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import cv2 as cv
import numpy as np
import gradio as gr
from mobilenet import MobileNet
from huggingface_hub import hf_hub_download
# Download ONNX model from Hugging Face
model_path = hf_hub_download(repo_id="opencv/image_classification_mobilenet", filename="image_classification_mobilenetv1_2022apr.onnx")
top_k = 1
backend_id = cv.dnn.DNN_BACKEND_OPENCV
target_id = cv.dnn.DNN_TARGET_CPU
# Load MobileNet model
model = MobileNet(modelPath=model_path, topK=top_k, backendId=backend_id, targetId=target_id)
def classify_image(input_image):
image = cv.resize(input_image, (256, 256))
image = image[16:240, 16:240, :]
result = model.infer(image)
result_str = "\n".join(f"{label}" for label in result)
return result_str
def clear_output_on_change(img):
return gr.update(value="")
def clear_all():
return None, None
with gr.Blocks(css='''.example * {
font-style: italic;
font-size: 18px !important;
color: #0ea5e9 !important;
}''') as demo:
gr.Markdown("### Image Classification with MobileNet (OpenCV DNN)")
gr.Markdown("Upload an image to classify using a MobileNet model loaded with OpenCV DNN.")
with gr.Row():
image_input = gr.Image(type="numpy", label="Upload Image")
output_box = gr.Textbox(label="Top Prediction(s)")
# Clear output when new image is uploaded
image_input.change(fn=clear_output_on_change, inputs=image_input, outputs=output_box)
with gr.Row():
submit_btn = gr.Button("Submit", variant="primary")
clear_btn = gr.Button("Clear")
submit_btn.click(fn=classify_image, inputs=image_input, outputs=output_box)
clear_btn.click(fn=clear_all, outputs=[image_input, output_box])
gr.Markdown("Click on any example to try it.", elem_classes=["example"])
gr.Examples(
examples=[
["examples/squirrel_cls.jpg"],
["examples/baboon.jpg"]
],
inputs=image_input
)
if __name__ == "__main__":
demo.launch()
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