8-image reports

nn_eval_lwir-02.py

+#!/usr/bin/env python3
+
+__copyright__ = "Copyright 2018, Elphel, Inc."
+__license__   = "GPL-3.0+"
+__email__     = "andrey@elphel.com"
+
+
+#from PIL import Image
+
+import os
+import sys
+#import glob
+
+#import numpy as np
+
+import imagej_tiffwriter
+
+import time
+import imagej_tiff as ijt
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import qcstereo_functions as qsf
+import numpy as np
+
+#import xml.etree.ElementTree as ET
+
+qsf.TIME_START = time.time()
+qsf.TIME_LAST  = qsf.TIME_START
+
+#IMG_WIDTH =        20 # 324 # tiles per image row Defined in config
+#IMG_HEIGHT =       15 # 324 # tiles per image row Defined in config
+
+DEBUG_LEVEL= 1
+
+try:
+    conf_file =  sys.argv[1]
+except IndexError:
+    print("Configuration path is required as a first argument. Optional second argument specifies root directory for data files")
+    exit(1)
+try:
+    root_dir =  sys.argv[2]
+except IndexError:
+    root_dir =  os.path.dirname(conf_file)
+
+try:
+    modes  =  [sys.argv[3]] # train, infer
+except IndexError:
+    modes = ['train']
+
+    
+print ("Configuration file: " + conf_file)
+parameters, dirs, files, dbg_parameters = qsf.parseXmlConfig(conf_file, root_dir)
+"""
+Temporarily for backward compatibility
+"""
+if not "SLOSS_CLIP" in parameters:
+    parameters['SLOSS_CLIP'] = 0.5
+    print ("Old config, setting SLOSS_CLIP=", parameters['SLOSS_CLIP'])
+    
+"""
+Defined in config file
+"""
+IMG_WIDTH =        None # 20 # 324 # tiles per image row Defined in config
+IMG_HEIGHT =       None # 15 # 324 # tiles per image row Defined in config
+TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
+ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA,  SLOSS_LAMBDA, SLOSS_CLIP  = [None]*5
+SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE  = [None]*5
+CLUSTER_RADIUS,ABSOLUTE_DISPARITY = [None]*2
+FGBG_MODE = 1 # 0 - do not filter by single-plane, 1 - remove split plabnes tiles, 2 - remove split planes and neighbors
+FIGS_EXTENSIONS = ['png','pdf','svg']
+#FIGS_ESXTENSIONS = ['png','pdf','svg']
+EVAL_MODES = ["train","infer"]
+FIGS_SAVESHOW = ['save','show']
+
+    
+globals().update(parameters)
+try:
+    FIGS_EXTENSIONS = globals()['FIGS_ESXTENSIONS'] # fixing typo in configs
+except:
+    pass
+
+
+#exit(0)
+
+
+TILE_SIZE =         TILE_SIDE* TILE_SIDE # == 81
+FEATURES_PER_TILE =  TILE_LAYERS * TILE_SIZE# == 324
+BATCH_SIZE =       ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
+
+SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
+       (["R","A"][ABSOLUTE_DISPARITY]) +
+       (["NS","S8"][SYM8_SUB])+
+       "WLAM"+str(WLOSS_LAMBDA)+
+       "SLAM"+str(SLOSS_LAMBDA)+
+       "SCLP"+str(SLOSS_CLIP)+
+       (['_nG','_G'][SPREAD_CONVERGENCE])+
+       (['_nI','_I'][INTER_CONVERGENCE]) +
+       (['_nHF',"_HF"][HOR_FLIP]) +
+       ('_CP'+str(DISP_DIFF_CAP)) +
+       ('_S'+str(DISP_DIFF_SLOPE))
+       )
+
+##############################################################################
+cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1)
+qsf.prepareFiles(dirs, files, suffix = SUFFIX)
+
+#import tensorflow.contrib.slim as slim
+#NN_DISP =   0
+#HEUR_DISP = 1
+#GT_DISP =   2
+#GT_CONF =   3
+#NN_NAN =    4
+#HEUR_NAN =  5
+#NN_DIFF =   6
+#HEUR_DIFF = 7
+# Now - more layers
+
+
+
+
+CONF_MAX = 0.7
+ERR_AMPL = 0.4 # 0.3
+TIGHT_TOP = 0.95
+TIGHT_HPAD = 1.0
+TIGHT_WPAD = 1.0
+FIGSIZE = [8.5,11.0]
+WOI_COLOR = "red"
+X_COLOR =   "grey"
+X_NEIBS = False
+TRANSPARENT = True # for export
+
+
+
+
+#dbg_parameters
+def get_fig_params(disparity_ranges):
+    fig_params = []
+    for dr in disparity_ranges:
+        if dr[-1][0]=='-':
+            fig_params.append(None)
+        else:
+            subs = []
+            for s in dr[:-1]:
+                mm = s[:2]
+                try:
+                    lims = s[2]
+                except IndexError:
+                    lims = None
+                subs.append({'lim_val':mm, 'lim_xy':lims})        
+            fig_params.append({'name':dr[-1],'ranges':subs}) 
+    return fig_params
+
+#try:
+#fig_params = get_fig_params(dbg_parameters['disparity_ranges'])
+extra_path =   os.path.join(root_dir, dbg_parameters['extra'])
+eo_width =     dbg_parameters['eo_params']['width']
+eo_height =    dbg_parameters['eo_params']['height']
+eo_woi =       dbg_parameters['eo_params']['woi'] # (x,y,width, height)
+eo_disparity_scale = 1.0/dbg_parameters['eo_params']['disparity_scale'] # 14.2
+image_sets =   dbg_parameters['extra_paths'] # list of dictionaries
+
+
+pass
+
+#temporary:
+TIFF_ONLY = False # True
+#max_bad =        2.5 # excludes only direct bad
+max_bad =        2.5 #2.5 # 1.5 # excludes only direct bad
+max_diff =       1.5 # 2.0 # 5.0 # maximal max-min difference
+max_target_err = 1.0 # 0.5 # maximal max-min difference
+max_disp =       5.0
+
+min_strength =   0.18 #ignore tiles below
+min_neibs =      1
+max_log_to_mm =  0.5 # difference between center average and center should be under this fraction of max-min (0 - disables feature) 
+
+
+#num_bins = 256 # number of histogram bins
+num_bins =        15 # 50 # number of histogram bins
+use_gt_weights = True # False # True
+index_gt =         2
+index_gt_weight =  3
+index_heur_err =   7
+index_nn_err =     6
+index_fgbg_sngl = 10
+index_fgbg_neib = 11
+
+index_mm =        23 #  8 # max-min
+index_log =       24 #  9
+index_bad =       25 # 10
+index_num_neibs = 26 # 11
+
+index_fgbg = [index_fgbg_sngl,index_fgbg_neib][X_NEIBS]
+"""
+Debugging high 9-tile variations, removing error for all tiles with lower difference between max and min
+"""
+#min_diff =       0.25 # remove all flat tiles with spread less than this (do not show on heuristic/network disparity errors subplots 
+min_diff =       0 # remove all flat tiles with spread less than this 
+
+
+
+
+max_target_err2 = max_target_err * max_target_err
+if not 'show' in FIGS_SAVESHOW:
+    plt.ioff()
+
+#for mode in ['train','infer']:
+#for mode in ['infer']:
+def cross_out(
+        plt,
+        cross_out_mask):
+    height = cross_out_mask.shape[0]
+    width =  cross_out_mask.shape[1]
+    for row in range (height):
+        for col in range(width):
+            if cross_out_mask[row,col]:
+                xdata = [col-0.3, col+0.3]
+                ydata = [row-0.3, row+0.3]
+                plt.plot(xdata,ydata,color=X_COLOR)
+                ydata = [row+0.3, row-0.3]
+                plt.plot(xdata,ydata,color=X_COLOR)
+
+for mode in modes: #  ['train']:
+    figs = []
+    ffiles = [] # no ext
+    def setlimsxy(lim_xy):
+        if not lim_xy is None:
+            plt.xlim(min(lim_xy[:2]),max(lim_xy[:2]))            
+            plt.ylim(max(lim_xy[2:]),min(lim_xy[2:]))
+    cumul_weights = None                   
+    cmap_disp=plt.get_cmap('viridis') # ('cividis')    
+    cmap_diff=plt.get_cmap('coolwarm') #('seismic') # ('viridis')    
+    for nfile, img_pars in enumerate(image_sets):
+        if not img_pars is None:
+            img_file = files['result'][nfile]
+            if mode == 'infer':
+                img_file = img_file.replace('.npy','-infer.npy')
+            print ("Processing image set: "+img_file)    
+
+            data,labels = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
+            cross_out_mask = data[...,index_fgbg] < 0.5 #data.shape = (15,20,27)
+#            for subindex, rng in enumerate(fpars['ranges']):
+                
+            lim_val = img_pars['range'] # rng['lim_val']
+            lim_val[0] -= ERR_AMPL
+            lim_xy =  [-0.5, IMG_WIDTH - 0.5, -0.5, IMG_HEIGHT - 0.5] # rng['lim_xy']
+            
+            #start new image page
+            fig = plt.figure(figsize=FIGSIZE)
+            fig.canvas.set_window_title(img_pars['title'])
+            fig.suptitle(img_pars['title'])
+            
+            # Create EO DSI image
+            # load tiff image
+             
+            img_ds_main = ijt.imagej_tiff(os.path.join(extra_path,img_pars['dsi_path'] ))
+            ds_main = img_ds_main.image[...,img_pars['dsi_slice']] * eo_disparity_scale
+            ds_main = np.maximum(ds_main, lim_val[0])
+            ds_main = np.minimum(ds_main, lim_val[1])
+            ax_conf=plt.subplot(322)
+            ax_conf.set_title("Hi-res camera disparity map")
+            plt.imshow(ds_main, vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
+            setlimsxy([-0.5, eo_width-0.5, -0.5, eo_height - 0.5])
+            if not eo_woi is None:
+                pass # show frame
+                xdata=[eo_woi['x'], eo_woi['x'] + eo_woi['width'], eo_woi['x'] + eo_woi['width'],  eo_woi['x'],                    eo_woi['x']]
+                ydata=[eo_woi['y'], eo_woi['y'],                   eo_woi['y'] + eo_woi['height'], eo_woi['y'] + eo_woi['height'], eo_woi['y']]
+                plt.plot(xdata,ydata,color=WOI_COLOR)
+            
+            plt.colorbar(orientation='vertical') # location='bottom')
+            '''
+            # Ground truth confidence - to be replaced
+            ax_conf=plt.subplot(322)
+            ax_conf.set_title("Ground truth confidence")
+            plt.imshow(data[...,qsf.GT_CONF], vmin=0, vmax=CONF_MAX, cmap='gray')
+            if not lim_xy is None:
+                pass # show frame
+                xdata=[min(lim_xy[:2]),max(lim_xy[:2]),max(lim_xy[:2]),min(lim_xy[:2]),min(lim_xy[:2])]
+                ydata=[min(lim_xy[2:]),min(lim_xy[2:]),max(lim_xy[2:]),max(lim_xy[2:]),min(lim_xy[2:])]
+                plt.plot(xdata,ydata,color=WOI_COLOR)
+            plt.colorbar(orientation='vertical') # location='bottom')
+            '''
+            
+            ax_gtd=plt.subplot(321)
+            ax_gtd.set_title("Ground truth disparity map")
+            plt.imshow(data[...,qsf.GT_DISP], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
+            setlimsxy(lim_xy)
+            cross_out(plt, cross_out_mask)            
+            plt.colorbar(orientation='vertical') # location='bottom')
+            
+            ax_hed=plt.subplot(323)
+            ax_hed.set_title("Heuristic disparity map")
+            plt.imshow(data[...,qsf.HEUR_NAN], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)            
+            setlimsxy(lim_xy)                        
+            cross_out(plt, cross_out_mask)            
+            plt.colorbar(orientation='vertical') # location='bottom')
+        
+            ax_nnd=plt.subplot(325)
+            ax_nnd.set_title("Network disparity output")
+            plt.imshow(data[...,qsf.NN_NAN], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
+            setlimsxy(lim_xy)                        
+            cross_out(plt, cross_out_mask)            
+            plt.colorbar(orientation='vertical') # location='bottom')
+
+            ax_hee=plt.subplot(324)
+            ax_hee.set_title("Heuristic disparity error")
+            cross_out(plt, cross_out_mask)            
+            plt.imshow(data[...,qsf.HEUR_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL, cmap=cmap_diff)            
+            setlimsxy(lim_xy)                        
+            cross_out(plt, cross_out_mask)            
+            plt.colorbar(orientation='vertical') # location='bottom')
+
+            ax_nne=plt.subplot(326)
+            ax_nne.set_title("Network disparity error")
+            plt.imshow(data[...,qsf.NN_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL, cmap=cmap_diff)            
+            setlimsxy(lim_xy)                        
+            cross_out(plt, cross_out_mask)            
+            plt.colorbar(orientation='vertical') # location='bottom')
+        
+            plt.tight_layout(rect =[0,0,1,TIGHT_TOP], h_pad = TIGHT_HPAD, w_pad = TIGHT_WPAD)
+            
+            figs.append(fig)
+            fb_noext = os.path.splitext(os.path.basename(img_file))[0]#
+#            if subindex > 0:
+#                if subindex < 10:
+#                    fb_noext+="abcdefghi"[subindex-1]
+#                else:
+#                    fb_noext+="-"+str(subindex)
+            ffiles.append(fb_noext)
+            pass
+
+#    
+    #how to allow adjustment before applying tight_layout?
+    pass
+    for fig in figs:
+        fig.tight_layout(rect =[0,0,1,TIGHT_TOP], h_pad = TIGHT_HPAD, w_pad = TIGHT_WPAD)
+    
+    if FIGS_EXTENSIONS and figs and 'save' in FIGS_SAVESHOW:
+        try:
+            print ("Creating output directory for figures: ",dirs['figures'])
+            os.makedirs(dirs['figures'])
+        except:
+            pass
+        pp=None
+        if 'pdf' in FIGS_EXTENSIONS:
+            if mode == 'infer':
+                pdf_path = os.path.join(dirs['figures'],"figures-infer%s.pdf"%str(min_diff))
+            else:
+                pdf_path = os.path.join(dirs['figures'],"figures-train%s.pdf"%str(min_diff))
+            pp= PdfPages(pdf_path)
+        
+        for fb_noext, fig in zip(ffiles,figs):
+            for ext in FIGS_EXTENSIONS:
+                if ext == 'pdf':
+                    pass
+                    fig.savefig(pp,format='pdf')
+                else:
+                    if mode == 'infer':
+                        noext = fb_noext+'-infer'
+                    else:
+                        noext = fb_noext+'-train'
+                    
+                    fig.savefig(
+                        fname = os.path.join(dirs['figures'],noext+"."+ext),
+                        transparent = TRANSPARENT,
+                        )
+                pass
+        if pp:
+            pp.close()
+            
+            
+if 'show' in FIGS_SAVESHOW:    
+    plt.show()
+
+#FIGS_ESXTENSIONS
+
+#qsf.evaluateAllResults(result_files = files['result'],
+#                       absolute_disparity = ABSOLUTE_DISPARITY,
+#                       cluster_radius = CLUSTER_RADIUS)
+print("All done")
+exit (0)

nn_eval_lwir.py

@@ -18,6 +18,7 @@ import imagej_tiffwriter
 import time
 import imagej_tiff as ijt
 import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
 from matplotlib.backends.backend_pdf import PdfPages
 import qcstereo_functions as qsf
 import numpy as np
@@ -156,11 +157,16 @@ def get_fig_params(disparity_ranges):
 extra_path =   os.path.join(root_dir, dbg_parameters['extra'])
 eo_width =     dbg_parameters['eo_params']['width']
 eo_height =    dbg_parameters['eo_params']['height']
-eo_woi =       dbg_parameters['eo_params']['woi'] # (x,y,width, height)
+eo_woi_rel =   dbg_parameters['eo_params']['woi'] # (x,y,width, height)
+eo_woi={
+    "x":eo_width * eo_woi_rel['x'],
+    "y":eo_height * eo_woi_rel['y'],
+    "width":eo_width * eo_woi_rel['width'],
+    "height":eo_height * eo_woi_rel['height'],
+        }
 eo_disparity_scale = 1.0/dbg_parameters['eo_params']['disparity_scale'] # 14.2
 image_sets =   dbg_parameters['extra_paths'] # list of dictionaries
 
-
 pass
 
 #temporary:
@@ -251,6 +257,28 @@ for mode in modes: #  ['train']:
             fig.canvas.set_window_title(img_pars['title'])
             fig.suptitle(img_pars['title'])
             
+            #insert LWIR JPEG image
+            lwir_rgb = mpimg.imread(os.path.join(extra_path,img_pars['lwir_path']))
+            ax_lwir = plt.subplot(421)
+            ax_lwir.set_title("LWIR image (1 of 4)")
+            plt.imshow(lwir_rgb)
+
+            #insert EO JPEG image
+            eo_rgb = mpimg.imread(os.path.join(extra_path,img_pars['eo_path']))
+            ax_eo = plt.subplot(422)
+            ax_eo.set_title("Visible range image (1 of 4)")
+            plt.imshow(eo_rgb)
+            if not eo_woi is None:
+                eo_woi_jpeg={
+                    "x":eo_rgb.shape[1] * eo_woi_rel['x'],
+                    "y":eo_rgb.shape[0] * eo_woi_rel['y'],
+                    "width":eo_rgb.shape[1] * eo_woi_rel['width'],
+                    "height":eo_rgb.shape[0] * eo_woi_rel['height'],
+                        }
+                xdata=[eo_woi_jpeg['x'], eo_woi_jpeg['x'] + eo_woi_jpeg['width'], eo_woi_jpeg['x'] + eo_woi_jpeg['width'],  eo_woi_jpeg['x'],  eo_woi_jpeg['x']]
+                ydata=[eo_woi_jpeg['y'], eo_woi_jpeg['y'], eo_woi_jpeg['y'] + eo_woi_jpeg['height'], eo_woi_jpeg['y'] + eo_woi_jpeg['height'], eo_woi_jpeg['y']]
+                plt.plot(xdata,ydata,color=WOI_COLOR)
+            
             # Create EO DSI image
             # load tiff image
              
@@ -258,7 +286,7 @@ for mode in modes: #  ['train']:
             ds_main = img_ds_main.image[...,img_pars['dsi_slice']] * eo_disparity_scale
             ds_main = np.maximum(ds_main, lim_val[0])
             ds_main = np.minimum(ds_main, lim_val[1])
-            ax_conf=plt.subplot(322)
+            ax_conf=plt.subplot(424)
             ax_conf.set_title("Hi-res camera disparity map")
             plt.imshow(ds_main, vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
             setlimsxy([-0.5, eo_width-0.5, -0.5, eo_height - 0.5])
@@ -282,28 +310,28 @@ for mode in modes: #  ['train']:
             plt.colorbar(orientation='vertical') # location='bottom')
             '''
             
-            ax_gtd=plt.subplot(321)
+            ax_gtd=plt.subplot(423)
             ax_gtd.set_title("Ground truth disparity map")
             plt.imshow(data[...,qsf.GT_DISP], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
             setlimsxy(lim_xy)
             cross_out(plt, cross_out_mask)            
             plt.colorbar(orientation='vertical') # location='bottom')
             
-            ax_hed=plt.subplot(323)
+            ax_hed=plt.subplot(425)
             ax_hed.set_title("Heuristic disparity map")
             plt.imshow(data[...,qsf.HEUR_NAN], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)            
             setlimsxy(lim_xy)                        
             cross_out(plt, cross_out_mask)            
             plt.colorbar(orientation='vertical') # location='bottom')
         
-            ax_nnd=plt.subplot(325)
+            ax_nnd=plt.subplot(427)
             ax_nnd.set_title("Network disparity output")
             plt.imshow(data[...,qsf.NN_NAN], vmin=lim_val[0], vmax=lim_val[1], cmap=cmap_disp)
             setlimsxy(lim_xy)                        
             cross_out(plt, cross_out_mask)            
             plt.colorbar(orientation='vertical') # location='bottom')
 
-            ax_hee=plt.subplot(324)
+            ax_hee=plt.subplot(426)
             ax_hee.set_title("Heuristic disparity error")
             cross_out(plt, cross_out_mask)            
             plt.imshow(data[...,qsf.HEUR_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL, cmap=cmap_diff)            
@@ -311,7 +339,7 @@ for mode in modes: #  ['train']:
             cross_out(plt, cross_out_mask)            
             plt.colorbar(orientation='vertical') # location='bottom')
 
-            ax_nne=plt.subplot(326)
+            ax_nne=plt.subplot(428)
             ax_nne.set_title("Network disparity error")
             plt.imshow(data[...,qsf.NN_DIFF], vmin=-ERR_AMPL, vmax=ERR_AMPL, cmap=cmap_diff)            
             setlimsxy(lim_xy)