mirror of
https://github.com/wassname/scikit-image.git
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pep8
This commit is contained in:
Thouis (Ray) Jones
+53
-55
@@ -62,11 +62,11 @@ cdef struct HistogramPiece:
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np.uint16_t fine[256]
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cdef struct Histogram:
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HistogramPiece top_left # top-left corner
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HistogramPiece top_right # top-right corner
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HistogramPiece edge # leading/trailing edge
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HistogramPiece bottom_left # bottom-left corner
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HistogramPiece bottom_right # bottom-right corner
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HistogramPiece top_left # top-left corner
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HistogramPiece top_right # top-right corner
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HistogramPiece edge # leading/trailing edge
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HistogramPiece bottom_left # bottom-left corner
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HistogramPiece bottom_right # bottom-right corner
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# The pixel count has the number of pixels histogrammed in
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# each of the five compartments for this position. This changes
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@@ -127,16 +127,16 @@ cdef struct Histograms:
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#
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# x -->
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#
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SCoord last_top_left # (-) left side of octagon's top - 1 row
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SCoord top_left # (+) -1 row from trailing edge top
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SCoord last_top_right # (-) right side of octagon's top - 1 col - 1 row
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SCoord top_right # (+) -1 col -1 row from leading edge top
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SCoord last_leading_edge # (-) leading edge (right) top stride - 1 row
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SCoord leading_edge # (+) leading edge bottom stride
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SCoord last_bottom_right # (-) leading edge bottom - 1 col
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SCoord bottom_right # (+) right side of octagon's bottom - 1 col
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SCoord last_bottom_left # (-) trailing edge bottom - 1 col
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SCoord bottom_left # (+) left side of octagon's bottom - 1 col
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SCoord last_top_left # (-) left side of octagon's top - 1 row
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SCoord top_left # (+) -1 row from trailing edge top
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SCoord last_top_right # (-) right side of octagon's top - 1 col - 1 row
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SCoord top_right # (+) -1 col -1 row from leading edge top
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SCoord last_leading_edge # (-) leading edge (right) top stride - 1 row
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SCoord leading_edge # (+) leading edge bottom stride
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SCoord last_bottom_right # (-) leading edge bottom - 1 col
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SCoord bottom_right # (+) right side of octagon's bottom - 1 col
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SCoord last_bottom_left # (-) trailing edge bottom - 1 col
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SCoord bottom_left # (+) left side of octagon's bottom - 1 col
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np.int32_t row_stride # stride between one row and the next
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np.int32_t col_stride # stride between one column and the next
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@@ -185,25 +185,25 @@ cdef Histograms *allocate_histograms(np.int32_t rows,
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SCoord *psc
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memory_size = (adjusted_stripe_length *
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(sizeof(Histogram) + sizeof(PixelCount))+
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sizeof(Histograms)+32)
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(sizeof(Histogram) + sizeof(PixelCount)) +
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sizeof(Histograms) + 32)
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ptr = malloc(memory_size)
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memset(ptr, 0, memory_size)
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ph = <Histograms *>ptr
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ph = <Histograms *> ptr
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if not ptr:
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return ph
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ph.memory = ptr
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ptr = <void *>(ph+1)
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ph.pixel_count = <PixelCount *>ptr
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ptr = <void *>(ph.pixel_count + adjusted_stripe_length)
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ptr = <void *> (ph + 1)
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ph.pixel_count = <PixelCount *> ptr
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ptr = <void *> (ph.pixel_count + adjusted_stripe_length)
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#
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# Align histogram memory to a 32-byte boundary
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#
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roundoff = <size_t>ptr
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roundoff = <size_t> ptr
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roundoff += 31
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roundoff -= roundoff % 32
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ptr = <void *>roundoff
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ph.histogram = <Histogram *>ptr
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ptr = <void *> roundoff
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ph.histogram = <Histogram *> ptr
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#
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# Fill in the statistical things we keep around
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#
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@@ -232,7 +232,7 @@ cdef Histograms *allocate_histograms(np.int32_t rows,
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# a_2 is the offset from the center to each of the octagon
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# corners
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#
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a = <int>(<np.float64_t>radius * 2.0 / 2.414213)
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a = <int> (<np.float64_t> radius * 2.0 / 2.414213)
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a_2 = a / 2
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if a_2 == 0:
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a_2 = 1
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@@ -327,17 +327,18 @@ cdef void set_stride(Histograms *ph, SCoord *psc):
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#
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############################################################################
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cdef inline np.int32_t tl_br_colidx(Histograms *ph, np.int32_t colidx):
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return <np.int32_t> (colidx + 3*ph.radius + ph.current_row) % ph.stripe_length
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return <np.int32_t> (colidx + 3 * ph.radius + ph.current_row) % \
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ph.stripe_length
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cdef inline np.int32_t tr_bl_colidx(Histograms *ph, np.int32_t colidx):
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return <np.int32_t> (colidx + 3*ph.radius + ph.row_count-ph.current_row) % \
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ph.stripe_length
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return <np.int32_t> (colidx + 3 * ph.radius + ph.row_count - ph.current_row) % \
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ph.stripe_length
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cdef inline np.int32_t leading_edge_colidx(Histograms *ph, np.int32_t colidx):
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return <np.int32_t> (colidx + 5*ph.radius) % ph.stripe_length
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return <np.int32_t> (colidx + 5 * ph.radius) % ph.stripe_length
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cdef inline np.int32_t trailing_edge_colidx(Histograms *ph, np.int32_t colidx):
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return <np.int32_t> (colidx + 3*ph.radius - 1) % ph.stripe_length
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return <np.int32_t> (colidx + 3 * ph.radius - 1) % ph.stripe_length
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############################################################################
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#
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@@ -528,9 +529,9 @@ cdef inline void update_histogram(Histograms *ph,
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y = last_coord.y + current_row
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stride = current_stride+last_coord.stride
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if (x >= 0 and x < column_count and
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y >= 0 and y < row_count and
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ph.mask[stride]):
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if (x >= 0 and x < column_count and \
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y >= 0 and y < row_count and \
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ph.mask[stride]):
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value = ph.data[stride]
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pixel_count[0] -= 1
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hist_piece.fine[value] -= 1
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@@ -540,9 +541,9 @@ cdef inline void update_histogram(Histograms *ph,
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y = coord.y + current_row
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stride = current_stride + coord.stride
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if (x >= 0 and x < column_count and
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y >= 0 and y < row_count and
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ph.mask[stride]):
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if (x >= 0 and x < column_count and \
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y >= 0 and y < row_count and \
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ph.mask[stride]):
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value = ph.data[stride]
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pixel_count[0] += 1
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hist_piece.fine[value] += 1
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@@ -613,7 +614,7 @@ cdef inline np.uint8_t find_median(Histograms *ph):
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if ph.accumulator_count == 0:
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return 0
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pixels_below = <np.uint32_t> ((ph.accumulator_count * ph.percent + 50)
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/ 100) # +50 for roundoff
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/ 100) # +50 for roundoff
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if pixels_below > 0:
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pixels_below -= 1
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@@ -625,7 +626,7 @@ cdef inline np.uint8_t find_median(Histograms *ph):
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accumulator -= ph.accumulator.coarse[i]
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update_fine(ph, i)
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for j in range(i*16,(i+1)*16):
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for j in range(i * 16, (i + 1) * 16):
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accumulator += ph.accumulator.fine[j]
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if accumulator > pixels_below:
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return <np.uint8_t> j
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@@ -659,9 +660,7 @@ cdef int c_median_filter(np.int32_t rows,
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cdef:
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Histograms *ph
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Histogram *phistogram
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int row
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int col
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int i
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int row, col, i
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np.int32_t top_left_off
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np.int32_t top_right_off
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np.int32_t bottom_left_off
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@@ -670,7 +669,7 @@ cdef int c_median_filter(np.int32_t rows,
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ph = allocate_histograms(rows, columns, row_stride, col_stride,
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radius, percent, data, mask, output)
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if not ph:
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return 1
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return 1
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for row in range(-radius, rows):
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#
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@@ -709,7 +708,7 @@ cdef int c_median_filter(np.int32_t rows,
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# Update locations and coarse accumulator for the octagon
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# for points before 0
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#
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for col in range(-radius, 0 if row >=0 else columns+radius):
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for col in range(-radius, 0 if row >= 0 else columns+radius):
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ph.current_column = col
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ph.current_stride = row * row_stride + col * col_stride
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update_current_location(ph)
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@@ -730,16 +729,15 @@ cdef int c_median_filter(np.int32_t rows,
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ph.current_stride = row * row_stride + col * col_stride
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update_current_location(ph)
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free_histograms(ph)
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return 0
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def median_filter(
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np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] data,
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np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] mask,
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np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] output,
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int radius,
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np.int32_t percent):
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def median_filter(np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] data,
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np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] mask,
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np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] output,
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int radius,
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np.int32_t percent):
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"""Median filter with octagon shape and masking.
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Parameters
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@@ -761,10 +759,10 @@ def median_filter(
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"""
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if percent < 0:
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raise ValueError('Median filter percent = %d is less than zero' % \
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raise ValueError('Median filter percent = %d is less than zero' %
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percent)
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if percent > 100:
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raise ValueError('Median filter percent = %d is greater than 100' % \
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raise ValueError('Median filter percent = %d is greater than 100' %
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percent)
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if data.shape[0] != mask.shape[0] or data.shape[1] != mask.shape[1]:
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raise ValueError('Data shape (%d, %d) is not mask shape (%d, %d)' %
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@@ -777,7 +775,7 @@ def median_filter(
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if c_median_filter(<np.int32_t> data.shape[0], <np.int32_t> data.shape[1],
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<np.int32_t> data.strides[0], <np.int32_t> data.strides[1],
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radius, percent,
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<np.uint8_t *>data.data,
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<np.uint8_t *>mask.data,
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<np.uint8_t *>output.data):
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<np.uint8_t *> data.data,
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<np.uint8_t *> mask.data,
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<np.uint8_t *> output.data):
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raise MemoryError('Failed to allocate scratchpad memory')
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