Create app_v1.txt

app_v1.txt

@@ -0,0 +1,255 @@
+try:
+    import detectron2
+except:
+    import os 
+    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
+
+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
+from annotated_text import annotated_text
+from streamlit_option_menu import option_menu
+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=["Damage Detection",
+                                    "Under Construction"],
+                        icons=["camera",
+                                "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 == "Damage Detection":
+        st.header('Car Parts Damage 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"):
+            with st.spinner("Loading..."):
+                # 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.table(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}")