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] description = '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).' examples=[['Image00001.jpg'],['Image00002.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 = description, examples=examples, enable_queue=True) ## gr_interface.launch(inline=False, share=True, debug=True)