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 = """ """ 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}")