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import streamlit as st
from PIL import Image
from matplotlib.pyplot import axis
import requests
import numpy as np
from torch import nn
import requests
import torch
import detectron2
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.utils.visualizer import ColorMode
damage_model_path = 'model_final_damage.pth'
scratch_model_path = 'model_final_scratch.pth'
parts_model_path = 'model_final_parts.pth'
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
cfg_scratches = get_cfg()
cfg_scratches.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg_scratches.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8
cfg_scratches.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg_scratches.MODEL.WEIGHTS = scratch_model_path
cfg_scratches.MODEL.DEVICE = device
predictor_scratches = DefaultPredictor(cfg_scratches)
metadata_scratch = MetadataCatalog.get("car_dataset_val")
metadata_scratch.thing_classes = ["scratch"]
cfg_damage = get_cfg()
cfg_damage.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg_damage.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7
cfg_damage.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg_damage.MODEL.WEIGHTS = damage_model_path
cfg_damage.MODEL.DEVICE = device
predictor_damage = DefaultPredictor(cfg_damage)
metadata_damage = MetadataCatalog.get("car_damage_dataset_val")
metadata_damage.thing_classes = ["damage"]
cfg_parts = get_cfg()
cfg_parts.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg_parts.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.75
cfg_parts.MODEL.ROI_HEADS.NUM_CLASSES = 19
cfg_parts.MODEL.WEIGHTS = parts_model_path
cfg_parts.MODEL.DEVICE = device
predictor_parts = DefaultPredictor(cfg_parts)
metadata_parts = MetadataCatalog.get("car_parts_dataset_val")
metadata_parts.thing_classes = ['_background_',
'back_bumper',
'back_glass',
'back_left_door',
'back_left_light',
'back_right_door',
'back_right_light',
'front_bumper',
'front_glass',
'front_left_door',
'front_left_light',
'front_right_door',
'front_right_light',
'hood',
'left_mirror',
'right_mirror',
'tailgate',
'trunk',
'wheel']
def merge_segment(pred_segm):
merge_dict = {}
for i in range(len(pred_segm)):
merge_dict[i] = []
for j in range(i+1,len(pred_segm)):
if torch.sum(pred_segm[i]*pred_segm[j])>0:
merge_dict[i].append(j)
to_delete = []
for key in merge_dict:
for element in merge_dict[key]:
to_delete.append(element)
for element in to_delete:
merge_dict.pop(element,None)
empty_delete = []
for key in merge_dict:
if merge_dict[key] == []:
empty_delete.append(key)
for element in empty_delete:
merge_dict.pop(element,None)
for key in merge_dict:
for element in merge_dict[key]:
pred_segm[key]+=pred_segm[element]
except_elem = list(set(to_delete))
new_indexes = list(range(len(pred_segm)))
for elem in except_elem:
new_indexes.remove(elem)
return pred_segm[new_indexes]
def inference(image):
img = np.array(image)
outputs_damage = predictor_damage(img)
outputs_parts = predictor_parts(img)
outputs_scratch = predictor_scratches(img)
out_dict = outputs_damage["instances"].to("cpu").get_fields()
merged_damage_masks = merge_segment(out_dict['pred_masks'])
scratch_data = outputs_scratch["instances"].get_fields()
scratch_masks = scratch_data['pred_masks']
damage_data = outputs_damage["instances"].get_fields()
damage_masks = damage_data['pred_masks']
parts_data = outputs_parts["instances"].get_fields()
parts_masks = parts_data['pred_masks']
parts_classes = parts_data['pred_classes']
new_inst = detectron2.structures.Instances((1024,1024))
new_inst.set('pred_masks',merge_segment(out_dict['pred_masks']))
parts_damage_dict = {}
parts_list_damages = []
for part in parts_classes:
parts_damage_dict[metadata_parts.thing_classes[part]] = []
for mask in scratch_masks:
for i in range(len(parts_masks)):
if torch.sum(parts_masks[i]*mask)>0:
parts_damage_dict[metadata_parts.thing_classes[parts_classes[i]]].append('scratch')
parts_list_damages.append(f'{metadata_parts.thing_classes[parts_classes[i]]} has scratch')
print(f'{metadata_parts.thing_classes[parts_classes[i]]} has scratch')
for mask in merged_damage_masks:
for i in range(len(parts_masks)):
if torch.sum(parts_masks[i]*mask)>0:
parts_damage_dict[metadata_parts.thing_classes[parts_classes[i]]].append('damage')
parts_list_damages.append(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
print(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
v_d = Visualizer(img[:, :, ::-1],
metadata=metadata_damage,
scale=0.5,
instance_mode=ColorMode.SEGMENTATION # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
#v_d = Visualizer(img,scale=1.2)
#print(outputs["instances"].to('cpu'))
out_d = v_d.draw_instance_predictions(new_inst)
img1 = out_d.get_image()[:, :, ::-1]
v_s = Visualizer(img[:, :, ::-1],
metadata=metadata_scratch,
scale=0.5,
instance_mode=ColorMode.SEGMENTATION # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
#v_s = Visualizer(img,scale=1.2)
out_s = v_s.draw_instance_predictions(outputs_scratch["instances"])
img2 = out_s.get_image()[:, :, ::-1]
v_p = Visualizer(img[:, :, ::-1],
metadata=metadata_parts,
scale=0.5,
instance_mode=ColorMode.SEGMENTATION # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
#v_p = Visualizer(img,scale=1.2)
out_p = v_p.draw_instance_predictions(outputs_parts["instances"])
img3 = out_p.get_image()[:, :, ::-1]
return img1, img2, img3, parts_list_damages
def main():
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
with st.sidebar:
image = Image.open('itaca_logo.png')
st.image(image, width=150) #,use_column_width=True)
page = option_menu(menu_title='Menu',
menu_icon="robot",
options=["Car Parts Detection",
"Under Construction"],
icons=["chat-dts",
"key"],
default_index=0
)
# Additional section below the option menu
# st.markdown("---") # Add a separator line
st.header("Settings")
st.title('ITACA Insurance Core AI Module')
if page == "Car Parts Detection":
st.header('Car Parts Detection')
st.write(
"""
"""
)
uploaded_file = st.file_uploader("Upload an image:")
# Check if a file has been uploaded
if uploaded_file is not None:
# Load and display the image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded image")
else:
st.write("Please upload an image.")
if st.button("Prediction"):
# Call the inference function with the uploaded image
imagen1, imagen2, imagen3, partes = inference(image)
st.image(imagen1, caption="crash image1")
st.image(imagen2, caption="crash image2")
st.image(imagen3, caption="crash image3")
st.write(partes)
print(partes)
elif page == "Under Construction":
st.header('Under Construction')
st.write(
"""
"""
)
try:
main()
except Exception as e:
st.sidebar.error(f"An error occurred: {e}") |