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import gradio as gr
#from florence import model_init, draw_image
#from wikai import analyze_dial, ocr_and_od
import matplotlib.pyplot as plt
from aimodel import model_init, read_meter, visualization
from test_rect import read_meter as read_meter_rect
from PIL import Image
import logging
#logging.basicConfig(level=logging.DEBUG)
print("Loading model...")
fl, fl_ft = model_init(hack=False)
def process_image(input_image:Image, meter_type:str):
if meter_type == "方形儀表":
value, img = read_meter_rect(input_image, fl, fl_ft)
return img, f"辨識結果: PA={value}", None
assert meter_type == "圓形儀表"
plt.clf()
print("process_image")
W, H = 640, 480
if input_image is None:
return None, None
meter_result = read_meter(input_image, fl, fl_ft)
img, fig = visualization(meter_result)
return img, f"辨識結果: PSI={meter_result.needle_psi:.1f} kg/cm²={meter_result.needle_kg_cm2:.2f} ", plt
with gr.Blocks() as demo:
gr.Markdown("## 指針辨識系統\n請選擇儀表類型，上傳圖片，或點擊Submit")
with gr.Row():
with gr.Column():
with gr.Row():
clear_button = gr.ClearButton()
submit_button = gr.Button("Submit", variant="primary")
meter_type_dropdown = gr.Dropdown(choices=["圓形儀表", "方形儀表"], label="選擇選項")
image_input = gr.Image(type="pil", label="上傳圖片")
with gr.Column():
number_output = gr.Textbox(label="辨識結果", placeholder="辨識結果")
image_output = gr.Image(label="輸出圖片")
plot_output = gr.Plot(label="模型結果")
clear_button.add([image_input, image_output, number_output])
image_input.upload(
fn=process_image,
inputs=[image_input, meter_type_dropdown],
outputs=[image_output, number_output, plot_output],
queue=False
)
submit_button.click(
fn=process_image,
inputs=[image_input, meter_type_dropdown],
outputs=[image_output, number_output, plot_output],
)
demo.launch(debug=True)
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