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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() |