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import gradio as gr
import os
from gradio_app.config import setup_logging, setup_sys_path
from gradio_app.processor import gradio_process, update_preview, update_visibility
# Initialize logging and sys.path
setup_logging()
setup_sys_path()
# Load custom CSS
custom_css = open(os.path.join(os.path.dirname(__file__), "gradio_app", "static", "styles.css"), "r").read()
# Path to examples directory
examples_dir = os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr")
# Collect example files
examples = [
{
"Input File": os.path.join(examples_dir, "1", "lp_image.jpg"),
"Output File": os.path.join(examples_dir, "1", "lp_image_output.jpg"),
"Input Type": "Image"
},
{
"Input File": os.path.join(examples_dir, "2", "lp_video.mp4"),
"Output File": os.path.join(examples_dir, "2", "lp_video_output.mp4"),
"Input Type": "Video"
}
]
# Function to handle example selection
def select_example(input_file, input_type):
return input_file, input_type, None, None, None # Reset outputs
# Gradio Interface
with gr.Blocks(css=custom_css) as iface:
gr.Markdown(
"""
# License Plate Detection and OCR
Detect license plates from images or videos and read their text using
advanced computer vision and OCR for accurate identification.
""",
elem_classes="markdown-title"
)
with gr.Row():
with gr.Column(scale=1):
input_file = gr.File(label="Upload Image or Video", elem_classes="custom-file-input")
input_type = gr.Radio(choices=["Image", "Video"], label="Input Type", value="Image", elem_classes="custom-radio")
with gr.Blocks():
input_preview_image = gr.Image(label="Input Preview", visible=True, elem_classes="custom-image")
input_preview_video = gr.Video(label="Input Preview", visible=False, elem_classes="custom-video")
with gr.Row():
clear_button = gr.Button("Clear", variant="secondary", elem_classes="custom-button secondary")
submit_button = gr.Button("Submit", variant="primary", elem_classes="custom-button primary")
# Examples table
gr.Markdown("### Examples")
examples_table = gr.Dataframe(
value=[[ex["Input File"], ex["Output File"], ex["Input Type"]] for ex in examples],
headers=["Input File", "Output File", "Input Type"],
interactive=False,
elem_classes="custom-table"
)
examples_table.click(
fn=select_example,
inputs=[examples_table, examples_table],
outputs=[input_file, input_type, output_image, output_video, output_text]
)
with gr.Column(scale=2):
with gr.Blocks():
output_image = gr.Image(label="Processed Output (Image)", type="numpy", visible=True, elem_classes="custom-image")
output_video = gr.Video(label="Processed Output (Video)", visible=False, elem_classes="custom-video")
output_text = gr.Textbox(label="Detected License Plates", lines=10, elem_classes="custom-textbox")
# Update preview and output visibility when input type changes
input_type.change(
fn=update_visibility,
inputs=input_type,
outputs=[input_preview_image, input_preview_video, output_image, output_video]
)
# Update preview when file is uploaded
input_file.change(
fn=update_preview,
inputs=[input_file, input_type],
outputs=[input_preview_image, input_preview_video]
)
# Bind the processing function
submit_button.click(
fn=gradio_process,
inputs=[input_file, input_type],
outputs=[output_image, output_video, output_text, input_preview_image, input_preview_video]
)
# Clear button functionality
clear_button.click(
fn=lambda: (None, None, None, "Image", None, None, None, None),
outputs=[input_file, output_image, output_video, input_type, input_preview_image, input_preview_video, output_image, output_video]
)
if __name__ == "__main__":
iface.launch(share=True) |