From 6c13cd70ac044da9070af0b75b076126f3dab063 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <amuoz@ed.ac.uk>
Date: Fri, 25 Mar 2022 18:48:31 +0000
Subject: [PATCH] revamp template identification algorithm
---
aliby/tile/traps.py | 121 ++++++++++++++------------------------------
1 file changed, 38 insertions(+), 83 deletions(-)
diff --git a/aliby/tile/traps.py b/aliby/tile/traps.py
index 4bdb1f43..b152261e 100644
--- a/aliby/tile/traps.py
+++ b/aliby/tile/traps.py
@@ -49,8 +49,14 @@ def segment_traps(
# TODO Optimise the hyperparameters
disk_radius = int(min([disk_radius_frac * x for x in img.shape]))
- min_area = min_frac_tilesize * (tile_size ** 2)
- max_area = max_frac_tilesize * (tile_size ** 2)
+ min_mal = min_frac_tilesize * np.sqrt(2) * tile_size
+ max_mal = max_frac_tilesize * np.sqrt(2) * tile_size
+
+ def half_floor(x):
+ return x - tile_size // 2
+
+ def half_ceil(x):
+ return x + -(tile_size // -2)
if downscale != 1:
img = transform.rescale(image, downscale)
@@ -69,64 +75,44 @@ def segment_traps(
# label image regions
label_image = label(cleared)
- # areas = [
- # region.area
- # for region in regionprops(label_image)
- # if region.area > min_area and region.area < max_area
- # ]
- traps = (
- np.array(
- [
- region.centroid
- for region in regionprops(label_image)
- if region.area > min_area and region.area < max_area
- ]
- )
- .round()
- .astype(int)
- )
- ma = (
- np.array(
- [
- region.minor_axis_length
- for region in regionprops(label_image)
- if region.area > min_area and region.area < max_area
- ]
- )
- .round()
- .astype(int)
- )
+ idx_valid_region = [
+ (i, region)
+ for i, region in enumerate(regionprops(label_image))
+ if min_mal < region.major_axis_length < max_mal
+ and tile_size // 2 < region.centroid[0] < half_floor(image.shape[0])
+ and tile_size // 2 < region.centroid[1] < half_floor(image.shape[1])
+ ]
+ idx, valid_region = zip(*idx_valid_region)
- maskx = (tile_size // 2 < traps[:, 0]) & (
- traps[:, 0] < image.shape[0] - tile_size // 2
- )
- masky = (tile_size // 2 < traps[:, 1]) & (
- traps[:, 1] < image.shape[1] - tile_size // 2
- )
+ valid_templates = copy(label_image)
+ for i in set(list(range(label_image.max()))).difference(idx):
+ valid_templates[np.where(valid_templates == i + 1)] = -2 * i
- traps = traps[maskx & masky, :]
- ma = ma[maskx & masky]
+ import matplotlib.colors as colors
- chosen_trap_coords = np.round(traps[ma.argmin()]).astype(int)
+ combined = valid_templates + label_image
+
+ centroids = np.array([x.centroid for x in valid_region]).round().astype(int)
+ minals = [region.minor_axis_length for region in valid_region]
+
+ chosen_trap_coords = np.round(centroids[np.argmin(minals)]).astype(int)
x, y = chosen_trap_coords
+
template = image[
- x - tile_size // 2 : x + tile_size // 2, y - tile_size // 2 : y + tile_size // 2
+ half_floor(x) : half_ceil(x),
+ half_floor(y) : half_ceil(y),
+ ]
+
+ candidate_templates = [
+ image[
+ slice(half_floor(x), half_ceil(x)),
+ slice(half_floor(y), half_ceil(y)),
+ ]
+ for x, y in centroids
]
# add template as mean of found traps
- mean_template = (
- np.dstack(
- [
- image[
- x - tile_size // 2 : x + tile_size // 2,
- y - tile_size // 2 : y + tile_size // 2,
- ]
- for x, y in traps
- ]
- )
- .astype(int)
- .mean(axis=-1)
- )
+ mean_template = np.dstack(candidate_templates).astype(int).mean(axis=-1)
traps = identify_trap_locations(image, template, **identify_traps_kwargs)
mean_traps = identify_trap_locations(image, mean_template, **identify_traps_kwargs)
@@ -141,37 +127,6 @@ def segment_traps(
return traps if len(traps_retry) < len(traps) else traps_retry
-# def segment_traps(image, tile_size, downscale=0.4):
-# # Make image go between 0 and 255
-# img = image # Keep a memory of image in case need to re-run
-# image = stretch_image(image)
-# # TODO Optimise the hyperparameters
-# disk_radius = int(min([0.01 * x for x in img.shape]))
-# min_area = 0.1 * (tile_size ** 2)
-# if downscale != 1:
-# img = transform.rescale(image, downscale)
-# entropy_image = entropy(img, disk(disk_radius))
-# if downscale != 1:
-# entropy_image = transform.rescale(entropy_image, 1 / downscale)
-
-# # apply threshold
-# thresh = threshold_otsu(entropy_image)
-# bw = closing(entropy_image > thresh, square(3))
-
-# # remove artifacts connected to image border
-# cleared = clear_border(bw)
-
-# # label image regions
-# label_image = label(cleared)
-# traps = [
-# region.centroid for region in regionprops(label_image) if region.area > min_area
-# ]
-# if len(traps) < 10 and downscale != 1:
-# print("Trying again.")
-# return segment_traps(image, tile_size, downscale=1)
-# return traps
-
-
def identify_trap_locations(
image, trap_template, optimize_scale=True, downscale=0.35, trap_size=None
):
--
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