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import gradio as gr
import json
import numpy as np
import datasets
bugs_ds = datasets.load_dataset("asgaardlab/SampleDataset", split="validation")
def generate_annotations(image_index):
image_index = int(image_index)
objects_json = bugs_ds[image_index]["Objects JSON"]
objects = json.loads(objects_json)
segmentation_image_rgb = bugs_ds[image_index]["Segmentation Image"]
segmentation_image_rgb = np.array(segmentation_image_rgb)
annotations = []
for obj in objects:
color = tuple(obj["color"].values())[:-1]
mask = np.all(segmentation_image_rgb == np.array(color), axis=-1).astype(
np.float32
)
annotations.append((mask, obj["name"]))
object_count = bugs_ds[image_index]["Object Count"]
victim_name = bugs_ds[image_index]["Victim Name"]
bug_type = bugs_ds[image_index]["Tag"]
return (
(bugs_ds[image_index]["Correct Image"], annotations),
objects,
object_count,
victim_name,
bug_type,
)
# Setting up the Gradio interface using blocks API
with gr.Blocks() as demo:
gr.Markdown(
"Enter the image index and click **Submit** to view the segmentation annotations."
)
with gr.Row():
inp = gr.Slider(
minimum=0, maximum=len(bugs_ds) - 1, step=1, label="Image Index"
)
btn = gr.Button("Submit")
with gr.Row():
with gr.Column():
object_count = gr.Number(label="Object Count")
victim_name = gr.Textbox(label="Victim Name")
bug_type = gr.Textbox(label="Bug Type")
seg_img = gr.AnnotatedImage()
with gr.Row():
json_data = gr.JSON()
btn.click(
fn=generate_annotations,
inputs=inp,
outputs=[seg_img, json_data, object_count, victim_name, bug_type],
)
demo.launch()
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