Add intensity step correction
- Finished curve correction - Add intensity correction for each x
This commit is contained in:
parent
2840f86646
commit
15325e0e8f
1 changed files with 281 additions and 233 deletions
514
ETA.py
514
ETA.py
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@ -1,6 +1,9 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import utils
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import sys
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import pathlib
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import shelve
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from scipy.signal import convolve as sp_convolve
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from scipy.signal import find_peaks
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from scipy.ndimage import rotate
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@ -9,156 +12,154 @@ if len( sys.argv ) < 2:
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raise Exception( 'this command must have a filename of an ETA fits as an argument' )
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data = utils.load( sys.argv[1] )
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"""
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find fill points
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"""
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points = []
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cache_file = pathlib.Path( 'asset/points_' + sys.argv[1].split( '/' )[-1][:-5] + '.pag' )
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points += utils.find_point( data[ : , 0 ] , 0 ) # x_min
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points += utils.find_point(
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data[ : , data.shape[1] - 1 ],
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data.shape[1] - 1 ,
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) # x_max
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index_min = 0
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while data.shape[0] - 1 > index_min:
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index_min += 1
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if len( utils.find_point( data[ index_min , : ] , index_min , 'y' ) ) == 3:
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break
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points.append(
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utils.find_point( data[ index_min , : ] , index_min , 'y' )[1]
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) # y_min
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index_max = data.shape[0] - 1
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while index_min < index_max:
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index_max -= 1
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if len( utils.find_point( data[ index_max , : ] , index_max , 'y' ) ) == 3:
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break
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points.append(
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utils.find_point( data[ index_max , : ] , index_max , 'y' )[1]
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) # y_max
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small_data = utils.compress( data , 5 )
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points = utils.big_to_small( points , 5 )
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# size - 1
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points[ 1 ][ 1 ] -= 1
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points[ 3 ][ 0 ] -= 1
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# little shift to be inside the light
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points[ 2 ][ 1 ] += 3
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points[ 3 ][ 1 ] += 3
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"""
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fill data
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"""
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extremum = []
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for point in points:
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if point[0] < points[2][0]:
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point[0] = points[2][0]
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if point[1] < points[0][1]:
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point[1] = points[0][1]
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taken_points = utils.small_to_big(
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np.array( [
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points[2][0],
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points[0][1],
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] ) + utils.fill(
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small_data[
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points[2][0] : points[3][0] + 1,
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points[0][1] : points[1][1] + 1
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],
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[
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point[0] - points[2][0],
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point[1] - points[0][1],
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],
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1000
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),
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5
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)
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extremum.append( [
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np.min( taken_points[ : , 1 ] ),
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np.max( taken_points[ : , 1 ] ),
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np.min( taken_points[ : , 0 ] ),
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np.max( taken_points[ : , 0 ] ),
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] )
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border = {
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'x': {
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'min': points[0][1] + extremum[0][1] + 1,
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'max': points[0][1] + extremum[1][0] ,
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},
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'y': {
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'min': points[2][0] + extremum[2][3] + 1,
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'max': points[2][0] + extremum[3][2] ,
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},
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}
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"""
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label deletion
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"""
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mean_data = np.convolve(
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np.gradient(
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np.mean(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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],
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axis = 0
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)
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),
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np.ones(
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int( 0.01 * (
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border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
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) )
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),
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'same'
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)
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mean_data -= np.min( mean_data )
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mean_data /= np.max( mean_data )
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top = utils.consecutive( np.where( mean_data > 0.75 )[0] )
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down = utils.consecutive( np.where( mean_data < 0.25 )[0] )
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size_top = [ len( list_ ) for list_ in top ]
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size_down = [ len( list_ ) for list_ in down ]
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label_x = {
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'min': border[ 'x' ][ 'min' ] + top[ np.argmax( size_top ) ][0] ,
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'max': border[ 'x' ][ 'min' ] + down[ np.argmax( size_down ) ][-1]
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}
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if label_x[ 'min' ] < data.shape[1] // 2:
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if label_x[ 'max' ] < data.shape[1] // 2:
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border[ 'x' ][ 'min' ] = label_x[ 'max' ]
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else:
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raise Exception( 'the label seems to be in the middle of the picture' )
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elif label_x[ 'max' ] > data.shape[1] // 2:
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border[ 'x' ][ 'max' ] = label_x[ 'min' ]
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if cache_file.is_file():
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with shelve.open( str( cache_file ) ) as cache:
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data = cache[ 'rotated_data' ]
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border = cache[ 'border' ]
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calibrations = cache[ 'calibrations' ]
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else:
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raise Exception( 'for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )
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"""
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find fill points
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"""
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points = []
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"""
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Rotation
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"""
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points += utils.find_point( data[ : , 0 ] , 0 ) # x_min
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points += utils.find_point(
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data[ : , data.shape[1] - 1 ],
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data.shape[1] - 1 ,
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) # x_max
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index = border[ 'x' ][ 'min' ]
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gradient = np.gradient(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) // 2,
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index
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]
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)
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while np.max( gradient ) - np.min( gradient ) > 5500:
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index += 1
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index_min = 0
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while data.shape[0] - 1 > index_min:
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index_min += 1
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if len( utils.find_point( data[ index_min , : ] , index_min , 'y' ) ) == 3:
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break
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points.append(
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utils.find_point( data[ index_min , : ] , index_min , 'y' )[1]
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) # y_min
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index_max = data.shape[0] - 1
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while index_min < index_max:
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index_max -= 1
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if len( utils.find_point( data[ index_max , : ] , index_max , 'y' ) ) == 3:
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break
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points.append(
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utils.find_point( data[ index_max , : ] , index_max , 'y' )[1]
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) # y_max
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small_data = utils.compress( data , 5 )
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points = utils.big_to_small( points , 5 )
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# size - 1
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points[ 1 ][ 1 ] -= 1
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points[ 3 ][ 0 ] -= 1
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# little shift to be inside the light
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points[ 2 ][ 1 ] += 3
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points[ 3 ][ 1 ] += 3
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"""
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fill data
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"""
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extremum = []
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for point in points:
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if point[0] < points[2][0]:
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point[0] = points[2][0]
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if point[1] < points[0][1]:
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point[1] = points[0][1]
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taken_points = utils.small_to_big(
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np.array( [
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points[2][0],
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points[0][1],
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] ) + utils.fill(
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small_data[
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points[2][0] : points[3][0] + 1,
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points[0][1] : points[1][1] + 1
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],
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[
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point[0] - points[2][0],
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point[1] - points[0][1],
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],
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1000
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),
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5
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)
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extremum.append( [
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np.min( taken_points[ : , 1 ] ),
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np.max( taken_points[ : , 1 ] ),
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np.min( taken_points[ : , 0 ] ),
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np.max( taken_points[ : , 0 ] ),
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] )
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border = {
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'x': {
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'min': points[0][1] + extremum[0][1] + 1,
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'max': points[0][1] + extremum[1][0] ,
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},
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'y': {
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'min': points[2][0] + extremum[2][3] + 1,
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'max': points[2][0] + extremum[3][2] ,
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},
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}
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"""
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label deletion
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"""
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mean_data = np.convolve(
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np.gradient(
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np.mean(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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],
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axis = 0
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)
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),
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np.ones(
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int( 0.01 * (
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border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
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) )
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),
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'same'
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)
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mean_data -= np.min( mean_data )
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mean_data /= np.max( mean_data )
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top = utils.consecutive( np.where( mean_data > 0.75 )[0] )
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down = utils.consecutive( np.where( mean_data < 0.25 )[0] )
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size_top = [ len( list_ ) for list_ in top ]
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size_down = [ len( list_ ) for list_ in down ]
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label_x = {
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'min': border[ 'x' ][ 'min' ] + top[ np.argmax( size_top ) ][0] ,
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'max': border[ 'x' ][ 'min' ] + down[ np.argmax( size_down ) ][-1]
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}
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if label_x[ 'min' ] < data.shape[1] // 2:
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if label_x[ 'max' ] < data.shape[1] // 2:
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border[ 'x' ][ 'min' ] = label_x[ 'max' ]
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else:
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raise Exception( 'the label seems to be in the middle of the picture' )
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elif label_x[ 'max' ] > data.shape[1] // 2:
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border[ 'x' ][ 'max' ] = label_x[ 'min' ]
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else:
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raise Exception( 'for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )
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"""
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Rotation
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"""
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index = border[ 'x' ][ 'min' ]
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gradient = np.gradient(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
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@ -167,122 +168,169 @@ while np.max( gradient ) - np.min( gradient ) > 5500:
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index
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]
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)
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positions = np.argmax(
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sp_convolve(
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np.gradient(
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while np.max( gradient ) - np.min( gradient ) > 5500:
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index += 1
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gradient = np.gradient(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) // 2 ,
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border[ 'x' ][ 'min' ] : index
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] ,
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axis = 0
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) ,
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np.ones( ( 100 , 1 ) ),
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'valid'
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) ,
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axis = 0
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)
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) // 2,
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index
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]
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)
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list_ = np.arange( 0 , index - border[ 'x' ][ 'min' ] , 1 )
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polyval = np.polyfit( list_ , positions , 1 )
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positions = np.argmax(
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sp_convolve(
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np.gradient(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) // 2 ,
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border[ 'x' ][ 'min' ] : index
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] ,
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axis = 0
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) ,
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np.ones( ( 100 , 1 ) ),
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'valid'
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) ,
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axis = 0
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)
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angle = np.arctan( polyval[0] )
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list_ = np.arange( 0 , index - border[ 'x' ][ 'min' ] , 1 )
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polyval = np.polyfit( list_ , positions , 1 )
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data = rotate( data , angle * ( 180 / np.pi ) ) # utils.rotate does not keep intensity absolute value ? TODO
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angle = np.arctan( polyval[0] )
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diff_y = int( np.tan( angle ) * ( border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] ) )
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data = rotate( data , angle * ( 180 / np.pi ) ) # utils.rotate does not keep intensity absolute value ? TODO
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border[ 'y' ][ 'min' ] -= diff_y
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border[ 'y' ][ 'max' ] -= diff_y
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diff_y = int( np.tan( angle ) * ( border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] ) )
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"""
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Calibration
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"""
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border[ 'y' ][ 'min' ] -= diff_y
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border[ 'y' ][ 'max' ] -= diff_y
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tot_avg = np.mean(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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]
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)
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def indicator( list_ ):
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if np.mean( list_ ) > 0.75 * tot_avg:
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return 0
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if np.mean( list_ ) < 0.25 * tot_avg:
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return 1
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list_ -= np.min( list_ )
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list_ /= np.max( list_ )
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positions = np.where( list_ > 0.5 )[0]
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if len( positions ) < 10:
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return 2
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if len( positions ) > 400:
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return 3
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distance = np.mean( positions[ 1 : ] - positions[ : -1 ] )
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if distance < 10:
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return 4
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return 10
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"""
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Calibration
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"""
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indicators = np.array( [ indicator( data[ i , border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ] ] ) for i in range( border[ 'y' ][ 'min' ] , border[ 'y' ][ 'max' ] , 1 ) ] )
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tot_avg = np.mean(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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]
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)
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def indicator( list_ ):
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if np.mean( list_ ) > 0.75 * tot_avg:
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return 0
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if np.mean( list_ ) < 0.25 * tot_avg:
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return 1
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list_ -= np.min( list_ )
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list_ /= np.max( list_ )
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positions = np.where( list_ > 0.5 )[0]
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if len( positions ) < 10:
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return 2
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if len( positions ) > 400:
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return 3
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distance = np.mean( positions[ 1 : ] - positions[ : -1 ] )
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if distance < 10:
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return 4
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return 10
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calibration_areas = utils.consecutive( np.where( indicators == 10 )[0] )
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calibration_sizes = [ len( calibration_area ) for calibration_area in calibration_areas ]
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indicators = np.array( [ indicator( data[ i , border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ] ].copy() ) for i in range( border[ 'y' ][ 'min' ] , border[ 'y' ][ 'max' ] , 1 ) ] )
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y_calibrations = [ calibration_areas[ i ] for i in np.argsort( calibration_sizes ) ][ -2 : ]
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calibrations = {
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'top': {
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'x': {
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'min': border['x']['min'],
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'max': border['x']['max'],
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calibration_areas = utils.consecutive( np.where( indicators == 10 )[0] )
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calibration_sizes = [ len( calibration_area ) for calibration_area in calibration_areas ]
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y_calibrations = [ calibration_areas[ i ] for i in np.argsort( calibration_sizes ) ][ -2 : ]
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calibrations = {
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'top': {
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'x': {
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'min': border['x']['min'],
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'max': border['x']['max'],
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},
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'y': {
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'min': border['y']['min'] + y_calibrations[0][ 0],
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'max': border['y']['min'] + y_calibrations[0][-1],
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},
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},
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'y': {
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'min': border['y']['min'] + y_calibrations[0][ 0],
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'max': border['y']['min'] + y_calibrations[0][-1],
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'down': {
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'x': {
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'min': border['x']['min'],
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'max': border['x']['max'],
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},
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'y': {
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'min': border['y']['min'] + y_calibrations[1][ 0],
|
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'max': border['y']['min'] + y_calibrations[1][-1],
|
||||
},
|
||||
},
|
||||
},
|
||||
'down': {
|
||||
'x': {
|
||||
'min': border['x']['min'],
|
||||
'max': border['x']['max'],
|
||||
},
|
||||
'y': {
|
||||
'min': border['y']['min'] + y_calibrations[1][ 0],
|
||||
'max': border['y']['min'] + y_calibrations[1][-1],
|
||||
},
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
with shelve.open( str( cache_file ) ) as cache:
|
||||
cache[ 'rotated_data' ] = data
|
||||
cache[ 'border' ] = border
|
||||
cache[ 'calibrations'] = calibrations
|
||||
|
||||
"""
|
||||
stripes curves detection
|
||||
"""
|
||||
|
||||
list_ = data[
|
||||
calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
|
||||
border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
|
||||
].copy()
|
||||
list_ -= np.min( list_ , axis = 0 )
|
||||
list_ /= np.max( list_ , axis = 0 )
|
||||
size = list_.shape[1]
|
||||
|
||||
size = border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
|
||||
x_stripe = np.arange( border[ 'x' ][ 'min' ] + 1 * size // 4 , border[ 'x' ][ 'min' ] + 3 * size // 4 , 1 ).astype( int )
|
||||
y_stripe = np.array( [
|
||||
np.where(
|
||||
list_[ : , x ] > 0.8
|
||||
)[0][0] for x in x_stripe
|
||||
] )
|
||||
y_stripe = np.argmax( list_ , axis = 0 )
|
||||
good_x = np.where( y_stripe < 2 * np.mean( y_stripe ) )[0]
|
||||
x_stripe = np.arange( 0 , size , 1 ).astype( int )[ good_x ]
|
||||
y_stripe = y_stripe[ good_x ]
|
||||
|
||||
stripes = [ # list of polyval result for each stripe
|
||||
np.polyfit( x_stripe , y_stripe , 2 )
|
||||
np.polyfit( x_stripe , y_stripe , 3 )
|
||||
]
|
||||
|
||||
# First deformation
|
||||
y_diff = np.polyval( stripes[0] , np.arange( 0 , size , 1 ) ).astype( int )
|
||||
results = np.zeros( ( list_.shape[1] , list_.shape[0] - np.max( y_diff ) ) )
|
||||
for i in range( list_.shape[1] ):
|
||||
results[i] = list_[ y_diff[ i ] : list_.shape[0] + y_diff[ i ] - np.max( y_diff ) , i ]
|
||||
results = results.transpose()
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
plt.imshow( results )
|
||||
plt.savefig( 'asset/deformation.png' )
|
||||
y_diff = ( np.polyval( stripes[0] , np.arange( 0 , size , 1 ) ) ).astype( int )
|
||||
y_diff[ np.where( y_diff < 0 ) ] = 0
|
||||
results = np.zeros( ( list_.shape[0] + np.max( y_diff ) , list_.shape[1] ) )
|
||||
for i in range( list_.shape[1] ):
|
||||
results[ : , i ] = np.concatenate( ( np.zeros( np.max( y_diff ) - y_diff[ i ] ) , list_[ : , i ] , np.zeros( y_diff[i] ) ) )
|
||||
|
||||
list_results = np.convolve(
|
||||
np.gradient(
|
||||
np.mean( results , axis = 1 ),
|
||||
) ,
|
||||
np.ones( 50 ),
|
||||
'same' ,
|
||||
)
|
||||
|
||||
fall = utils.consecutive( np.where( list_results < - 0.02 )[0] )
|
||||
fall = np.array( [
|
||||
np.argmax( list_results )
|
||||
] + [
|
||||
consecutive[0] + np.argmin(
|
||||
list_results[ consecutive[0] : consecutive[-1] ]
|
||||
) for consecutive in fall
|
||||
] ).astype( int )
|
||||
|
||||
"""
|
||||
plt.imshow( results , aspect = 'auto' )
|
||||
plt.hlines( fall , 0 , size )
|
||||
plt.show()
|
||||
"""
|
||||
|
||||
temp = np.convolve( results[ : , 10000 ] , np.ones( 50 ) , 'same' )
|
||||
for i in range( len( fall ) - 1 ):
|
||||
temp[ fall[ i ] : fall[ i + 1 ] ] = np.mean( temp[ fall[ i ] : fall[ i + 1 ] ] )
|
||||
|
||||
plt.plot( temp )
|
||||
plt.plot(
|
||||
np.convolve(
|
||||
results[ : , 10000 ],
|
||||
np.ones( 50 ) ,
|
||||
'same' ,
|
||||
),
|
||||
)
|
||||
plt.vlines( fall , 0 , 50 , colors = 'red' )
|
||||
plt.show()
|
||||
|
|
|
|||
Loading…
Reference in a new issue