Tibeuleu/FOC_Reduction
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reformat code using python-lsp-ruff

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Thibault Barnouin
2024-07-01 15:21:52 +02:00
parent 271ecbb631
commit 5a62fa4983
13 changed files with 1271 additions and 860 deletions
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59
package/lib/convex_hull.py
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@@ -3,6 +3,7 @@ Library functions for graham algorithm implementation (find the convex hull of a
"""
from copy import deepcopy
import numpy as np
@@ -16,23 +17,23 @@ def clean_ROI(image):
row, col = np.indices(shape)
for i in range(0, shape[0]):
r = row[i, :][image[i, :] > 0.]
c = col[i, :][image[i, :] > 0.]
r = row[i, :][image[i, :] > 0.0]
c = col[i, :][image[i, :] > 0.0]
if len(r) > 1 and len(c) > 1:
H.append((r[0], c[0]))
H.append((r[-1], c[-1]))
H = np.array(H)
for j in range(0, shape[1]):
r = row[:, j][image[:, j] > 0.]
c = col[:, j][image[:, j] > 0.]
r = row[:, j][image[:, j] > 0.0]
c = col[:, j][image[:, j] > 0.0]
if len(r) > 1 and len(c) > 1:
J.append((r[0], c[0]))
J.append((r[-1], c[-1]))
J = np.array(J)
xmin = np.min([H[:, 1].min(), J[:, 1].min()])
xmax = np.max([H[:, 1].max(), J[:, 1].max()])+1
xmax = np.max([H[:, 1].max(), J[:, 1].max()]) + 1
ymin = np.min([H[:, 0].min(), J[:, 0].min()])
ymax = np.max([H[:, 0].max(), J[:, 0].max()])+1
ymax = np.max([H[:, 0].max(), J[:, 0].max()]) + 1
return np.array([xmin, xmax, ymin, ymax])
@@ -81,7 +82,7 @@ def distance(A, B):
Euclidian distance between A, B.
"""
x, y = vector(A, B)
return np.sqrt(x ** 2 + y ** 2)
return np.sqrt(x**2 + y**2)
# Define lexicographic and composition order
@@ -174,8 +175,8 @@ def partition(s, left, right, order):
temp = deepcopy(s[i])
s[i] = deepcopy(s[j])
s[j] = deepcopy(temp)
temp = deepcopy(s[i+1])
s[i+1] = deepcopy(s[right])
temp = deepcopy(s[i + 1])
s[i + 1] = deepcopy(s[right])
s[right] = deepcopy(temp)
return i + 1
@@ -206,16 +207,32 @@ def sort_angles_distances(Omega, s):
Sort the list of points 's' for the composition order given reference point
Omega.
"""
def order(A, B): return comp(Omega, A, B)
def order(A, B):
return comp(Omega, A, B)
quicksort(s, order)
# Define fuction for stacks (use here python lists with stack operations).
def empty_stack(): return []
def stack(S, A): S.append(A)
def unstack(S): S.pop()
def stack_top(S): return S[-1]
def stack_sub_top(S): return S[-2]
def empty_stack():
return []
def stack(S, A):
S.append(A)
def unstack(S):
S.pop()
def stack_top(S):
return S[-1]
def stack_sub_top(S):
return S[-2]
# Alignement handling
@@ -299,7 +316,7 @@ def convex_hull(H):
return S
def image_hull(image, step=5, null_val=0., inside=True):
def image_hull(image, step=5, null_val=0.0, inside=True):
"""
Compute the convex hull of a 2D image and return the 4 relevant coordinates
of the maximum included rectangle (ie. crop image to maximum rectangle).
@@ -331,7 +348,7 @@ def image_hull(image, step=5, null_val=0., inside=True):
H = []
shape = np.array(image.shape)
row, col = np.indices(shape)
for i in range(0, int(min(shape)/2), step):
for i in range(0, int(min(shape) / 2), step):
r1, r2 = row[i, :][image[i, :] > null_val], row[-i, :][image[-i, :] > null_val]
c1, c2 = col[i, :][image[i, :] > null_val], col[-i, :][image[-i, :] > null_val]
if r1.shape[0] > 1:
@@ -349,10 +366,10 @@ def image_hull(image, step=5, null_val=0., inside=True):
# S1 = S[x_min*y_max][np.argmax(S[x_min*y_max][:, 1])]
# S2 = S[x_max*y_min][np.argmin(S[x_max*y_min][:, 1])]
# S3 = S[x_max*y_max][np.argmax(S[x_max*y_max][:, 0])]
S0 = S[x_min*y_min][np.abs(0-S[x_min*y_min].sum(axis=1)).min() == np.abs(0-S[x_min*y_min].sum(axis=1))][0]
S1 = S[x_min*y_max][np.abs(shape[1]-S[x_min*y_max].sum(axis=1)).min() == np.abs(shape[1]-S[x_min*y_max].sum(axis=1))][0]
S2 = S[x_max*y_min][np.abs(shape[0]-S[x_max*y_min].sum(axis=1)).min() == np.abs(shape[0]-S[x_max*y_min].sum(axis=1))][0]
S3 = S[x_max*y_max][np.abs(shape.sum()-S[x_max*y_max].sum(axis=1)).min() == np.abs(shape.sum()-S[x_max*y_max].sum(axis=1))][0]
S0 = S[x_min * y_min][np.abs(0 - S[x_min * y_min].sum(axis=1)).min() == np.abs(0 - S[x_min * y_min].sum(axis=1))][0]
S1 = S[x_min * y_max][np.abs(shape[1] - S[x_min * y_max].sum(axis=1)).min() == np.abs(shape[1] - S[x_min * y_max].sum(axis=1))][0]
S2 = S[x_max * y_min][np.abs(shape[0] - S[x_max * y_min].sum(axis=1)).min() == np.abs(shape[0] - S[x_max * y_min].sum(axis=1))][0]
S3 = S[x_max * y_max][np.abs(shape.sum() - S[x_max * y_max].sum(axis=1)).min() == np.abs(shape.sum() - S[x_max * y_max].sum(axis=1))][0]
# Get the vertex of the biggest included rectangle
if inside:
f0 = np.max([S0[0], S1[0]])