def test_discover_ndv_list_three(arr_str2):
candidates = discover_ndv(arr_str2, debug=False, verbose=True)
mode_vals = mode(_convert_rgb(arr_str2)[1])
candidate_original = [int((mode_vals[0])[0, i]) for i in range(3)]
candidate_continuous, arr = _compute_continuous(
_convert_rgb(arr_str2)[0], 1)
candidate_list = \
[i for i, j in zip(candidate_original,
candidate_continuous)
if i == j]
assert candidates == [1, 1, 1]
def test_discover_ndv_list_less_three(arr_str2):
cons_arr = np.array(
[
[1, 1, 1],
[1, 1, 1],
[1, 2, 1],
[1, 2, 1],
[1, 2, 1],
[1, 2, 1],
[1, 1, 1],
[1, 2, 1],
]
)
cons_arr2 = np.array(
[
[1, 1, 1],
[1, 2, 1],
[1, 1, 1],
[1, 2, 1],
[1, 1, 1],
[1, 2, 1],
[1, 1, 1],
[1, 2, 1],
]
)
arr_str2[0] = cons_arr
arr_str2[1:] = cons_arr2
candidates = discover_ndv(arr_str2, debug=False, verbose=True)
candidate_continuous, arr = _compute_continuous(_convert_rgb(arr_str2)[0], 1)
assert candidates == "None"
def test_search_image_edge_ca2(test_fixtures):
data_ca, data_ca2 = test_fixtures
data_ca2_mod = _convert_rgb(data_ca2)[0]
candidate_original = [255, 255, 255]
candidate_continuous = [255, 255, 250]
rgb_mod = np.copy(data_ca2_mod)
top_row = rgb_mod[0, :, :]
bottom_row = rgb_mod[-1, :, :]
first_col = rgb_mod[:, 0, :]
last_col = rgb_mod[:, -1, :]
img_edge = np.concatenate((top_row, last_col, bottom_row, first_col), axis=0)
arr = _compute_continuous(rgb_mod, 0)[-1]
full = [
len(np.transpose(np.where((img_edge == candidate).all(axis=1))))
for candidate in (candidate_original, candidate_continuous)
]
cont = [
len(np.transpose(np.where((arr == candidate).all(axis=1))))
for candidate in (candidate_original, candidate_continuous)
]
assert img_edge.shape[0] == rgb_mod.shape[0] * 4
assert full == [242, 0]
assert cont == [7048, 0]
def test_discover_ndv_list_less_three2(arr_str2):
cons_arr = np.array([[1, 1, 1], [1, 1, 1], [1, 2, 1], [1, 2, 1], [1, 2, 1],
[1, 2, 1], [1, 1, 1], [1, 2, 1]])
cons_arr2 = np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1], [1, 2, 1],
[1, 1, 1], [1, 2, 1], [1, 1, 1], [1, 2, 1]])
arr_str2[0] = cons_arr
arr_str2[1:] = cons_arr2
candidates = discover_ndv(arr_str2, debug=False, verbose=False)
mode_vals = mode(_convert_rgb(arr_str2)[1])
candidate_original = [int((mode_vals[0])[0, i]) for i in range(3)]
candidate_continuous, arr = _compute_continuous(
_convert_rgb(arr_str2)[0], 1)
candidate_list = \
[i for i, j in zip(candidate_original,
candidate_continuous)
if i == j]
assert candidates == ''
def discover_ndv(rgb_orig, debug, verbose):
"""Returns nodata value by calculating mode of RGB array
Parameters
----------
rgb_orig: ndarray
array of input pixels of shape (rows, cols, depth)
debug: Boolean
Enables matplotlib & printing of figures
verbose: Boolean
Prints extra information, like competing candidate values
Returns
-------
list of nodata value candidates or empty string if none found
"""
rgb_mod, rgb_mod_flat = _convert_rgb(rgb_orig)
# Full image mode bincount
mode_vals = mode(rgb_mod_flat)
candidate_original = [int((mode_vals[0])[0, i]) for i in range(3)]
# Find continuous values in RGB array
candidate_continuous, arr = _compute_continuous(rgb_mod, 1)
# If debug mode, print histograms & be verbose
if debug:
click.echo("Original image ndv candidate: %s" %
(str(candidate_original)))
click.echo("Filtered image ndv candidate: %s" %
(str(candidate_continuous)))
outplot = "/tmp/hist_plot.png"
_debug_mode(rgb_mod_flat, arr, outplot)
# Compare ndv candidates from full & squished image
candidate_list = [
i for i, j in zip(candidate_original, candidate_continuous) if i == j
]
# If candidates from original & filtered images match exactly,
# print value & exit
if len(candidate_list) == 3:
return candidate_list
# If candidates do not match exactly, continue vetting process
# by searching image edge for frequency of each candidate
elif len(candidate_list) < 3:
if verbose:
click.echo("Competing ndv candidates...searching "
"image collar for value frequency. "
"Candidate list: %s" % str(candidate_list))
count_img_edge_full, count_img_edge_continuous = _search_image_edge(
rgb_mod, candidate_original, candidate_continuous)
if verbose:
for candidate in (candidate_original, candidate_continuous):
click.echo("Candidate value: %s "
"Candidate count: %s "
"Continuous count: %s" % (
str(candidate),
str(count_img_edge_full),
str(count_img_edge_continuous),
))
output = _evaluate_count(count_img_edge_full,
count_img_edge_continuous, verbose)
return output
else:
raise ValueError("Invalid candidate list {!r}".format(candidate_list))
def test_discover_ndv_list_three(arr_str2):
candidates = discover_ndv(arr_str2, debug=False, verbose=True)
candidate_continuous, arr = _compute_continuous(_convert_rgb(arr_str2)[0], 1)
assert candidates == [1, 1, 1]
def test_convert_rgb_dim_zero(rgb_orig):
rgb_mod_flat = _convert_rgb(rgb_orig)
assert np.array_equal(
rgb_mod_flat[1],
rgb_orig.reshape(rgb_orig.shape[0] * rgb_orig.shape[1], rgb_orig.shape[-1]),
)
def test_convert_rgba(test_fixtures):
a, _ = test_fixtures
amod = _convert_rgb(a)[0]
assert amod.shape == (167, 167, 3)