DataFlit to XML 2
This commit is contained in:
Daniel Saavedra
1
.gitignore
vendored
1
.gitignore
vendored
@@ -5,6 +5,7 @@ result_ssd7_panel_1/
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result_ssd7_panel_2/
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Train&Test_A/
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Train&Test_B/
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Train&Test_C/
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result_ssd7_panel/
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result_ssd7_panel_cell/
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Thermal/
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111
DataFlit2xml.py
111
DataFlit2xml.py
@@ -27,9 +27,9 @@ def mkdir(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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@@ -53,69 +53,76 @@ argparser.add_argument(
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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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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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path_Flir_aux = thermal_path + '/'.join(path_Flir.split('/')[-2:])
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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(aux)
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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(path_Flir.split('/')[-2:])
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path_save_anns = output_path + 'anns/' + '_'.join(path_Flir.split('/')[-2:])
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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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@@ -126,13 +133,13 @@ def _main_(args):
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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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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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@@ -144,9 +151,9 @@ def _main_(args):
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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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@@ -157,26 +164,26 @@ def _main_(args):
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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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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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@@ -187,10 +194,10 @@ def _main_(args):
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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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#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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@@ -199,16 +206,16 @@ def _main_(args):
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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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_main_(args)
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@@ -850,7 +850,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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@@ -895,7 +895,82 @@
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"\n",
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"Producing predictions batch-wise: 18%|█▊ | 3/17 [00:59<04:34, 19.60s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 24%|██▎ | 4/17 [01:18<04:08, 19.13s/it]\u001b[A\u001b[A"
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"Producing predictions batch-wise: 24%|██▎ | 4/17 [01:18<04:08, 19.13s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 29%|██▉ | 5/17 [01:36<03:46, 18.91s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 35%|███▌ | 6/17 [01:55<03:29, 19.09s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 41%|████ | 7/17 [02:16<03:16, 19.68s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 47%|████▋ | 8/17 [02:36<02:57, 19.73s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 53%|█████▎ | 9/17 [02:55<02:36, 19.51s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 59%|█████▉ | 10/17 [03:14<02:14, 19.19s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 65%|██████▍ | 11/17 [03:33<01:54, 19.13s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 71%|███████ | 12/17 [03:53<01:37, 19.42s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 76%|███████▋ | 13/17 [04:13<01:18, 19.53s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 82%|████████▏ | 14/17 [04:31<00:57, 19.25s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 88%|████████▊ | 15/17 [04:51<00:38, 19.40s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 94%|█████████▍| 16/17 [05:09<00:19, 19.12s/it]\u001b[A\u001b[A\n",
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"\n",
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"Producing predictions batch-wise: 100%|██████████| 17/17 [05:23<00:00, 17.60s/it]\u001b[A\u001b[A\n",
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"\n",
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" 0%| | 0/13400 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 0%| | 0/13400 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 9%|▉ | 1205/13400 [00:00<00:01, 12042.06it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 18%|█▊ | 2448/13400 [00:00<00:00, 12154.75it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 26%|██▋ | 3536/13400 [00:00<00:00, 11741.99it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 36%|███▌ | 4827/13400 [00:00<00:00, 12069.46it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 45%|████▌ | 6052/13400 [00:00<00:00, 12122.20it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 53%|█████▎ | 7066/13400 [00:00<00:00, 11271.86it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 60%|██████ | 8066/13400 [00:00<00:00, 10481.38it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 68%|██████▊ | 9167/13400 [00:00<00:00, 10634.41it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 76%|███████▋ | 10233/13400 [00:00<00:00, 10641.74it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 84%|████████▍ | 11317/13400 [00:01<00:00, 10698.40it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 93%|█████████▎| 12409/13400 [00:01<00:00, 10763.24it/s]\u001b[A\u001b[A\n",
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"\n",
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"Matching predictions to ground truth, class 1/8.: 100%|██████████| 13400/13400 [00:01<00:00, 10908.63it/s]\u001b[A\u001b[ANo predictions for class 2/8\n",
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"No predictions for class 3/8\n",
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"No predictions for class 4/8\n",
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"No predictions for class 5/8\n",
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"No predictions for class 6/8\n",
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"No predictions for class 7/8\n",
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"No predictions for class 8/8\n",
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"Computing precisions and recalls, class 1/8\n",
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"Computing precisions and recalls, class 2/8\n"
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]
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},
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{
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"ename": "IndexError",
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"evalue": "list index out of range",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-28-7b51ac359b0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0mreturn_recalls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0mreturn_average_precisions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m verbose=True)\n\u001b[0m\u001b[1;32m 87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0mmean_average_precision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage_precisions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprecisions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/Desktop/Rentadrone.cl-ai-test/ssd_keras-master/eval_utils/average_precision_evaluator.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img_height, img_width, batch_size, data_generator_mode, round_confidences, matching_iou_threshold, border_pixels, sorting_algorithm, average_precision_mode, num_recall_points, ignore_neutral_boxes, return_precisions, return_recalls, return_average_precisions, verbose, decoding_confidence_thresh, decoding_iou_threshold, decoding_top_k, decoding_pred_coords, decoding_normalize_coords)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;31m#############################################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_precision_recall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;31m#############################################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/Desktop/Rentadrone.cl-ai-test/ssd_keras-master/eval_utils/average_precision_evaluator.py\u001b[0m in \u001b[0;36mcompute_precision_recall\u001b[0;34m(self, verbose, ret)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Computing precisions and recalls, class {}/{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_classes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 767\u001b[0;31m \u001b[0mtp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumulative_true_positives\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 768\u001b[0m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumulative_false_positives\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mIndexError\u001b[0m: list index out of range"
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||||
]
|
||||
}
|
||||
],
|
||||
|
||||
1
command_train
Normal file
1
command_train
Normal file
@@ -0,0 +1 @@
|
||||
ls images | sed -e 's/\..*$//' > train.txt
|
||||
Reference in New Issue
Block a user