Update code.txt

code.txt

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+try:
+    import detectron2
+except:
+    import os 
+    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
+
+from matplotlib.pyplot import axis
+import gradio as gr
+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 = 'damage/model_final.pth'
+scratch_model_path = 'scratch/model_final.pth'
+parts_model_path = 'parts/model_final.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
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Inputs")
+            image = gr.Image(type="pil",label="Input")
+            submit_button = gr.Button(value="Submit", label="Submit")
+        with gr.Column():
+            gr.Markdown("## Outputs")
+            with gr.Tab('Image of damages'):
+                im1 = gr.Image(type='numpy',label='Image of damages')
+            with gr.Tab('Image of scratches'):
+                im2 = gr.Image(type='numpy',label='Image of scratches')
+            with gr.Tab('Image of parts'):
+                im3 = gr.Image(type='numpy',label='Image of car parts')
+            with gr.Tab('Information about damaged parts'):
+                intersections = gr.Textbox(label='Information about type of damages on each part')
+    
+    #actions
+    submit_button.click(
+        fn=inference,
+        inputs = [image],
+        outputs = [im1,im2,im3,intersections]
+    )
+        
+if __name__ == "__main__":
+    demo.launch()