Update app.py

app.py

@@ -64,6 +64,7 @@ with open(r'labels.txt', 'r') as fp:
 
 colormap = np.asarray(ade_palette())
 
+
 def label_to_color_image(label):
     if label.ndim != 2:
         raise ValueError("Expect 2-D input label")
@@ -71,8 +72,8 @@ def label_to_color_image(label):
     if np.max(label) >= len(colormap):
         raise ValueError("label value too large.")
     return colormap[label]
-    
-def draw_plot(pred_img, seg, show_seg=False):
+
+def draw_plot(pred_img, seg):
     fig = plt.figure(figsize=(20, 15))
 
     grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
@@ -80,8 +81,53 @@ def draw_plot(pred_img, seg, show_seg=False):
     plt.subplot(grid_spec[0])
     plt.imshow(pred_img)
     plt.axis('off')
+    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)
+
+    inputs = feature_extractor(images=input_img, return_tensors="tf")
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+    logits = tf.transpose(logits, [0, 2, 3, 1])
+    logits = tf.image.resize(
+        logits, input_img.size[::-1]
+    )  # We reverse the shape of `image` because `image.size` returns width and height.
+    seg = tf.math.argmax(logits, axis=-1)[0]
+
+    color_seg = np.zeros(
+        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
+    )  # height, width, 3
+    for label, color in enumerate(colormap):
+        color_seg[seg.numpy() == label, :] = color
+
+    # Show image + mask
+    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
+    pred_img = pred_img.astype(np.uint8)
+
+    fig = draw_plot(pred_img, seg)
+    return fig
+
+demo = gr.Interface(fn=sepia,
+                    inputs=gr.Image(shape=(400, 600)),
+                    outputs=['plot'],
+                    examples=["side-1.jpg", "side-2.jpg", "side-3.jpg", "side-4.jpg"],
+                    allow_flagging='never')
+
+
+demo.launch()
 
-    if show_seg:
-        unique_labels = np.unique(seg.numpy().astype("uint8"))
-        ax = plt.subplot(grid_spec[1])