))
|
|
|
+
|
|
|
+ len_batch = len(batch_data)
|
|
|
+
|
|
|
+ if is_end_of_row and is_end_of_col and len_batch < batch_size:
|
|
|
+ # Pad `batch_data` by repeating the last element
|
|
|
+ batch_data = batch_data + [batch_data[-1]] * (batch_size -
|
|
|
+ len_batch)
|
|
|
+ # While keeping `len(batch_offsets)` the number of valid elements in the batch
|
|
|
+
|
|
|
+ if len(batch_data) == batch_size:
|
|
|
+ # Predict
|
|
|
+ batch_out = predict_func(batch_data, transforms=transforms)
|
|
|
+
|
|
|
+ for out, (xoff_, yoff_) in zip(batch_out, batch_offsets):
|
|
|
+ # Get processed result
|
|
|
+ pred = overlap_processor.process_pred(out, xoff_, yoff_)
|
|
|
+ # Write to file
|
|
|
+ band.WriteArray(pred, xoff_, yoff_)
|
|
|
+
|
|
|
+ dst_data.FlushCache()
|
|
|
+ batch_data.clear()
|
|
|
+ batch_offsets.clear()
|
|
|
+
|
|
|
+ cnt += 1
|
|
|
+
|
|
|
+ if show_progress:
|
|
|
+ pb.update(1)
|
|
|
+ pb.set_description("{} out of {} blocks processed.".format(
|
|
|
+ cnt, num_blocks))
|
|
|
+
|
|
|
+ dst_data = None
|
|
|
+ logging.info("GeoTiff file saved in {}.".format(save_file))
|
Bobholamovic