DataExcel to XML

.gitignore

@@ -4,6 +4,7 @@ panel_jpg/
 result_ssd7_panel_1/
 result_ssd7_panel_2/
 Train&Test_A/
+Train&Test_B/
 result_ssd7_panel/
 result_ssd7_panel_cell/
 Thermal/

.jpg.xml

@@ -0,0 +1 @@
+<annotation><folder>Train_B</folder><filename>Mision 1_DJI_0069.jpg</filename><path>Train_B/images/Mision 1_DJI_0069.jpg</path><source><database>Unknown</database></source><size><width>512</width><height>640</height><depth>1</depth></size><segmented>0</segmented><object><name>4</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>399</xmin><ymin>242</ymin><xmax>415</xmax><ymax>272</ymax></bndbox></object><object><name>4</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>258</xmin><ymin>341</ymin><xmax>274</xmax><ymax>371</ymax></bndbox></object></annotation>

DataFlit2xml.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jan 25 14:12:34 2020
+
+@author: dlsaavedra
+"""
+
+
+
+
+import argparse
+import os
+import numpy as np
+import errno
+import flirimageextractor
+import matplotlib.pyplot as plt
+import pandas
+import matplotlib.patches as patches
+import xml.etree.cElementTree as ET
+
+
+def mkdir(filename):
+    if not os.path.exists(os.path.dirname(filename)):
+        try:
+            os.makedirs(os.path.dirname(filename))
+        except OSError as exc: # Guard against race condition
+            if exc.errno != errno.EEXIST:
+                raise
+                
+
+  
+
+
+argparser = argparse.ArgumentParser(
+    description = 'Data flirt excel to train estructure data')
+
+
+
+argparser.add_argument(
+    '-i',
+    '--input',
+    help='path data excel')
+argparser.add_argument(
+    '-T',
+    '--input_thermal',
+    help='path thermal images')
+    # Example 'Thermal/'
+argparser.add_argument(
+    '-o',
+    '--output',
+    help='folder save Train data')
+    #Examplo 'Train_B/'
+
+
+              
+def _main_(args):
+    
+    input_path   = args.input
+    output_path = args.output
+    thermal_path = args.input_thermal
+    
+    mkdir(output_path)
+    mkdir(output_path + 'images/')
+    mkdir(output_path + 'anns/')
+    
+    Excel = pandas.read_excel(input_path, sheet_name= 'Lista_Archivos_Fotos', header= 1)
+    
+    for index_path in  range(len(Excel.Archivo)):
+        
+        if not pandas.notna(Excel.Archivo[index_path]):
+            continue
+        
+        path_Flir = Excel.loc[index_path]['Archivo']
+        cod_falla = int(Excel.loc[index_path]['Cód. Falla'])
+        sev = Excel.loc[index_path]['Severidad']
+        
+        path_Flir_aux = thermal_path  + '/'.join(path_Flir.split('/')[-2:])
+        
+        if not os.path.isfile(path_Flir_aux):
+            print ('No existe la imagen', path_Flir_aux)
+            continue
+        
+        flir = flirimageextractor.FlirImageExtractor()
+        
+        try:
+            flir.process_image(path_Flir_aux)
+            I = flirimageextractor.FlirImageExtractor.get_thermal_np(flir)
+            w, h = I.shape
+            
+        except:
+            print('No se puede leer la imagen Flir', path_Flir_aux)
+            continue
+        
+        dic_data = flir.get_metadata(path_Flir_aux)
+        meas = [s for s in dic_data.keys() if "Meas" in s]
+        q_bbox = len(meas)//3 # cada bbox tiene 3 parametros
+        
+        param_bbox = []
+        for num_bbox in range(1, q_bbox + 1):
+            # Se guarda los parametros de los boundibox (xmin, ymin, width, height) width = xmax- xmin
+            param_bbox.append(list(map(int, dic_data['Meas' + str(num_bbox) + 'Params'].split(' '))))
+        
+        ##### Save Image and create XML annotations type of fault
+        path_save_img = output_path + 'images/' + '_'.join(path_Flir.split('/')[-2:])
+        path_save_anns =  output_path + 'anns/' + '_'.join(path_Flir.split('/')[-2:])
+        path_save_anns = path_save_anns[:-4] + '.xml'
+        
+        if not os.path.isfile(path_save_img):
+            plt.imsave(path_save_img , I, cmap = 'gray')
+        
+        #si el archivo ya existe se agregan mas anotaciones
+        if os.path.isfile(path_save_anns):
+            
+            et = ET.parse(path_save_anns)
+            root = et.getroot()
+            for box in param_bbox:
+    
+                obj = ET.SubElement(root, "object")
+                ET.SubElement(obj, "name").text = str(cod_falla)
+                ET.SubElement(obj, "pose").text = 'Unspecified'
+                ET.SubElement(obj, "truncated").text = str(0)
+                ET.SubElement(obj, "difficult").text = str(0)
+                bx = ET.SubElement(obj, "bndbox")
+                ET.SubElement(bx, "xmin").text = str(box[0])
+                ET.SubElement(bx, "ymin").text = str(box[1])
+                ET.SubElement(bx, "xmax").text = str(box[0] + box[2])
+                ET.SubElement(bx, "ymax").text = str(box[1] + box[3])
+        
+            tree = ET.ElementTree(root)
+            tree.write(path_save_anns)      
+        
+        ## Si no existe se crea desde cero
+        else:
+            
+            root = ET.Element("annotation")
+            ET.SubElement(root, "folder").text = output_path[:-1]
+            ET.SubElement(root, "filename").text = '_'.join(path_Flir.split('/')[-2:])
+            ET.SubElement(root, "path").text = path_save_img
+            source = ET.SubElement(root, "source")
+            ET.SubElement(source, "database").text = 'Unknown'
+            size = ET.SubElement(root, "size")
+            ET.SubElement(size, "width").text = str(w)
+            ET.SubElement(size, "height").text = str(h)
+            ET.SubElement(size, "depth").text = str(1)
+            ET.SubElement(root, "segmented").text = '0'
+        
+            for box in param_bbox:
+    
+                obj = ET.SubElement(root, "object")
+                ET.SubElement(obj, "name").text = str(cod_falla)
+                ET.SubElement(obj, "pose").text = 'Unspecified'
+                ET.SubElement(obj, "truncated").text = str(0)
+                ET.SubElement(obj, "difficult").text = str(0)
+                bx = ET.SubElement(obj, "bndbox")
+                ET.SubElement(bx, "xmin").text = str(box[0])
+                ET.SubElement(bx, "ymin").text = str(box[1])
+                ET.SubElement(bx, "xmax").text = str(box[0] + box[2])
+                ET.SubElement(bx, "ymax").text = str(box[1] + box[3])
+        
+            tree = ET.ElementTree(root)
+            tree.write(path_save_anns)         
+        
+        
+        
+        
+        
+        
+        
+        
+    
+    
+    files = []
+    # r=root, d=directories, f = files
+    for r, d, f in os.walk(input_path):
+        for file in f:
+            if '.jpg' in file:
+                files.append(os.path.join(r, file))
+    
+    for f in files:
+        flir = flirimageextractor.FlirImageExtractor()
+        print(f)
+        try:
+            flir.process_image(f)
+            I = flirimageextractor.FlirImageExtractor.get_thermal_np(flir)
+        except:
+            I = plt.imread(f)
+        #flir.save_images()
+        #flir.plot()
+        
+        
+        
+        #img = img.astype(np.int8) 
+        W = np.where(np.isnan(I))
+        if np.shape(W)[1] > 0:
+
+            #xmax = np.max(np.amax(W,axis=0))
+            ymax = np.max(np.amin(W,axis=1))
+            img = I[:ymax,:]
+        else:
+            img = I
+        
+        list_string = f.split('/')
+        list_string[-3]+= '_jpg'
+        f_aux = '/'.join(list_string)
+        
+        mkdir(f_aux)
+        plt.imsave(f_aux, img, cmap = 'gray')
+        
+
+
+if __name__ == '__main__':
+    args = argparser.parse_args()
+    _main_(args)

config_7_fault.json

@@ -0,0 +1,28 @@
+{
+    "model" : {
+        "backend":      "ssd7",
+        "input":        400,
+        "labels":               ["1","2","3","4","5","6","7","8"]
+    },
+
+    "train": {
+        "train_image_folder":   "Train&Test_B/images",
+        "train_annot_folder":   "Train&Test_B/anns",
+        "train_image_set_filename": "Train&Test_B/train.txt",
+
+        "train_times":          1,
+        "batch_size":           8,
+        "learning_rate":        1e-4,
+        "nb_epochs":            10,
+        "warmup_epochs":        3,
+	       "saved_weights_name":     "experimento_ssd7_fault.h5",
+        "debug":                true
+    },
+
+
+"test": {
+        "test_image_folder":   "Train&Test_B/images",
+        "test_annot_folder":   "Train&Test_B/anns",
+        "test_image_set_filename":   "Train&Test_B/test.txt"
+    }
+}

log.csv

@@ -419,3 +419,29 @@ epoch,loss,val_loss
 97,3.7434568858146666,4.392282009124756
 98,4.092953279018402,4.400970935821533
 99,3.898336341381073,4.445141792297363
+0,15.208782874850701,12.099904885932581
+1,12.517779222319398,9.29780756537594
+2,11.099253389172087,8.594528625260539
+3,9.910101813108183,7.770827549607007
+4,8.87201379220697,7.3329147011486455
+5,8.233459735573982,7.4922582142388645
+6,7.841278509440156,6.3696390550528
+7,7.244439907315411,6.6484536057087915
+8,7.456777523633532,5.680388607195954
+9,6.6837966272673635,6.748422978529289
+10,6.9996571444257905,5.011203175160422
+11,6.991896384341974,10.520304850677945
+12,6.599298155247258,13.11472865716735
+13,6.449381576576814,7.365987898698494
+14,6.726311950683594,6.321325010328151
+15,6.528120457371579,12.707196548803529
+16,6.336373497814926,10.22905507728235
+17,6.783499336242675,12.416464705965412
+18,6.243661289892826,11.046684393242224
+19,6.450892756256876,8.678086650905325
+20,6.494656276702881,5.327648618328038
+21,6.6301689813584845,6.921413478566639
+22,6.063628935631905,4.657180942706208
+23,5.98993371963501,9.678441431984973
+24,6.50997715479211,7.558368946189311
+25,6.291930182936228,4.799717326662433