a

README.md

@@ -5,7 +5,7 @@ emoji: 📈
 colorFrom: blue
 colorTo: yellow
 sdk: gradio
-sdk_version: 4.12.0.dev0
+sdk_version: 4.12.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -1,121 +1,13 @@
-import gradio as gr
-
-from matplotlib import gridspec
-import matplotlib.pyplot as plt
-import numpy as np
+from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
 from PIL import Image
 import requests
-from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation
 
-extractor = AutoFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
-model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
+feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-1024-1024")
+model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-1024-1024")
 
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
 inputs = feature_extractor(images=image, return_tensors="pt")
 outputs = model(**inputs)
-logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
-
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [255, 0, 0],
-        [255, 94, 0],
-        [255, 187, 0],
-        [255, 228, 0],
-        [171, 242, 0],
-        [29, 219, 22],
-        [0, 216, 255],
-        [0, 84, 255],
-        [1, 0, 255],
-        [95, 0, 255],
-        [255, 0, 221],
-        [255, 0, 127],
-        [152, 0, 0],
-        [153, 112, 0],
-        [107, 153, 0],
-        [0, 51, 153],
-        [63, 0, 153],
-        [153, 0, 133]
-    ]
-
-
-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=[
-        "image1.jpg"],
-    allow_flagging="never",
-)
-
-
-demo.launch()
+logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)