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af3fe62 3ad9cb6 af3fe62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | import gradio as gr
import numpy as np
import pandas as pd
# our own helper tools
import clustering
import utils
import logging
logging.getLogger().setLevel(logging.INFO)
from tensorflow import keras
#image_threshold = 20
damage_sites = {}
model1_windowsize = [250,250]
#model1_threshold = 0.7
model1 = keras.models.load_model('rwthmaterials_dp800_network1_inclusion.h5')
model1.compile()
damage_classes = {3: "Martensite",2: "Interface",0:"Notch",1:"Shadowing"}
model2_windowsize = [100,100]
#model2_threshold = 0.5
model2 = keras.models.load_model('rwthmaterials_dp800_network2_damage.h5')
model2.compile()
##
## Function to do the actual damage classification
##
def damage_classification(SEM_image,image_threshold, model1_threshold, model2_threshold):
##
## clustering
##
logging.debug('---------------: clustering :=====================')
all_centroids = clustering.get_centroids(SEM_image, image_threshold=image_threshold,
fill_holes=True, filter_close_centroids=True)
for i in range(len(all_centroids)) :
key = (all_centroids[i][0],all_centroids[i][1])
damage_sites[key] = 'Not Classified'
##
## Inclusions vs the rest
##
logging.debug('---------------: prepare model 1 :=====================')
images_model1 = utils.prepare_classifier_input(SEM_image, all_centroids, window_size=model1_windowsize)
logging.debug('---------------: run model 1 :=====================')
y1_pred = model1.predict(np.asarray(images_model1, float))
logging.debug('---------------: model1 threshold :=====================')
inclusions = y1_pred[:,0].reshape(len(y1_pred),1)
inclusions = np.where(inclusions > model1_threshold)
logging.debug('---------------: model 1 update dict :=====================')
for i in range(len(inclusions[0])):
centroid_id = inclusions[0][i]
coordinates = all_centroids[centroid_id]
key = (coordinates[0], coordinates[1])
damage_sites[key] = 'Inclusion'
logging.debug('Damage sites after model 1')
logging.debug(damage_sites)
##
## Martensite cracking, etc
##
logging.debug('---------------: prepare model 2 :=====================')
centroids_model2 = []
for key, value in damage_sites.items():
if value == 'Not Classified':
coordinates = list([key[0],key[1]])
centroids_model2.append(coordinates)
logging.debug('Centroids model 2')
logging.debug(centroids_model2)
logging.debug('---------------: prepare model 2 :=====================')
images_model2 = utils.prepare_classifier_input(SEM_image, centroids_model2, window_size=model2_windowsize)
logging.debug('Images model 2')
logging.debug(images_model2)
logging.debug('---------------: run model 2 :=====================')
y2_pred = model2.predict(np.asarray(images_model2, float))
damage_index = np.asarray(y2_pred > model2_threshold).nonzero()
for i in range(len(damage_index[0])):
index = damage_index[0][i]
identified_class = damage_index[1][i]
label = damage_classes[identified_class]
coordinates = centroids_model2[index]
#print('Damage {} \t identified as {}, \t coordinates {}'.format(i, label, coordinates))
key = (coordinates[0], coordinates[1])
damage_sites[key] = label
##
## show the damage sites on the image
##
logging.debug("-----------------: final damage sites :=================")
logging.debug(damage_sites)
image_path = 'classified_damage_sites.png'
image = utils.show_boxes(SEM_image, damage_sites,
save_image=True,
image_path=image_path)
##
## export data
##
csv_path = 'classified_damage_sites.csv'
cols = ['x', 'y', 'damage_type']
data = []
for key, value in damage_sites.items():
data.append([key[0], key[1], value])
df = pd.DataFrame(columns=cols, data=data)
df.to_csv(csv_path)
return image, image_path, csv_path
## ---------------------------------------------------------------------------------------------------------------
## main app interface
## -----------------------------------------------------------------------------------------------------------------
with gr.Blocks() as app:
gr.Markdown('# Damage Classification in Dual Phase Steels')
gr.Markdown('This app classifies damage types in dual phase steels. Two models are used. The first model is used to identify inclusions in the steel. The second model is used to identify the remaining damage types: Martensite cracking, Interface Decohesion, Notch effect and Shadows.')
gr.Markdown('If you use this app, kindly cite the following papers:')
gr.Markdown('Kusche, C., Reclik, T., Freund, M., Al-Samman, T., Kerzel, U., & Korte-Kerzel, S. (2019). Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning. PloS one, 14(5), e0216493. [Link](https://doi.org/10.1371/journal.pone.0216493)')
gr.Markdown('Medghalchi, S., Kusche, C. F., Karimi, E., Kerzel, U., & Korte-Kerzel, S. (2020). Damage analysis in dual-phase steel using deep learning: transfer from uniaxial to biaxial straining conditions by image data augmentation. Jom, 72, 4420-4430. [Link](https://link.springer.com/article/10.1007/s11837-020-04404-0)')
image_input = gr.Image()
with gr.Row():
cluster_threshold_input = gr.Number(label='Cluster Threshold', value = 20,
info='Grayscale value at which a pixel is attributed to a potential damage site')
model1_threshold_input = gr.Number(label='Model 1 Threshold', value = 0.7, info='Threshold for the model identifying inclusions')
model2_threshold_input = gr.Number(label='Model 2 Threshold', value = 0.5, info='Thrshold for the model identifying the remaining damage types')
button = gr.Button("Classify")
output_image = gr.Image()
with gr.Row():
download_image = gr.DownloadButton(label='Download Image')
download_csv = gr.DownloadButton(label='Download Damage List')
button.click(damage_classification,
inputs=[image_input, cluster_threshold_input, model1_threshold_input, model2_threshold_input],
outputs=[output_image, download_image, download_csv])
# simple interface, no title, etc
# app = gr.Interface(damage_classification,
# inputs=[gr.Image(),
# gr.Number(label='Cluster Threshold', value = 20, info='Grayscale value at which a pixel is attributed to a potential damage site'),
# gr.Number(label='Model 1 Threshold', value = 0.7, info='Threshold for the model identifying inclusions'),
# gr.Number(label='Model 2 Threshold', value = 0.5, info='Thrshold for the model identifying the remaining damage types')
# ],
# outputs=[gr.Image(),
# gr.DownloadButton(label='Download Image'),
# gr.DownloadButton(label='Download Damage List')])
if __name__ == "__main__":
app.launch()
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