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README.md

@@ -1,8 +1,8 @@
 ---
-title: ThirdPRE
-emoji: 🌖
-colorFrom: pink
-colorTo: blue
+title: Practice3
+emoji: 🚀
+colorFrom: yellow
+colorTo: red
 sdk: gradio
 sdk_version: 4.2.0
 app_file: app.py

app.py

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+import gradio as gr
+
+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
+
+feature_extractor = SegformerFeatureExtractor.from_pretrained(
+    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
+)
+
+def ade_palette():
+
+    return [
+        [204, 87, 92],  # road (Reddish)
+        [112, 185, 212],  # sidewalk (Blue)
+        [196, 160, 122],  # building (Brown)
+        [106, 135, 242],  # wall (Light Blue)
+        [91, 192, 222],  # fence (Turquoise)
+        [255, 192, 203],  # pole (Pink)
+        [176, 224, 230],  # traffic light (Light Blue)
+        [222, 49, 99],  # traffic sign (Red)
+        [139, 69, 19],  # vegetation (Brown)
+        [255, 0, 0],  # terrain (Red)
+        [0, 0, 255],  # sky (Blue)
+        [255, 228, 181],  # person (Peach)
+        [128, 0, 0],  # rider (Maroon)
+        [0, 128, 0],  # car (Green)
+        [255, 99, 71],  # truck (Tomato)
+        [0, 255, 0],  # bus (Lime)
+        [128, 0, 128],  # train (Purple)
+        [255, 255, 0],  # motorcycle (Yellow)
+        [128, 0, 128]  # bicycle (Purple)
+
+    ]
+
+labels_list = []
+
+with open(r'labels.txt', 'r') as fp:
+    for line in fp:
+        labels_list.append(line[:-1])
+
+colormap = np.asarray(ade_palette())
+
+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]
+
+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')
+    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=["city1.jpg","city2.jpg","city3.jpg"],
+                    allow_flagging='never')
+
+
+demo.launch()

labels.txt

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+road
+sidewalk
+building
+wall
+fence
+pole
+traffic light
+traffic sign
+vegetation
+terrain
+sky
+person
+rider
+car
+truck
+bus
+train
+motorcycle
+bicycle

requirements.txt

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+torch
+transformers
+tensorflow
+numpy
+Image
+matplotlib