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| import subprocess | |
| import sys | |
| print("Reinstalling mmcv") | |
| subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "-y", "mmcv-full==1.3.17"]) | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "mmcv-full==1.3.17", "-f", "https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html"]) | |
| print("mmcv install complete") | |
| #from icevision.models import * | |
| from icevision.models.checkpoint import * | |
| from icevision.all import * | |
| from icevision.models import mmdet | |
| #import icedata | |
| import PIL | |
| import requests | |
| import torch | |
| from torchvision import transforms | |
| import cv2 | |
| import gradio as gr | |
| classes = ['Army_navy', 'Bulldog', 'Castroviejo', 'Forceps', 'Frazier', 'Hemostat', 'Iris', | |
| 'Mayo_metz', 'Needle', 'Potts', 'Richardson', 'Scalpel', 'Towel_clip', 'Weitlaner', 'Yankauer'] | |
| class_map = ClassMap(classes) | |
| metrics = [COCOMetric(metric_type=COCOMetricType.bbox)] | |
| model_type = models.mmdet.vfnet | |
| backbone = model_type.backbones.resnet50_fpn_mstrain_2x | |
| checkpoint_path = 'VFNet_teacher_nov29_mAP82.6.pth' | |
| checkpoint_and_model = model_from_checkpoint(checkpoint_path) | |
| model_loaded = checkpoint_and_model["model"] | |
| img_size = checkpoint_and_model["img_size"] | |
| valid_tfms = tfms.A.Adapter( | |
| [*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()]) | |
| # examples | |
| #for root, dirs, files in os.walk(r'sample_images/'): | |
| # for filename in files: | |
| # print(filename) | |
| #examples = ["sample_images/"+file for file in files] | |
| description1 = 'Tool for detecting 15 classes of surgical instruments: Scalpel, Forceps, Suture needle, Clamps (Hemostat, Towel clip, Bulldog), Scissors (Mayo_metz, Iris, Potts), Needle holder (Castroviejo), Retractors (Army-navy, Richardson, Weitlaner), Suctions (Yankauer, Frazier).' | |
| description2 = '\n \n Choose one of the examples below or use your own image of an instrument. Click on the Submit button, allow for model prediction and see the bounding box and/or label result.' | |
| examples=[['Image00001.jpg'],['Image00002.jpg'],['Image00003.jpg'],['Image00004.jpg'],['Image00005.jpg']] | |
| def show_preds_gradio(input_image, display_label, display_bbox, detection_threshold): | |
| if detection_threshold == 0: | |
| detection_threshold = 0.5 | |
| img = PIL.Image.fromarray(input_image, 'RGB') | |
| pred_dict = model_type.end2end_detect(img, valid_tfms, model_loaded, class_map=class_map, detection_threshold=detection_threshold, | |
| display_label=display_label, display_bbox=display_bbox, return_img=True, | |
| font_size=16, label_color="#FF59D6") | |
| return pred_dict['img'] | |
| display_chkbox_label = gr.inputs.Checkbox(label="Label", default=True) | |
| display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True) | |
| detection_threshold_slider = gr.inputs.Slider( | |
| minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold") | |
| outputs = gr.outputs.Image(type="pil") | |
| gr_interface = gr.Interface(fn=show_preds_gradio, inputs=["image", display_chkbox_label, display_chkbox_box, detection_threshold_slider], | |
| outputs=outputs, title='Surgical Instrument Detection and Identification Tool', # , article=article, | |
| description = [description1,description2], | |
| examples=examples, | |
| enable_queue=True) ## | |
| gr_interface.launch(inline=False, share=True, debug=True) |