2DS/PlotSingleStereoImages.py at main · seboshea/2DS · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
# -*- coding: utf-8 -*-

#Save stereo images as individual .pngs
#FileName are '2DS raw file name + index number in Colocate_base.... + channel number

#v1 20/04/2021
#original


import numpy as np
import h5py
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.colors as colors
#import matplotlib.colors.Colormap
import datetime
import os
from FlightInfo2DS import GetFlightInfo2DS
import imageio

#FlightNumberStr = 'MAC_233'
Info2DS = GetFlightInfo2DS()
SizeThreshold  = 50 # min size particle to save

# Flights = ['MAC_235', 'MAC_233', 'MAC_232','MAC_231', 'MAC_230','MAC_228', 'MAC_227',
#            'MAC_226', 'MAC_225','MAC_224','MAC_223','MAC_222','MAC_221',
#            'MAC_220','MAC_219','MAC_218',
#            'C174_dataPC', 'C172_dataPC', 'C171_dataPC', 'C170_dataPC', 'C169_dataPC',
#            'C098_dataPC', 'C097_dataPC']

# Flights = ['MAC_231',
#            'C174_dataPC', 'C172_dataPC', 'C171_dataPC', 'C170_dataPC', 'C169_dataPC',
#            'C098_dataPC', 'C097_dataPC']

Flights = ['C240_dataPC']

# Time period to plot images
StartTimeStr = '14:09:59' # '-1' = plot all
EndTimeStr = '15:26:16'

if StartTimeStr != '-1' :
    hms = [3600,60,1]
    StartTime = sum([a*b for a,b in zip(hms, map(int,StartTimeStr.split(':')))])
    EndTime = sum([a*b for a,b in zip(hms, map(int,EndTimeStr.split(':')))])
else :
    StartTime  = -1
    EndTime = -1

for FlightNumberStr in Flights :
    print(FlightNumberStr)
    Path2DS = Info2DS[FlightNumberStr,'Path2DS']
    Path2DSsave = Info2DS[FlightNumberStr,'Path2DSsave']
    ThresholdDeltaDimaterY = Info2DS[FlightNumberStr,'ThresholdDeltaDiameterY']
    tmp = [F for F in os.listdir(Path2DSsave) if F.endswith(".h5") and F.startswith('dNdD_L_Colocate_')]
    files = [x.replace('dNdD_L_Colocate_', '') for x in tmp]

    for filena in files :
        print(filena)
        #Load colocation pbp statistics
        SaveFullPath = Path2DSsave + filena[:-3]+'_stereoimages_i/'
        if not os.path.exists(SaveFullPath):
            os.makedirs(SaveFullPath)
        Data_h5 = h5py.File(Path2DSsave + 'Colocate_'+filena, 'r')
        ColocationParticleBufferTimeS_Ch0=np.array(Data_h5['ColocationParticleBufferTimeS_Ch0'])
        ColocationParticleBufferTimeS_Ch1=np.array(Data_h5['ColocationParticleBufferTimeS_Ch1'])
        ColocationImageID_Ch0=np.array(Data_h5['ColocationImageID_Ch0'])
        ColocationImageID_Ch1=np.array(Data_h5['ColocationImageID_Ch1'])
        ColocationEdgeCH0=np.array(Data_h5['ColocationEdgeCh0'])
        ColocationEdgeCH1=np.array(Data_h5['ColocationEdgeCh1'])
        ColocationMeanXYDiameter_Ch1 =np.array(Data_h5['ColocationMeanXYDiameter_Ch1'])
        ColocationMeanXYDiameter_Ch0 =np.array(Data_h5['ColocationMeanXYDiameter_Ch0'])
        ColocationSlicesY_Ch0 = np.array(Data_h5['ColocationSlicesY_Ch0'])
        ColocationSlicesY_Ch1 = np.array(Data_h5['ColocationSlicesY_Ch1'])
        ColocationSecondsCh0 = np.array(Data_h5['ColocationSecondsCh0'])
        ColocationSecondsCh1 = np.array(Data_h5['ColocationSecondsCh1'])
        Data_h5.close()

        #Load images
        Data_h5 = h5py.File(Path2DS+ 'Export_'+filena, 'r')
        ImageTimes=np.array(Data_h5['ImageTimes'][:,0])
        ImageSlices  =np.array(Data_h5['ImageTimes'][:,1])
        ImageID_Ch0 =np.array(Data_h5['ImageTimes'][:,2])
        ImageID_Ch1 =np.array(Data_h5['ImageTimes'][:,2])
        ImageSlices[ImageSlices<0] = np.nan
        #Find start position of image within ImageData
        ImagePosition = np.cumsum(ImageSlices, axis = 0)
        ImagePosition = np.append(0, ImagePosition)

        #Indexes to save images
        Stereo_Idxs = np.nonzero(((ColocationMeanXYDiameter_Ch1 > SizeThreshold) | (ColocationMeanXYDiameter_Ch0 > SizeThreshold))
                       & ((ColocationEdgeCH0 == 0) & (ColocationEdgeCH1 == 0))
                       & ((ThresholdDeltaDimaterY == -1) | (np.absolute(ColocationSlicesY_Ch0 - ColocationSlicesY_Ch1) < ThresholdDeltaDimaterY))
                       & (((ColocationSecondsCh0 > StartTime) & (ColocationSecondsCh0 < EndTime)) | (StartTime == -1)))
        Stereo_Idxs=Stereo_Idxs[0]

        # Number of slices per stereo images
        OutputSlicesY_Ch0 = ColocationSlicesY_Ch0[Stereo_Idxs] /10 # needs to be in pixels not size
        OutputSlicesY_Ch1 = ColocationSlicesY_Ch1[Stereo_Idxs] /10
        #Position within output image array
        OutputImagePositionCh0 = np.cumsum(OutputSlicesY_Ch0, axis = 0)
        OutputImagePositionCh0 = np.append(0, OutputImagePositionCh0)
        OutputImagePositionCh1 = np.cumsum(OutputSlicesY_Ch1, axis = 0)
        OutputImagePositionCh1 = np.append(0, OutputImagePositionCh1)

        # Set up output array
        OutputImageCh0 = np.ones([128,int(np.nansum(OutputSlicesY_Ch0))], dtype=np.uint8)*255
        OutputImageCh1 = np.ones([128,int(np.nansum(OutputSlicesY_Ch1))], dtype=np.uint8)*255

        # select particles and put images in OutputImage
        for j, Idx in enumerate(Stereo_Idxs) :
            # find each image in array
            #channel 0
            Ch0i = np.nonzero((ImageTimes == ColocationParticleBufferTimeS_Ch0[Idx]) & (ImageID_Ch0 == ColocationImageID_Ch0[Idx]))
            i = Ch0i[0]
            if (len(i)==0):
                print('Missing =' + str(i)) # if can't find particle
            else :
                if (len(i) > 1):
                    print('Multiple particles with same ID=' + str(i)) #repeat particle
                    i=i[0]
                if (ImagePosition[i+1]-ImagePosition[i] != (ColocationSlicesY_Ch0[Idx]/10) ):
                    print('zero slice =' + str(i)) # 0 slice particle
                else:
                    ImageCH0 = np.array(Data_h5['ImageData'][:,int(ImagePosition[i]):int(ImagePosition[i+1])])
                    imageio.imwrite(SaveFullPath+ filena.replace('.h5', '')+'_'+str(Idx)+'_Ch0.png', ImageCH0)

            #Channel 1
            Ch1i = np.nonzero((ImageTimes == ColocationParticleBufferTimeS_Ch1[Idx]) & (ImageID_Ch1 == ColocationImageID_Ch1[Idx]))
            i = Ch1i[0]
            if (len(i)==0):
                print('Missing =' + str(i)) # if can't find particle
            else :
                if (len(i) > 1):
                    print('Multiple particles with same ID=' + str(i)) #repeat particle
                    i=i[0]
                if (ImagePosition[i+1]-ImagePosition[i] != (ColocationSlicesY_Ch1[Idx]/10) ):
                    print('zero slice =' + str(i)) # 0 slice particle
                else:
                    ImageCH1 = np.array(Data_h5['ImageData'][:,int(ImagePosition[i]):int(ImagePosition[i+1])])
                    imageio.imwrite(SaveFullPath+ filena.replace('.h5', '')+'_'+str(Idx)+'_Ch1.png', ImageCH1)

        Data_h5.close()