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from gradio.outputs import Label
from icevision.all import *
from icevision.models.checkpoint import *
import PIL
import gradio as gr
import os
# Load model
checkpoint_path = "model_checkpoint.pth"
checkpoint_and_model = model_from_checkpoint(checkpoint_path)
model = checkpoint_and_model["model"]
model_type = checkpoint_and_model["model_type"]
class_map = checkpoint_and_model["class_map"]
# Transforms
img_size = checkpoint_and_model["img_size"]
valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
# Populate examples in Gradio interface
examples = [
['1.jpg'],
['2.jpg'],
['3.jpg']
]
def show_preds(input_image):
img = PIL.Image.fromarray(input_image, "RGB")
pred_dict = model_type.end2end_detect(img, valid_tfms, model,
class_map=class_map,
detection_threshold=0.5,
display_label=False,
display_bbox=True,
return_img=True,
font_size=16,
label_color="#FF59D6")
return pred_dict["img"]
gr_interface = gr.Interface(
fn=show_preds,
inputs=["image"],
outputs=[gr.outputs.Image(type="pil", label="RetinaNet Inference")],
title="Aircraft Detector",
examples=examples,
)
gr_interface.launch(inline=False, share=False, debug=True)
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