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import gradio as gr
import subprocess
import json
import os
from PIL import Image
darknetpath = os.path.join(os.path.dirname(__file__), "darknet")
darknet_executable = os.path.join(darknetpath, "darknet")
modelpath = os.path.join(os.path.dirname(__file__), "models")
models = {}
modellist = []
def init():
subprocess.run(
"make",
cwd=darknetpath,
)
# subprocess.run(
# "git lfs install",
# cwd=modelpath,
# )
# subprocess.run(
# "git lfs pull",
# cwd=modelpath,
# )
global models
models = json.load(open(os.path.join(modelpath, "path.json")))
global modellist
modellist = list(models.keys())
def darknet_command(model, img, thresh=0.25):
return [
darknet_executable,
"detector",
"test",
model["data"],
model["cfg"],
model["weights"],
img,
"-thresh",
str(thresh),
]
def predict(model, img):
input_path = os.path.join(modelpath, "input.jpg")
output_path = os.path.join(modelpath, "predictions.jpg")
img.save(input_path)
model = models[model]["640x640"]
command = darknet_command(model, input_path)
subprocess.run(command, cwd=modelpath)
return Image.open(output_path)
if __name__ == "__main__":
init()
iface = gr.Interface(
predict,
inputs=[
gr.Dropdown(modellist, label="Model"),
gr.Image(type="pil", label="Input Image"),
],
outputs=gr.Image(type="pil", label="Output Image"),
title="Yolo-lightnet",
description="Yolo-lightnet is a lightweight version of Yolo. It removes the heavy layers of Yolo and replaces them with lightweight layers. This makes it faster and more efficient.",
# examples=[
# [
# "driving",
# "car.jpg",
# ],
# [
# "head_body",
# "human.jpg",
# ],
# ],
)
iface.launch()
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