Update app.py

app.py

@@ -1,18 +1,21 @@
 import gradio as gr
-import tensorflow as tf
-from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
-import numpy as np
-from PIL import Image
+
 from matplotlib import gridspec
 import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
 
-# 모델 및 특성 추출기를 불러옵니다
-feature_extractor = SegformerFeatureExtractor.from_pretrained("mattmdjaga/segformer_b2_clothes")
-model = TFSegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
-
+feature_extractor = SegformerFeatureExtractor.from_pretrained(
+    "mattmdjaga/segformer_b2_clothes"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+    "mattmdjaga/segformer_b2_clothes"
+)
 
-# ADE20K 팔레트 및 라벨 목록을 정의합니다
 def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
     return [
         [34, 116, 28],
         [84, 57, 0],
@@ -25,37 +28,34 @@ def ade_palette():
         [66, 39, 0],
         [240, 100, 39],
         [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
+        [242, 100, 60],
+        [116, 116, 116],
+        [99, 58, 0],
+        [245, 100, 26],
+        [66, 56, 0],
         [51, 51, 51],
         [206, 114, 61],
     ]
 
-
 labels_list = []
 
-with open('labels.txt', 'r') as fp:
+with open(r'labels.txt', 'r') as fp:
     for line in fp:
         labels_list.append(line[:-1])
 
 colormap = np.asarray(ade_palette())
 
-
-# 라벨을 컬러 이미지로 변환하는 함수를 정의합니다
 def label_to_color_image(label):
     if label.ndim != 2:
         raise ValueError("Expect 2-D input label")
+
     if np.max(label) >= len(colormap):
         raise ValueError("label value too large.")
     return colormap[label]
 
-
-# 예측 이미지와 세그멘테이션을 시각화하는 함수를 정의합니다
 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])
@@ -64,6 +64,7 @@ def draw_plot(pred_img, seg):
     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")
@@ -73,41 +74,37 @@ def draw_plot(pred_img, seg):
     ax.tick_params(width=0.0, labelsize=25)
     return fig
 
-
-# 이미지 전처리 함수를 정의합니다
-def preprocess_image(input_img):
+def sepia(input_img):
     input_img = Image.fromarray(input_img)
 
-    # 이미지를 모델의 입력 형식에 맞게 전처리합니다
     inputs = feature_extractor(images=input_img, return_tensors="tf")
     outputs = model(**inputs)
     logits = outputs.logits
-    logits = tf.transpose(logits, [0, 2, 3, 1])
-
-    # 크기 조정
-    logits = tf.image.resize(logits, [input_img.size[1], input_img.size[0]])
 
+    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)
 
+    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=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
+                    allow_flagging='never')
 
-# Gradio 인터페이스를 설정합니다
-demo = gr.Interface(
-    fn=preprocess_image,
-    inputs=gr.Image(type='pil'),
-    outputs=['plot'],
-    examples=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
-    allow_flagging='never'
-)
 
-# 앱을 실행합니다
 demo.launch()