Add capture shift fix and deep-enhancement preprocess
This commit is contained in:
Emilio Martínez
parent
4060b73dde
commit
6f5d1e1ce9
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@ -98,7 +98,7 @@ blocks:
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|||
start: '0'
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step: '1'
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stop: int(4096*1.5)
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value: '1056'
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value: '1800'
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widget: counter
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states:
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bus_sink: false
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@ -111,7 +111,7 @@ blocks:
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id: variable
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parameters:
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comment: ''
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value: '800'
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value: '1600'
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states:
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bus_sink: false
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bus_source: false
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@ -143,7 +143,7 @@ blocks:
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start: '0'
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step: '1'
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stop: int(2160*1.5)
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value: '628'
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value: '1000'
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widget: counter
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states:
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bus_sink: false
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@ -156,7 +156,7 @@ blocks:
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id: variable
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parameters:
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comment: ''
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value: '600'
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value: '900'
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states:
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bus_sink: false
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bus_source: false
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@ -788,7 +788,7 @@ blocks:
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alias: ''
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blanking: 'True'
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comment: ''
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image_file: /home/emidan19/deep-tempest/images/VAMO!!.png
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image_file: /home/emidan19/deep-tempest/images/1600x900_google.png
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maxoutbuf: '0'
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minoutbuf: '0'
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mode: '1'
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@ -840,25 +840,6 @@ blocks:
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coordinate: [2048, 924.0]
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rotation: 180
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state: enabled
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- name: tempest_sync_detector_0_0
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id: tempest_sync_detector
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parameters:
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affinity: ''
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alias: ''
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comment: ''
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hblanking: interpolatedHblank
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hscreen: interpolatedHscreen
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maxoutbuf: '0'
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minoutbuf: '0'
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vblanking: Vblank
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vscreen: Vsize-Vblank
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states:
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bus_sink: false
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bus_source: false
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bus_structure: null
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coordinate: [1440, 676.0]
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rotation: 0
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state: enabled
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- name: uhd_usrp_source_0
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id: uhd_usrp_source
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parameters:
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@ -1256,7 +1237,7 @@ blocks:
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connections:
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- [FFT_autocorrelation_0_0, '0', tempest_fft_peak_fine_sampling_sync_0_0, '0']
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- [Keep_1_in_N_Frames_0, '0', FFT_autocorrelation_0_0, '0']
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- [Keep_1_in_N_Frames_0, '0', tempest_sync_detector_0_0, '0']
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- [Keep_1_in_N_Frames_0, '0', blocks_delay_0_0, '0']
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- [analog_sig_source_x_0, '0', blocks_multiply_xx_0, '1']
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- [binary_serializer_0, '0', blocks_add_xx_0, '0']
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- [binary_serializer_0, '0', blocks_float_to_complex_0, '0']
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@ -1279,10 +1260,8 @@ connections:
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- [tempest_TMDS_image_source_0, '2', blocks_null_sink_0, '1']
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- [tempest_fft_peak_fine_sampling_sync_0_0, '0', qtgui_time_sink_x_1, '0']
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- [tempest_fft_peak_fine_sampling_sync_0_0, en, FFT_autocorrelation_0_0, en]
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- [tempest_fft_peak_fine_sampling_sync_0_0, en, tempest_sync_detector_0_0, en]
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- [tempest_fft_peak_fine_sampling_sync_0_0, rate, uhd_usrp_source_0, command]
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- [tempest_normalize_flow_0, '0', blocks_float_to_short_0, '0']
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- [tempest_sync_detector_0_0, '0', blocks_delay_0_0, '0']
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- [uhd_usrp_source_0, '0', Keep_1_in_N_Frames_0, '0']
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metadata:
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@ -41,14 +41,14 @@ blocks:
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bus_sink: false
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bus_source: false
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bus_structure: null
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coordinate: [464, 148]
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coordinate: [448, 148.0]
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rotation: 0
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state: enabled
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- name: Hsize
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id: variable
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parameters:
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comment: ''
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value: '1800'
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value: '2200'
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states:
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bus_sink: false
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bus_source: false
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@ -60,7 +60,7 @@ blocks:
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id: variable
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parameters:
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comment: ''
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value: '1600'
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value: '1920'
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states:
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bus_sink: false
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bus_source: false
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@ -77,14 +77,14 @@ blocks:
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bus_sink: false
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bus_source: false
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bus_structure: null
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coordinate: [384, 148]
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coordinate: [352, 148.0]
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rotation: 0
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state: enabled
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- name: Vsize
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id: variable
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parameters:
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comment: ''
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value: '1000'
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value: '1125'
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states:
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bus_sink: false
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bus_source: false
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@ -96,7 +96,7 @@ blocks:
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id: variable
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parameters:
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comment: ''
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value: '900'
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value: '1080'
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states:
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bus_sink: false
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bus_source: false
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@ -113,7 +113,7 @@ blocks:
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bus_sink: false
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bus_source: false
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bus_structure: null
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coordinate: [304, 148]
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coordinate: [280, 148.0]
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rotation: 0
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state: enabled
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- name: epsilon_channel
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@ -209,7 +209,7 @@ blocks:
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bus_sink: false
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bus_source: false
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bus_structure: null
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coordinate: [232, 148]
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coordinate: [216, 148.0]
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rotation: 0
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state: enabled
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- name: interpolatedHsize
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@ -285,8 +285,8 @@ blocks:
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orient: Qt.Horizontal
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rangeType: float
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start: '0'
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step: 1e-4
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stop: 2e-1
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step: 1e-3
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stop: '2'
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value: '0'
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widget: counter_slider
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states:
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@ -450,6 +450,22 @@ blocks:
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coordinate: [160, 408.0]
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rotation: 0
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state: enabled
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- name: blocks_complex_to_float_0
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id: blocks_complex_to_float
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parameters:
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affinity: ''
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alias: ''
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comment: ''
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maxoutbuf: '0'
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minoutbuf: '0'
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vlen: '1'
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states:
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bus_sink: false
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bus_source: false
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bus_structure: null
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coordinate: [824, 712.0]
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rotation: 180
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state: true
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- name: blocks_complex_to_mag_0
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id: blocks_complex_to_mag
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parameters:
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@ -463,9 +479,9 @@ blocks:
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bus_sink: false
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bus_source: false
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bus_structure: null
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coordinate: [824, 656.0]
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coordinate: [832, 568.0]
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rotation: 180
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state: enabled
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state: disabled
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- name: blocks_delay_0
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id: blocks_delay
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parameters:
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@ -736,7 +752,7 @@ blocks:
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alias: ''
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blanking: 'True'
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comment: ''
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image_file: /home/emidan19/Desktop/deep-tempest/capturas/fine-tuning-images/Abolghasemi_Pay_Attention_-_Robustifying_a_Deep_Visuomotor_Policy_Through_Task-Focused_CVPR_2019_paper.png
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image_file: /home/emidan19/Desktop/deep-tempest/drunet/notebooks/images/mattermost_screen.png
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maxoutbuf: '0'
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minoutbuf: '0'
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mode: '1'
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@ -750,7 +766,7 @@ blocks:
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- name: tempest_buttonToFileSink_0
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id: tempest_buttonToFileSink
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parameters:
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Filename: /home/emidan19/Desktop/captura_simulada
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Filename: /home/emidan19/Desktop/captura_simulada_shiftfix
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H_size: Hsize
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V_size: Vsize
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affinity: ''
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@ -896,7 +912,9 @@ connections:
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- [binary_serializer_0_0_0, '0', blocks_add_xx_0, '2']
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- [blocks_add_xx_0, '0', blocks_float_to_complex_0, '0']
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- [blocks_add_xx_0, '0', interp_fir_filter_xxx_0, '0']
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- [blocks_complex_to_float_0, '0', tempest_normalize_flow_0, '0']
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- [blocks_complex_to_mag_0, '0', tempest_normalize_flow_0, '0']
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- [blocks_delay_0, '0', blocks_complex_to_float_0, '0']
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- [blocks_delay_0, '0', blocks_complex_to_mag_0, '0']
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- [blocks_delay_0, '0', tempest_buttonToFileSink_0, '0']
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- [blocks_float_to_complex_0, '0', blocks_throttle_0_0, '0']
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@ -1,7 +1,8 @@
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import numpy as np
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from matplotlib import pyplot as plt
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from scipy import signal
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from scipy.fft import fft, ifft, fftshift
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import cv2 as cv
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from scipy.spatial import distance_matrix
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def autocorr(x):
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"""Compute autocorrelation function of 1-D array
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@ -463,3 +464,267 @@ def TMDS_serial(I):
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del(channel_list)
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return Iserials
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def remove_outliers(I, radius=3, threshold=20):
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"""
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Replaces a pixel by the median of the pixels in the surrounding if it deviates from the median by more than a certain value (the threshold).
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"""
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# Copy input
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I_output = I.copy()
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# Apply median filter
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I_median = cv.medianBlur(I,radius)
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# Replace with median value where difference with median exceedes threshold
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where_replace = np.abs(I-I_median) > threshold
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I_output[where_replace] = I_median[where_replace]
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return I_output
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def adjust_dynamic_range(I):
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I_output = I.astype('float32')
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I_output = (I_output - I_output.min()) / (I_output.max() - I_output.min())
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I_output = 255 * I_output
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return I_output.astype('uint8')
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def find_intersection(line1, line2):
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"""Finds the intersection of two lines given in Hesse normal form.
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Returns closest integer pixel locations.
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See https://stackoverflow.com/a/383527/5087436
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"""
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rho1, theta1 = line1
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rho2, theta2 = line2
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A = np.array([
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[np.cos(theta1), np.sin(theta1)],
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[np.cos(theta2), np.sin(theta2)]
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])
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b = np.array([[rho1], [rho2]])
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x0, y0 = np.linalg.solve(A, b)
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x0, y0 = int(np.round(x0)), int(np.round(y0))
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# Return as row-column coordinates
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return [y0, x0]
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def apply_blanking_shift(I, h_active=1600, v_active=900,
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h_blanking=200,v_blanking=100,
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pad_len=300, debug=False):
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"""
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Find
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"""
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if debug:
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# Show original image
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plt.figure(figsize=(12,10))
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plt.title(f'Original image ')
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plt.imshow(I)
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plt.axis('off')
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plt.show()
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# Color to RGB
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I_gray = cv.cvtColor(I,cv.COLOR_BGR2GRAY)
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# I_gray = cv.medianBlur(I_gray,3)
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I_gray = cv.GaussianBlur(I_gray,ksize=(5,5),sigmaX=3,sigmaY=3)
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# I_gray = cv.blur(I_gray,(3,3))
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# Wrap-padding image to get whole blanking pattern
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pad_size_gray = ((pad_len,0),(pad_len,0))
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pad_size = ((pad_len,0),(pad_len,0),(0,0))
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I_gray = np.pad(I_gray,pad_size_gray,'wrap')
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I = np.pad(I,pad_size,'wrap')
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# Edge-detector with Canny filter with empirical parameters
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I_edge = cv.Canny(I_gray,40,50,apertureSize = 3) # sin blur
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if debug:
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# Show padded image and edges
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plt.figure()
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plt.title(f'Original image wrap-padded {pad_len} pixels up and left sided')
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plt.imshow(I)
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plt.axis('off')
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plt.show()
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plt.figure()
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plt.title('Canny edged image')
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plt.imshow(I_edge,cmap='gray')
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plt.axis('off')
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plt.show()
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# Hough Transform resolution and minimum votes threshold
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theta_step = np.pi/2
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rho_step = 1
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# votes_thrs = 255 # sin blur
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votes_thrs = 80
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rho_max = np.sqrt(I_gray.shape[0]**2 + I_gray.shape[1]**2)
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# Find Hough Transform
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lines = cv.HoughLines(I_edge, rho_step, theta_step, votes_thrs, None, 0, 0)
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if debug:
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# Show detected lines
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I_lines = I_gray.copy()
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for line in lines:
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rho = line[0][0]
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theta = line[0][1]
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a = np.cos(theta)
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b = np.sin(theta)
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x0 = a * rho
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y0 = b * rho
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pt1 = (int(x0 + rho_max*(-b)), int(y0 + rho_max*(a)))
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pt2 = (int(x0 - rho_max*(-b)), int(y0 - rho_max*(a)))
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cv.line(I_lines, pt1, pt2, (255,0,0), 1, cv.LINE_AA)
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plt.figure()
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plt.title('All detected lines')
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plt.imshow(I_lines)
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plt.axis('off')
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plt.show()
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# Angle and rho arrays
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lines_angles = lines[:,0,1]
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lines_rhos = lines[:,0,0]
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# Find unique lines angles detected
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unique_angles = np.unique(lines_angles)
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blankings = [h_blanking, v_blanking]
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# Initiate blanking lines variable
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blanking_lines = []
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for angle, blanking in zip(unique_angles, blankings):
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# Keep lines with certain angle
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angle_lines = lines[lines[:,0,1]==angle]
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angle_lines = angle_lines[:,0,:]
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# Keep the pair with rho distance that equals blanking
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# First, compute the distance matrix over all rhos:
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rho_angle_lines = np.array(angle_lines[:,0]).reshape(-1,1)
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rho_distances = np.abs(np.abs(distance_matrix(rho_angle_lines, rho_angle_lines)) - blanking)
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# Find minimum index
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pair_lines_idx = np.unravel_index(rho_distances.argmin(), rho_distances.shape)
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# Add blanking limit line
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for line in pair_lines_idx:
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blanking_lines.append(angle_lines[line])
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# List to array
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blanking_lines = np.array(blanking_lines)
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# Show blanking limit lines
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if debug:
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I_lines = I_gray.copy()
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for line in blanking_lines:
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rho = line[0]
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theta = line[1]
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a = np.cos(theta)
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b = np.sin(theta)
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x0 = a * rho
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y0 = b * rho
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pt1 = (int(x0 + rho_max*(-b)), int(y0 + rho_max*(a)))
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pt2 = (int(x0 - rho_max*(-b)), int(y0 - rho_max*(a)))
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cv.line(I_lines, pt1, pt2, (255,0,0), 3, cv.LINE_AA)
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plt.figure(figsize=(12,10))
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plt.title('Blanking limit lines')
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plt.imshow(I_lines)
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plt.axis('off')
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plt.show()
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# Initialize top-left corner blanking lines to find intersection
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# These lines statisfies to be the ones with bigger rho value
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blanking_start = []
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unique_angles = np.unique(blanking_lines[:,1])
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for angulo in unique_angles:
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angle_lines = blanking_lines[blanking_lines[:,1]==angulo]
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max_rho_line = angle_lines[np.argmax(angle_lines[:,0])]
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blanking_start.append(max_rho_line)
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if len(blanking_start)!=2:
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return -1
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if debug:
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I_lines = I_gray.copy()
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for line in blanking_start:
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rho = line[0]
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theta = line[1]
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a = np.cos(theta)
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b = np.sin(theta)
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x0 = a * rho
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y0 = b * rho
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pt1 = (int(x0 + rho_max*(-b)), int(y0 + rho_max*(a)))
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pt2 = (int(x0 - rho_max*(-b)), int(y0 - rho_max*(a)))
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cv.line(I_lines, pt1, pt2, (255,0,0), 3, cv.LINE_AA)
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plt.figure(figsize=(12,10))
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plt.title('Blanking start lines')
|
||||
plt.imshow(I_lines)
|
||||
plt.axis('off')
|
||||
plt.show()
|
||||
|
||||
# Find blanking start lines intersection coordinates
|
||||
x_shift, y_shift = find_intersection(blanking_start[0],blanking_start[1])
|
||||
|
||||
# Adjust to active image only (remove all blanking)
|
||||
I_shift = I[pad_len:,pad_len:]
|
||||
I_shift = np.roll(I_shift, -x_shift+pad_len, axis=0)
|
||||
I_shift = np.roll(I_shift, -y_shift+pad_len, axis=1)
|
||||
I_shift = I_shift[:v_active,:h_active]
|
||||
|
||||
if debug:
|
||||
plt.figure(figsize=(12,10))
|
||||
plt.title('Blanking removed image')
|
||||
plt.imshow(I_shift)
|
||||
plt.axis('off')
|
||||
plt.show()
|
||||
|
||||
return I_shift
|
||||
|
||||
def preprocess_raw_capture(I, h_active, v_active,
|
||||
h_blanking, v_blanking, debug=False):
|
||||
"""
|
||||
Center raw captured image, filter noise and adjust the contrast
|
||||
"""
|
||||
|
||||
# Center image. Returns -1 if no sufficient lines where detected
|
||||
# In the latter case, use image as is without centering
|
||||
is_centered = True
|
||||
I_shift_fix = apply_blanking_shift(I,
|
||||
h_active=h_active, v_active=v_active,
|
||||
h_blanking=h_blanking, v_blanking=v_blanking,
|
||||
debug=debug
|
||||
)
|
||||
if np.shape(I_shift_fix) == ():
|
||||
I_shift_fix = I.copy()
|
||||
is_centered = False
|
||||
|
||||
# Remove outliers with median thresholding heuristic
|
||||
# Default: radius=3, threshold=20
|
||||
I_no_outliers = remove_outliers(I_shift_fix)
|
||||
|
||||
# Stretch dynamic range to [0,255]
|
||||
I_out = adjust_dynamic_range(I_no_outliers)
|
||||
|
||||
if debug:
|
||||
plt.figure(figsize=(12,10))
|
||||
ax0 = plt.subplot(3,1,1)
|
||||
ax0.imshow(I_shift_fix, interpolation='none')
|
||||
ax0.set_title('Centered image'*is_centered + 'Image'*(~is_centered))
|
||||
ax0.axis('off')
|
||||
ax1 = plt.subplot(3,1,2, sharex=ax0, sharey=ax0)
|
||||
ax1.imshow(I_no_outliers, interpolation='none')
|
||||
ax1.set_title('Outliers removed')
|
||||
ax1.axis('off')
|
||||
ax1 = plt.subplot(3,1,3, sharex=ax0, sharey=ax0)
|
||||
ax1.imshow(I_out, interpolation='none')
|
||||
ax1.set_title('Contrast adjusted')
|
||||
ax1.axis('off')
|
||||
plt.show()
|
||||
|
||||
return I_out
|
||||
|
|
@ -16,15 +16,16 @@ from PIL import Image
|
|||
|
||||
### Deep-TEMPEST imports
|
||||
import os
|
||||
import math
|
||||
import argparse
|
||||
import time
|
||||
import random
|
||||
import torch
|
||||
import sys
|
||||
|
||||
# Adding gr-tempest/python folder to system path
|
||||
dtutils_path = '/home/emidan19/deep-tempest/gr-tempest/python'
|
||||
sys.path.insert(0,dtutils_path)
|
||||
from DTutils import preprocess_raw_capture, apply_blanking_shift
|
||||
|
||||
# Adding KAIR folder to the system path
|
||||
# current_file_path = os.path.abspath(__file__)
|
||||
# sys.path.insert(0, os.path.join(current_file_path,'../../KAIR/'))
|
||||
kair_path = '/home/emidan19/deep-tempest/KAIR'
|
||||
sys.path.insert(0,kair_path)
|
||||
from utils import utils_option as option
|
||||
|
|
@ -109,32 +110,49 @@ class buttonToFileSink(gr.sync_block):
|
|||
self.message_port_register_in(pmt.intern("en")) #declare message port
|
||||
self.set_msg_handler(pmt.intern("en"), self.handle_msg) #declare handler for messages
|
||||
self.stream_image = [] # initialize list to apppend samples
|
||||
|
||||
# TODO: fancy active-blanking resolution identification
|
||||
self.V_active = (self.V_size==1125)*1080 + (self.V_size==1000)*900 + (self.V_size==750) *720 + (self.V_size==628) *600 + (self.V_size==525)*480
|
||||
self.H_active = (self.H_size==2200)*1920 + (self.H_size==1800)*1600 + (self.H_size==1650)*1280 + (self.H_size==1056)*800 + (self.H_size==800)*640
|
||||
|
||||
self.V_blanking = self.V_size - self.V_active
|
||||
self.H_blanking = self.H_size - self.H_active
|
||||
|
||||
if enhance_image:
|
||||
# Load model
|
||||
self.model = load_enhancement_model()
|
||||
# Define inference function
|
||||
def inference (img):
|
||||
# Preprocess image to network input
|
||||
img_L = img[:,:,:2]
|
||||
# Preprocess image to network input
|
||||
# (correct shift, remove sparkle noise and to float tensor)
|
||||
img_L = preprocess_raw_capture(img, h_active=self.H_active, v_active=self.V_active,
|
||||
h_blanking=self.H_blanking, v_blanking=self.V_blanking)
|
||||
img_L = img_L[:,:,:2]
|
||||
img_L = util.uint2single(img_L)
|
||||
img_L = util.single2tensor4(img_L)
|
||||
|
||||
# Model inference on image
|
||||
img_E = self.model(img_L)
|
||||
img_E = util.tensor2uint(img_E)
|
||||
|
||||
return img_E
|
||||
|
||||
self.inference = inference
|
||||
else:
|
||||
# No enhancement.
|
||||
# No enhancement. Correct shift
|
||||
def inference(img):
|
||||
return img
|
||||
img_out = apply_blanking_shift(img, h_active=self.H_active, v_active=self.V_active,
|
||||
h_blanking=self.H_blanking, v_blanking=self.V_blanking)
|
||||
# Check for any errors. If so, return original image
|
||||
if np.shape(img_out) == ():
|
||||
img_out = img.copy()
|
||||
return img_out
|
||||
self.inference = inference
|
||||
|
||||
|
||||
def work(self, input_items, output_items): # Don't process, just save samples
|
||||
|
||||
self.available_samples = len(input_items[0]) #available samples at the input for read at this moment
|
||||
def work(self, input_items, output_items):
|
||||
# Don't process, just save available samples
|
||||
self.available_samples = len(input_items[0])
|
||||
|
||||
if self.en == True:
|
||||
|
||||
|
|
@ -144,19 +162,6 @@ class buttonToFileSink(gr.sync_block):
|
|||
|
||||
if len(self.stream_image)==self.num_samples:#or self.remaining2Save > 0:
|
||||
|
||||
# if self.remaining2Save > 0 and self.remaining2Save < self.available_samples: #remaining to save from last time, don't care if true was pressed or not
|
||||
# self.savingSamples = self.remaining2Save
|
||||
# self.remaining2Save = 0 #no more remaining after this
|
||||
# elif self.remaining2Save > 0: #the remaining still are more than available
|
||||
# self.savingSamples = self.available_samples #save whatever available
|
||||
# self.remaining2Save = self.remaining2Save - self.savingSamples # refresh whatever left to save next time
|
||||
# else: #not remaining from last time but pressed true
|
||||
# if self.num_samples > self.available_samples: #too large for the available samples
|
||||
# self.savingSamples = self.available_samples # save whatever available and wait for the remaining
|
||||
# self.remaining2Save = self.num_samples - self.savingSamples #remaining to save
|
||||
# else:
|
||||
# self.savingSamples = self.num_samples # enough samples available when pressing button
|
||||
|
||||
# Save the number of samples calculated before
|
||||
self.save_samples()
|
||||
# Back to default
|
||||
|
|
|
|||
Loading…
Reference in New Issue