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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
from icevision.all import *
import PIL
model1 = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn, num_classes=len(class_map))
state_dict = torch.load('fasterRCNN_resnet18_Raccoons.pth')
model1.load_state_dict(state_dict)
def show_preds(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 = models.torchvision.faster_rcnn.end2end_detect(img, valid_tfms, model1, 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 = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
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")
# Option 1: Get an image from local drive
gr_interface = gr.Interface(fn=show_preds, inputs=["image", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, examples=['raccoon1.jpg','raccoon2.jpg'])
# # Option 2: Grab an image from a webcam
# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)
# # Option 3: Continuous image stream from the webcam
# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)
gr_interface.launch(inline=False, share=False, debug=True) |