Create car_damage_detection.py

car_damage_detection.py

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+
+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
+
+model_path = 'model_final.pth'
+
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.6
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
+cfg.MODEL.WEIGHTS = model_path
+
+if not torch.cuda.is_available():
+    cfg.MODEL.DEVICE='cpu'
+
+predictor = DefaultPredictor(cfg)
+my_metadata = MetadataCatalog.get("car_dataset_val")
+my_metadata.thing_classes = ["damage"]
+
+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):
+    print(image.height)
+
+    height = image.height
+
+    # img = np.array(image.resize((500, height)))
+    img = np.array(image)
+    outputs = predictor(img)
+    out_dict = outputs["instances"].to("cpu").get_fields()
+    new_inst = detectron2.structures.Instances((1024,1024))
+    new_inst.set('pred_masks',merge_segment(out_dict['pred_masks']))
+    v = Visualizer(img[:, :, ::-1],
+                   metadata=my_metadata, 
+                   scale=0.5, 
+                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
+    )
+    # v = Visualizer(img,scale=1.2)
+    #print(outputs["instances"].to('cpu'))
+    out = v.draw_instance_predictions(new_inst)
+    
+    return out.get_image()[:, :, ::-1]
+    
+title = "Detectron2 Car damage Detection"
+description = "This demo introduces an interactive playground for our trained Detectron2 model."
+
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input")], 
+    gr.outputs.Image(type="numpy", label="Output"),
+    title=title,
+    description=description,
+    examples=[]).launch()