Upload app.py

app.py

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+#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()])
+    
+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, examples=examples)
+
+
+gr_interface.launch(inline=False, share=True, debug=True)