225 lines
6.4 KiB
Python
225 lines
6.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sat Jan 25 14:12:34 2020
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@author: dlsaavedra
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"""
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import argparse
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import os
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import numpy as np
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import errno
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import flirimageextractor
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import matplotlib.pyplot as plt
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import pandas
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import matplotlib.patches as patches
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import xml.etree.cElementTree as ET
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def mkdir(filename):
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if not os.path.exists(os.path.dirname(filename)):
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try:
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os.makedirs(os.path.dirname(filename))
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except OSError as exc: # Guard against race condition
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if exc.errno != errno.EEXIST:
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raise
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argparser = argparse.ArgumentParser(
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description = 'Data flirt excel to train estructure data')
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argparser.add_argument(
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'-i',
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'--input',
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help='path data excel')
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argparser.add_argument(
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'-T',
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'--input_thermal',
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help='path thermal images')
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# Example 'Thermal/'
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argparser.add_argument(
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'-o',
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'--output',
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help='folder save Train data')
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#Examplo 'Train_B/'
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def _main_(args):
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input_path = args.input
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output_path = args.output
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thermal_path = args.input_thermal
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mkdir(output_path)
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mkdir(output_path + 'images/')
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mkdir(output_path + 'anns/')
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Excel = pandas.read_excel(input_path, sheet_name= 'Lista_Archivos_Fotos', header = 1)
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for index_path in range(len(Excel.Archivo)):
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if not pandas.notna(Excel.Archivo[index_path]):
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continue
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path_Flir = Excel.loc[index_path]['Archivo']
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cod_falla = int(Excel.loc[index_path]['Cód. Falla'])
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sev = Excel.loc[index_path]['Severidad']
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#if cod_falla != 4:
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# continue
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## Junta las mismas fotos con distintos label EJ :
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# DJI_0021B ---> DJI_0021
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aux = path_Flir.split('/')[-2:]
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if len(aux[1].split('.')[0]) > 8:
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aux[1] = aux[1].split('.')[0][:8] + '.' + aux[1].split('.')[1]
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path_Flir_aux = thermal_path + '/'.join(path_Flir.split('/')[-2:])
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if not os.path.isfile(path_Flir_aux):
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print ('No existe la imagen', path_Flir_aux)
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continue
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flir = flirimageextractor.FlirImageExtractor()
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try:
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flir.process_image(path_Flir_aux)
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I = flirimageextractor.FlirImageExtractor.get_thermal_np(flir)
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w, h = I.shape
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except:
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print('No se puede leer la imagen Flir', path_Flir_aux)
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continue
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dic_data = flir.get_metadata(path_Flir_aux)
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meas = [s for s in dic_data.keys() if "Meas" in s]
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q_bbox = len(meas)//3 # cada bbox tiene 3 parametros
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param_bbox = []
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for num_bbox in range(1, q_bbox + 1):
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# Se guarda los parametros de los boundibox (xmin, ymin, width, height) width = xmax- xmin
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param_bbox.append(list(map(int, dic_data['Meas' + str(num_bbox) + 'Params'].split(' '))))
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##### Save Image and create XML annotations type of fault
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path_save_img = output_path + 'images/' + '_'.join(aux)
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path_save_anns = output_path + 'anns/' + '_'.join(aux)
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path_save_anns = path_save_anns[:-4] + '.xml'
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if not os.path.isfile(path_save_img):
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plt.imsave(path_save_img , I, cmap = 'gray')
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#si el archivo ya existe se agregan mas anotaciones
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if os.path.isfile(path_save_anns):
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et = ET.parse(path_save_anns)
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root = et.getroot()
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for box in param_bbox:
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obj = ET.SubElement(root, "object")
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ET.SubElement(obj, "name").text = str(cod_falla)
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ET.SubElement(obj, "pose").text = 'Unspecified'
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ET.SubElement(obj, "truncated").text = str(0)
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ET.SubElement(obj, "difficult").text = str(0)
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bx = ET.SubElement(obj, "bndbox")
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ET.SubElement(bx, "xmin").text = str(box[0])
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ET.SubElement(bx, "ymin").text = str(box[1])
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ET.SubElement(bx, "xmax").text = str(box[0] + box[2])
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ET.SubElement(bx, "ymax").text = str(box[1] + box[3])
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tree = ET.ElementTree(root)
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tree.write(path_save_anns)
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## Si no existe se crea desde cero
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else:
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root = ET.Element("annotation")
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ET.SubElement(root, "folder").text = output_path[:-1]
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ET.SubElement(root, "filename").text = '_'.join(path_Flir.split('/')[-2:])
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ET.SubElement(root, "path").text = path_save_img
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source = ET.SubElement(root, "source")
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ET.SubElement(source, "database").text = 'Unknown'
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size = ET.SubElement(root, "size")
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ET.SubElement(size, "width").text = str(w)
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ET.SubElement(size, "height").text = str(h)
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ET.SubElement(size, "depth").text = str(1)
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ET.SubElement(root, "segmented").text = '0'
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for box in param_bbox:
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obj = ET.SubElement(root, "object")
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ET.SubElement(obj, "name").text = str(cod_falla)
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ET.SubElement(obj, "pose").text = 'Unspecified'
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ET.SubElement(obj, "truncated").text = str(0)
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ET.SubElement(obj, "difficult").text = str(0)
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bx = ET.SubElement(obj, "bndbox")
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ET.SubElement(bx, "xmin").text = str(box[0])
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ET.SubElement(bx, "ymin").text = str(box[1])
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ET.SubElement(bx, "xmax").text = str(box[0] + box[2])
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ET.SubElement(bx, "ymax").text = str(box[1] + box[3])
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tree = ET.ElementTree(root)
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tree.write(path_save_anns)
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files = []
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# r=root, d=directories, f = files
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for r, d, f in os.walk(input_path):
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for file in f:
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if '.jpg' in file:
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files.append(os.path.join(r, file))
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for f in files:
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flir = flirimageextractor.FlirImageExtractor()
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print(f)
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try:
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flir.process_image(f)
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I = flirimageextractor.FlirImageExtractor.get_thermal_np(flir)
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except:
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I = plt.imread(f)
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#flir.save_images()
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#flir.plot()
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#img = img.astype(np.int8)
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W = np.where(np.isnan(I))
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if np.shape(W)[1] > 0:
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#xmax = np.max(np.amax(W,axis=0))
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ymax = np.max(np.amin(W,axis=1))
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img = I[:ymax,:]
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else:
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img = I
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list_string = f.split('/')
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list_string[-3]+= '_jpg'
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f_aux = '/'.join(list_string)
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mkdir(f_aux)
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plt.imsave(f_aux, img, cmap = 'gray')
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if __name__ == '__main__':
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args = argparser.parse_args()
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_main_(args)
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