Update app.py

app.py

@@ -1,5 +1,8 @@
 import gradio as gr
 
+import pandas as pd
+from matplotlib import gridspec
+import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
 import tensorflow as tf
@@ -167,6 +170,58 @@ def ade_palette():
         [92, 0, 255],
     ]
 
+def label_to_color_image(label):
+    """Adds color defined by the dataset colormap to the label.
+
+  Args:
+    label: A 2D array with integer type, storing the segmentation label.
+
+  Returns:
+    result: A 2D array with floating type. The element of the array
+      is the color indexed by the corresponding element in the input label
+      to the PASCAL color map.
+
+  Raises:
+    ValueError: If label is not of rank 2 or its value is larger than color
+      map maximum entry.
+  """
+    if label.ndim != 2:
+        raise ValueError("Expect 2-D input label")
+
+    colormap = np.asarray(ade_palette())
+
+    if np.max(label) >= len(colormap):
+        raise ValueError("label value too large.")
+
+    return colormap[label]
+
+def draw_plot(pred_img, seg):
+    fig = plt.figure(figsize=(20, 15))
+
+    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
+
+    plt.subplot(grid_spec[0])
+    plt.imshow(pred_img)
+    plt.axis('off')
+
+    ade20k_labels_info = pd.read_csv(
+        "https://raw.githubusercontent.com/CSAILVision/sceneparsing/master/objectInfo150.csv"
+    )
+    labels_list = list(ade20k_labels_info["Name"])    
+
+    LABEL_NAMES = np.asarray(labels_list)
+    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
+    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
+
+    unique_labels = np.unique(seg.numpy().astype("uint8"))
+    ax = plt.subplot(grid_spec[1])
+    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
+    ax.yaxis.tick_right()
+    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
+    plt.xticks([], [])
+    ax.tick_params(width=0.0, labelsize=25)
+    return fig
+
 def sepia(input_img):
     input_img = Image.fromarray(input_img)
 
@@ -194,8 +249,10 @@ def sepia(input_img):
     # Show image + mask
     pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
     pred_img = pred_img.astype(np.uint8)    
-    return pred_img
 
-demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image", examples=["ADE_val_00000001.jpeg"])
+    fig = draw_plot(pred_img, seg)
+    return fig
+
+demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), outputs=['plot'], examples=["ADE_val_00000001.jpeg"])
 
 demo.launch()