forked from TensorLayer/tensorlayer3
52 lines
1.5 KiB
Cython
52 lines
1.5 KiB
Cython
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import numpy as np
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cimport numpy as np
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DTYPE = np.float
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ctypedef np.float_t DTYPE_t
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def bbox_overlaps(
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np.ndarray[DTYPE_t, ndim=2] boxes,
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np.ndarray[DTYPE_t, ndim=2] query_boxes):
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"""
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Parameters
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----------
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boxes: (N, 4) ndarray of float
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query_boxes: (K, 4) ndarray of float
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Returns
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-------
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overlaps: (N, K) ndarray of overlap between boxes and query_boxes
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"""
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cdef unsigned int N = boxes.shape[0]
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cdef unsigned int K = query_boxes.shape[0]
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cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)
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cdef DTYPE_t iw, ih, box_area
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cdef DTYPE_t ua
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cdef unsigned int k, n
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for k in range(K):
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box_area = (
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(query_boxes[k, 2] - query_boxes[k, 0] + 1) *
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(query_boxes[k, 3] - query_boxes[k, 1] + 1)
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)
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for n in range(N):
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iw = (
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min(boxes[n, 2], query_boxes[k, 2]) -
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max(boxes[n, 0], query_boxes[k, 0]) + 1
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)
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if iw > 0:
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ih = (
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min(boxes[n, 3], query_boxes[k, 3]) -
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max(boxes[n, 1], query_boxes[k, 1]) + 1
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)
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if ih > 0:
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ua = float(
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(boxes[n, 2] - boxes[n, 0] + 1) *
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(boxes[n, 3] - boxes[n, 1] + 1) +
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box_area - iw * ih
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)
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overlaps[n, k] = iw * ih / ua
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return overlaps
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