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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
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() |