From a240bf83e85ab4b035451037812f9ffb61c7889b Mon Sep 17 00:00:00 2001
From: linarphy <linarphy@linarphy.net>
Date: Tue, 9 May 2023 12:43:34 +0200
Subject: [PATCH] Update label detection method
- Remove old label detection code and dependancy to scipy
- Add quick label detection method without dependancies
---
ETA.py | 131 +++++++++++++++++++++++++++------------------------------
1 file changed, 62 insertions(+), 69 deletions(-)
diff --git a/ETA.py b/ETA.py
index c26e5df..0d03b84 100644
--- a/ETA.py
+++ b/ETA.py
@@ -1,9 +1,10 @@
import numpy as np
-from scipy.optimize import curve_fit
import utils
import sys
-data = utils.load( sys.argv[1] )
+if len( sys.argv ) < 2:
+ raise Exception( 'this command must have a filename of an ETA fits as an argument' )
+data = utils.load( sys.argv[1] )
"""
find fill points
@@ -55,8 +56,25 @@ fill data
extremum = []
for point in points:
+ if point[0] < points[2][0]:
+ point[0] = points[2][0]
+ if point[1] < points[0][1]:
+ point[1] = points[0][1]
taken_points = utils.small_to_big(
- utils.fill( small_data , point , 1000 ),
+ np.array( [
+ points[2][0],
+ points[0][1],
+ ] ) + utils.fill(
+ small_data[
+ points[2][0] : points[3][0] + 1,
+ points[0][1] : points[1][1] + 1
+ ],
+ [
+ point[0] - points[2][0],
+ point[1] - points[0][1],
+ ],
+ 1000
+ ),
5
)
extremum.append( [
@@ -68,12 +86,12 @@ for point in points:
border = {
'x': {
- 'min': extremum[0][1] + 1,
- 'max': extremum[1][0] ,
+ 'min': points[0][1] + extremum[0][1] + 1,
+ 'max': points[0][1] + extremum[1][0] ,
},
'y': {
- 'min': extremum[2][3] + 1,
- 'max': extremum[3][2] ,
+ 'min': points[2][0] + extremum[2][3] + 1,
+ 'max': points[2][0] + extremum[3][2] ,
},
}
@@ -81,72 +99,47 @@ border = {
label deletion
"""
-mean_data = np.mean( data[
- border['y']['min'] : border['y']['max'],
- border['x']['min'] : border['x']['max']
-] , axis = 0 )
-
-gauss = lambda x , sigma , mu , a , b : a * (
- 1 / sigma * np.sqrt(
- 2 * np.pi
- ) * np.exp(
- - ( x - mu ) ** 2 / ( 2 * sigma ** 2 )
- )
-) + b
-abciss = np.arange(
- border['x']['min'],
- border['x']['max'],
- 1
-)
-guess_params = [
- 1 ,
- border['x']['min'] + ( border['x']['max'] - border['x']['min'] ) // 2,
- np.max( mean_data ) ,
- np.min( mean_data ) ,
-]
-
-first_estimate = curve_fit(
- gauss ,
- abciss ,
- mean_data ,
- guess_params
-)[0]
-
-part_data = [
- mean_data[ : mean_data.shape[0] // 2 ],
- mean_data[ mean_data.shape[0] // 2 : ]
-]
-part_abciss = [
- abciss[ : abciss.shape[0] // 2 ],
- abciss[ abciss.shape[0] // 2 : ]
-]
-part_result = []
-for i in range( 2 ):
- part_result.append(
- curve_fit(
- gauss ,
- part_abciss[i],
- part_data[i] ,
- first_estimate
+mean_data = np.convolve(
+ np.gradient(
+ np.mean(
+ data[
+ border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
+ border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
+ ],
+ axis = 0
)
- )
-
-cov = np.array( [
- np.sum( np.diag( part_result[i][1] ) ) for i in range( 2 )
-] )
-i = np.argmax( cov ) # part where the label is
-
-derivee = np.convolve(
- np.gradient( part_data[i] ),
- np.ones( part_data[i].shape[0] // 100 ),
- 'same',
+ ),
+ np.ones(
+ int( 0.01 * (
+ border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
+ ) )
+ ),
+ 'same'
)
-start_label = np.argmax( derivee )
-end_label = np.argmin( derivee[ start_label :: ( - 1 ) ** i ] )
-keys = [ 'min' , 'max' ]
+mean_data -= np.min( mean_data )
+mean_data /= np.max( mean_data )
-border['x'][keys[i]] += ( - 1 ) ** i * ( start_label + end_label )
+top = utils.consecutive( np.where( mean_data > 0.75 )[0] )
+down = utils.consecutive( np.where( mean_data < 0.25 )[0] )
+
+size_top = [ len( list_ ) for list_ in top ]
+size_down = [ len( list_ ) for list_ in down ]
+
+label_x = {
+ 'min': border[ 'x' ][ 'min' ] + top[ np.argmax( size_top ) ][0] ,
+ 'max': border[ 'x' ][ 'min' ] + down[ np.argmax( size_down ) ][-1]
+}
+
+if label_x[ 'min' ] < data.shape[1] // 2:
+ if label_x[ 'max' ] < data.shape[1] // 2:
+ border[ 'x' ][ 'min' ] = label_x[ 'max' ]
+ else:
+ raise Exception( 'the label seems to be in the middle of the picture' )
+elif label_x[ 'max' ] > data.shape[1] // 2:
+ border[ 'x' ][ 'max' ] = label_x[ 'min' ]
+else:
+ raise Exception( 'for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )
import matplotlib.pyplot as plt
plt.imshow( data[