finish

app.py

@@ -48,6 +48,7 @@ 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")
@@ -57,26 +58,14 @@ def label_to_color_image(label):
     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])
+def draw_class_visualization(seg, class_id):
+    class_mask = seg.numpy() == class_id
+    class_color = colormap[class_id]
 
-    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)
+    class_visualization = np.zeros(seg.shape + (3,))
+    class_visualization[class_mask] = class_color
 
-    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
+    return class_visualization
 
 def sepia(input_img):
     input_img = Image.fromarray(input_img)
@@ -88,43 +77,31 @@ def sepia(input_img):
     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)
+    class_visualizations = []
+    for class_id in range(len(colormap)):
+        class_visualization = draw_class_visualization(seg, class_id)
+        class_visualizations.append(class_visualization)
 
-    fig = draw_plot(pred_img, seg)
-    return fig
+    return class_visualizations
 
 
-def segment_image(input_img):
-    input_img = Image.fromarray(input_img)
-
-    # 세그멘테이션 수행
-    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]
-    )
-    seg = tf.math.argmax(logits, axis=-1)[0]
+def plot_class_visualization(class_visualizations):
+    fig, axes = plt.subplots(1, len(class_visualizations), figsize=(20, 15))
 
-    return input_img, seg
+    for i, class_visualization in enumerate(class_visualizations):
+        ax = axes[i]
+        ax.imshow(class_visualization)
+        ax.axis('off')
+        ax.set_title(labels_list[i])
+        return fig
 
 
-demo = gr.Interface(fn=segment_image,
+demo = gr.Interface(fn=sepia,
                     inputs=gr.Image(shape=(1024, 1024)),
-                    outputs=["image", "image"],
+                    outputs=gr.outputs.Image(type='plot', label="Class Visualizations"),
                     examples=["city-1.jpg", "city-2.jpg", "city-3.jpg", "city-4.jpg", "city-5.jpg"],
                     allow_flagging='never')