Update app.py

app.py

@@ -1,43 +1,32 @@
 import torch
 from transformers import pipeline
-
 from PIL import Image
-
 import matplotlib.pyplot as plt
-
-from random import choice
 import io
 
 detector50 = pipeline(model="TuningAI/DETR-BASE_Marine")
 
 import gradio as gr
 
-COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
- "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
- "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
-
 fdic = {
- "family" : "Impact",
  "style" : "italic",
  "size" : 10,
  "color" : "red",
  "weight" : "bold"
 }
-
-
+labels_ = { "LABEL_0":"None" , "LABEL_1": "Boat" ,"LABEL_2": "Car" ,"LABEL_3" : "Dock" , "LABEL_4" : "Jetski" ,"LABEL_5" : "Lift"}
 def get_figure(in_pil_img, in_results):
  plt.figure(figsize=(16, 10))
  plt.imshow(in_pil_img)
  ax = plt.gca()
 
  for prediction in in_results:
- selected_color = choice(COLORS)
+ selected_color ="#008000"
 
  x, y = prediction['box']['xmin'], prediction['box']['ymin'],
  w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
-
  ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
- ax.text(x, y, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
+ ax.text(x, y, f"{labels_[prediction['label']]}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
 
  plt.axis("off")
 
@@ -61,6 +50,6 @@ with gr.Blocks(title="DETR Object Detection") as demo:
  with gr.Row():
  input_image = gr.Image(label="Input image", type="pil")
  output_image = gr.Image(label="Output image with predicted instances", type="pil")
- send_btn = gr.Button("Infer")
+ send_btn = gr.Button("start")
  send_btn.click(fn=infer, inputs=input_image, outputs=[output_image])
 demo.launch(debug=True)