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import icevision
from icevision.all import *
import icedata
import PIL
import torch
from torchvision import transforms
import gradio as gr
from icevision.models.checkpoint import *
model_type = models.mmdet.retinanet ##
backbone = model_type.backbones.resnet50_fpn_1x##
model = torch.hub.load('~/Download/Model_M_set11_ret_nov8_map63.6.pth', source = 'local')
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")
# Option 1: Get an image from local drive
gr_interface = gr.Interface(fn=show_preds_gradio, inputs=["image", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO')
gr_interface.launch(inline=False, share=True, debug=True)
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